Overlay measurement based on template matching with adaptive weighting

Abstract

A method of image template matching with adaptive weight maps is described. The image template comprises a weight map that is adaptively updated during template matching based on the position of the image template on the image. A method of template matching grouped patterns or artifacts in a composite template is described, where the pattern includes less weighted non-enhanced areas than the image template. A method of generating an image template based on a composite image is described. The composite image may be generated based on a process and image modeling. A method of selecting grouped patterns or artifacts and generating a composite template is described. A method of layer-wise image template matching is described.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. patent application No. 63 / 291,278, filed December 17, 2021, U.S. patent application No. 63 / 338,142, filed May 4, 2022, and U.S. patent application No. 63 / 429,533, filed December 1, 2022, which are incorporated by reference in their entireties into this specification.

[0002] This disclosure relates generally to image analysis using image reference techniques, and more specifically to template matching with adaptive weighting. [Background technology]

[0003]

[0003] The manufacture of semiconductor devices such as integrated circuits typically involves processing a substrate (e.g., a semiconductor wafer) using several fabrication processes to form various features and layers of the device. Such layers and features are typically manufactured and processed using, for example, deposition, lithography, etching, chemical mechanical polishing, and ion implantation. Multiple devices can be fabricated on multiple dies on a substrate and then separated into individual devices. This device manufacturing process typically involves a patterning process. The patterning process includes a patterning step such as optical and / or nanoimprint lithography using a patterning device in a lithography apparatus to transfer a pattern on the patterning device onto the substrate, and optionally includes one or more associated pattern processing steps such as developing the resist with a developer, baking the substrate with a bake tool, and etching using the pattern with an etcher.

[0004]

[0004] Lithography is a central step in manufacturing devices such as ICs, where patterns formed on a substrate define the functional elements of the device, such as microprocessors, memory chips, etc. Similar lithographic techniques are also used in forming flat panel displays, microelectromechanical systems (MEMS), and other devices.

[0005]

[0005] Lithographic projection apparatus can be used, for example, in the manufacture of integrated circuits (ICs). A patterning device (e.g. a mask) can contain or provide a pattern (a "design layout") that corresponds to an individual layer of the IC, and this pattern can be transferred onto a target portion (e.g. comprising one or more dies) on a substrate (e.g. a silicon wafer) that is covered with a layer of radiation-sensitive material ("resist"), such as by irradiating the target portion with the pattern on the patterning device. Generally, a single substrate will contain several adjacent target portions onto which the pattern is successively transferred by the lithographic projection apparatus, one target portion at a time.

[0006]

[0006] Before transferring the pattern from the patterning device to the substrate, the substrate may undergo various procedures such as priming, resist coating and a soft bake. After exposure, the substrate may undergo other procedures ("post-exposure procedures") such as a post-exposure bake (PEB), development, a hard bake and measurement / inspection of the transferred pattern. This series of procedures is used as a basis for creating an individual layer of a device, e.g. an IC. The substrate may then undergo various processes such as etching, ion implantation (doping), metallization, oxidation, chemical-mechanical polishing, etc., all intended to complete an individual layer of the device. If the device requires several layers, the entire procedure or a modification thereof is repeated for each layer. Eventually, a device will be present on each target portion on the substrate. These devices are separated from each other by techniques such as dicing or sawing, so that the individual devices can be mounted on a carrier, connected to pins, etc.

[0007]

[0007] Lithography steps are monitored not only during mass production for process control reasons, but also during process qualification. Lithography steps are generally monitored by measurements of products produced by the lithography steps. Images of devices resulting from various processes are often compared with each other or with "gold standard" images to monitor the processes, detect defects, detect process changes, etc. Better control of the lithography steps generally corresponds to the production of better and more profitable devices.

[0008]

[0008] With advances in semiconductor manufacturing processes, the dimensions of functional elements are continually decreasing. At the same time, the number of functional elements, such as transistors, per device is steadily increasing, following a trend commonly referred to as "Moore's Law". In the current state of the art, layers of devices are fabricated using lithographic projection apparatus that projects a design layout onto a substrate using illumination from a deep ultraviolet illumination source, which creates individual functional elements with dimensions well below 100 nm, i.e., less than half the wavelength of radiation from the illumination source (e.g., a 193 nm illumination source).

[0009]

[0009] This process, in which features with dimensions smaller than the classical resolution limit of a lithographic projection apparatus are printed, is commonly known as low k1 lithography, with the resolution formula CD=k1×λ / NA, where λ is the wavelength of the radiation employed (currently often 248 nm or 193 nm), NA is the numerical aperture of the projection optics in the lithographic projection apparatus, CD is the "critical dimension" (typically the smallest feature size to be printed), and k1 is an empirical resolution factor. Generally, the smaller k1 is, the more difficult it is to reproduce a pattern on a substrate that resembles the shape and dimensions planned by the designer to achieve a particular electrical function and performance. To overcome these difficulties, sophisticated fine-tuning steps are applied to the lithographic projection apparatus, the design layout, or the patterning device. These steps include, for example, but are not limited to, optimization of NA and optical coherence settings, customized illumination schemes, use of out-of-phase patterning devices, optical proximity correction (OPC, also called "optical and process correction") in the design layout, source mask optimization (SMO) or other methods commonly defined as "resolution enhancement techniques" (RET). As used herein, the term "projection optics" should be interpreted broadly to encompass various types of optical systems including, for example, refractive optics, reflective optics, aperture and catadioptric optics. Summary of the Invention

[0010]

[0010] A method of image template matching using an adaptive weight map is described. According to an embodiment of the present disclosure, a weight map can be applied to the image template to selectively de-emphasize or emphasize certain regions of the image template or the image of the measurement structure, thereby improving the matching of the image template with the image of the measurement structure. Matching may further include updating and / or adapting the weight map as a function of the position of the image template on the weight map. When the image template is matched to various positions on the image of the measurement structure, the adaptive weight map accounts for regions of the image template that are blocked or otherwise unsuitable for matching. Based on the selective and adaptive weighting of the image template, image template matching can be advantageously improved.

[0011]

[0011] Template matching can be applied to determine the size or location of features during fabrication, and knowledge of feature location, shape, size, and alignment is useful for process control, quality assessment, etc. Template matching for features on multiple layers can be used to determine or measure overlay (e.g., layer-to-layer misalignment) and can be used with multiple overlay metrology tools. Template matching can also be used to determine the distance between features and the contours of features, which may be in the same or different layers, and can be used to determine edge placement (EP), edge placement error (EPE), and / or critical dimension (CD) using various types of metrology.

[0012]

[0012] A method of image template matching based on a composite template is described. In the following, a "composite template" refers to a template composed of multiple (same or different) constituent image templates, such as patterns, selected using a grouping process based on certain criteria and combined into one template, with at least one non-emphasized area filling the field of the composite template between any two of the constituent patterns. The grouping process can be manual or automatic. A composite template can be composed of multiple templates, each containing one or more patterns, or a single template containing multiple patterns. According to an embodiment of the present disclosure, matching of a composite template to an image of a measurement structure can be improved by applying a weight map to the composite template to emphasize and de-emphasize certain regions of the pattern image template. Particularly for non-repeating patterns on an image, multiple patterns and relationships between patterns (such as in a composite template) can be selected to improve the robustness of the matching. For example, the selection can be based on image analysis, pattern analysis, and / or grouping of patterns based on certain metrics, such as metrics related to image quality or noise. In some embodiments, non-emphasized areas on the patterns may not be filtered out or emphasized during matching. Matching may further include updating and / or adapting the weight map of the patterns as a function of the position of the composite template on the weight map. Advantageously, composite template matching can be improved based on selectively choosing patterns for inclusion in the composite template.

[0013]

[0013] A method for generating a synthetic image template based on simulated or synthetic data is described. According to an embodiment of the present disclosure, information about the layers of the measurement structure can be used to generate the image template. One or more process models, such as a computational lithography model, a deposition model, an etching model, a CMP (Chemical Mechanical Polishing) model, etc., can be used to generate a synthetic image template or contour based on a GDS or other information about the layers of the measurement structure. A scanning electron microscope model can also be used to refine the synthetic template. Further methods for creating, refining, or updating a synthetic image template are described. The synthetic image template can include a weight map and / or pixel values ​​and polarity values. The synthetic image template is then matched with a test image for the measurement structure. Matching can further include updating and / or adapting the weight map of the image template as a function of the position of the pattern of the image template on the weight map. Based on selectively choosing the features and / or synthetic generation process to include in the synthetic image template, synthetic image template matching can be advantageously improved.

[0014]

[0014] A method for generating a composite template based on image data is described. According to an embodiment of the present disclosure, the composite template can be generated using information about the layers of the measurement structure. The composite template can be based on an acquired image (i.e., an image acquired from an imaging tool), a captured image (i.e., an image acquired from stored data), and / or a composite or modeled image. A lithography model, such as a tachyon model, an etching model, and / or a scanning electron microscope model, a process tool model, or a metrology tool image simulation model can be used to generate a composite image or contour for the composite template. A composite template can be generated using multiple acquired images or an average of images, such as based on the contrast and stability of the acquired images. The composite template can include a weight map and / or pixel values. The composite template is then matched with a test image for the measurement structure. Matching can further include matching based on the weight map and, optionally, adapting the weight map of the pattern as a function of the position of the composite template on the weight map. Matching can be advantageously improved for non-periodic patterns based on selectively choosing patterns to include in the composite template.

[0015]

[0015] A method for layer-wise image template matching is described. According to an embodiment of the present disclosure, a template can be generated based on information about layers of a multi-layer structure. The template can be matched to an image of the multi-layer structure, including using adaptive weight mapping. Layer-wise image template matching can be used to identify regions of interest in an image, perform image enhancement, and segment an image. A composite template can also be generated from multiple templates corresponding to one layer of a multi-layer structure.

[0016]

[0016] A method for selecting a template of a particular size for template matching is described. According to an embodiment of the present disclosure, templates of various sizes are generated for a feature in an image (e.g., a feature in a via layer). Template matching is performed using each template size, and an optimal template size is selected based on a performance metric associated with the template matching. The optimal template size can be used to determine the location of the feature in the image, which can be further used in various applications, including determining a measure of overlay with other features. The performance metric can be any attribute that indicates the degree of match between a feature in an image and the template. For example, the performance metric can include a similarity metric that indicates the similarity between a feature in an image and the template.

[0017]

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one or more embodiments and, together with the description, explain these embodiments. Embodiments of the invention will now be described, by way of example only, with reference to the accompanying schematic drawings in which corresponding reference symbols indicate corresponding parts, and in which: [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system in accordance with one embodiment. [Diagram 2]

[0019] 2 is a schematic diagram illustrating an example electron beam tool that may be part of the example electron beam inspection system of FIG. 1, according to one embodiment. [Diagram 3]

[0020] 1 is a schematic diagram of an exemplary charged particle beam device including a charged particle detector, according to one embodiment. [Figure 4]

[0021] 1 depicts a schematic overview of a lithographic apparatus according to an embodiment; [Diagram 5]

[0022] 1 shows a schematic overview of a lithographic cell according to an embodiment; [Figure 6]

[0023] 1 shows a schematic representation of Holistic Lithography, which depicts collaboration between three technologies to optimize semiconductor manufacturing, according to one embodiment. [Figure 7]

[0024] 1 illustrates an overlay determination method based on template matching, according to one embodiment. [Figure 8A]

[0025] 1 illustrates a schematic representation of template matching for a block layer according to one embodiment. [Figure 8B]

[0026] 1 illustrates a schematic representation of template matching for a block layer with offset according to one embodiment. [Figure 9]

[0027] 1 shows a schematic representation of two-layer template matching on a set of periodic images according to one embodiment. [Figure 10A]

[0028] 1 illustrates an exemplary image template according to one embodiment. [Figure 10B]

[0029] 4 illustrates a weight map of an exemplary image template according to one embodiment. [Figure 11]

[0030] 1 illustrates an exemplary method for matching an image template to an image based on an adaptive weight map, according to one embodiment. [Figure 12]

[0031] 1 illustrates an exemplary composite contour template according to one embodiment. [Figure 13A]

[0032] 1 illustrates an exemplary synthetic image template for template matching with polar matching, according to one embodiment. [Figure 13B] 4 illustrates an exemplary synthetic image template for template matching with polarity matching, according to one embodiment. [Figure 14]

[0033] 1 illustrates an exemplary method for generating an image template based on a composite image, according to one embodiment. [Figure 15A]

[0034] 1 illustrates an exemplary composite template generated based on an image, according to one embodiment. [Figure 15B] 4 illustrates an exemplary composite template generated based on an image, according to one embodiment. [Figure 15C] 4 illustrates an exemplary composite template generated based on an image, according to one embodiment. [Figure 15D] 4 illustrates an exemplary composite template generated based on an image, according to one embodiment. [Figure 15E] 4 illustrates an exemplary composite template generated based on an image, according to one embodiment. [Figure 16]

[0035] 1 illustrates an exemplary method for generating a composite template, according to one embodiment. [Figure 17]

[0036] 1 shows a schematic representation of determining a measure of offset based on multiple image templates, each template itself including a group of patterns, according to one embodiment. [Figure 18A]

[0037] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18B] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18C] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18D] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18E] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18F] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 18G] 1 illustrates a schematic representation of layer-wise template matching according to one embodiment. [Figure 19A]

[0038] 1 shows a schematic representation of using template matching to select a region of interest according to one embodiment. [Figure 19B] 1 shows a schematic representation of using template matching to select a region of interest, according to one embodiment. [Figure 19C] 1 shows a schematic representation of using template matching to select a region of interest, according to one embodiment. [Figure 19D] 1 shows a schematic representation of using template matching to select a region of interest, according to one embodiment. [Figure 19E] 1 shows a schematic representation of using template matching to select a region of interest, according to one embodiment. [Figure 19F] 1 shows a schematic representation of using template matching to select a region of interest, according to one embodiment. [Figure 20]

[0039] 1 shows a schematic representation of image segmentation according to one embodiment. [Figure 21A]

[0040] 1 shows a schematic representation of a template alignment based on a previous template alignment according to one embodiment. [Figure 21B] 1 illustrates a schematic representation of a template alignment based on a previous template alignment, according to one embodiment. [Figure 22]

[0041] 1 shows a schematic representation of an image-to-image comparison according to one embodiment. [Diagram 23]

[0042] 1 illustrates a schematic representation of unit cell based template matching according to one embodiment. [Figure 24]

[0043] FIG. 1 is a block diagram of an exemplary computer system according to one embodiment of the present disclosure. [Figure 25A]

[0044] 1 shows a block diagram for selecting a template size from a library of template sizes for template matching, consistent with various embodiments. [Figure 25B] FIG. 10 shows a block diagram for selecting a template size from a library of template sizes for template matching, consistent with various embodiments. [Figure 25C]

[0045] 1 shows a graph of performance metric values ​​for various template sizes in template matching, consistent with various embodiments. [Figure 26]

[0046] 4 is a flow diagram of a method for selecting a template size from a library of template sizes for template matching, consistent with various embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019]

[0047] In order to enable those skilled in the art to carry out the present disclosure, the embodiments of the present disclosure are described in detail with reference to the drawings, which are provided as illustrative examples of the present disclosure to enable those skilled in the art to practice the present disclosure. In particular, the following drawings and examples are not intended to limit the scope of the present disclosure to a single embodiment, and other embodiments are possible by replacing some or all of the elements described or illustrated. Furthermore, where certain elements of the present disclosure can be implemented partially or fully using known components, only the parts of such known components necessary for understanding the present disclosure will be described, and detailed descriptions of other parts of such known components will be omitted, in order not to obscure the present disclosure. Unless otherwise specified herein, an embodiment described as being implemented by software should not be limited thereto, and may include embodiments implemented by hardware or a combination of software and hardware, as would be apparent to one skilled in the art, and vice versa. In this specification, an embodiment showing a single component should not be considered as limiting, but rather, the present disclosure is intended to encompass other embodiments including multiple identical components, and vice versa, unless otherwise expressly specified herein. Moreover, applicants do not intend that any term in this specification or claims be given a general or special meaning unless expressly defined as such. Moreover, this disclosure encompasses present and future known equivalents to known components referenced herein by way of example.

[0020]

[0048] While specific reference may be made herein to the manufacture of ICs, it should be clearly understood that the description herein has many other possible applications. For example, the description herein may be used in the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid crystal display panels, thin film magnetic heads, and the like. In connection with such alternative applications, those skilled in the art will appreciate that any use of the terms "reticle," "wafer," or "die" herein should be considered interchangeable with the more general terms "mask," "substrate," and "target portion," respectively.

[0021]

[0049] The terms "radiation" and "beam" are used herein to encompass all types of electromagnetic radiation, including ultraviolet radiation (e.g., having wavelengths of 365, 248, 193, 157 or 126 nm) and EUV (e.g., extreme ultraviolet having a wavelength in the range of about 5 to 100 nm).

[0022]

[0050] A (e.g., semiconductor) patterning device may include or form one or more patterns. The patterns may be generated utilizing a CAD (Computer Aided Design) program based on the pattern or design layout, a process often referred to as EDA (Electronic Design Automation). Most CAD programs follow a given set of design rules to create a functional design layout / patterning device. These rules are set by process and design limitations. For example, design rules may define the spacing tolerance between devices (gates, capacitors, etc.) or interconnect lines to ensure that the devices or lines do not interact with each other in an undesired manner. Design rules may include and / or specify certain parameters, parameter limits and / or ranges, and / or other information. One or more of the design rule limits and / or parameters may be referred to as "critical dimensions" (CDs). A critical dimension of a device may be defined as the minimum width of a line or hole or the minimum spacing between two lines or two holes or other features. Thus, the CDs determine the overall size and density of the designed device. One of the goals in device fabrication is to faithfully reproduce the original design intent on the substrate (via a patterning device).

[0023]

[0051] As used herein, the terms "mask" or "patterning device" may be broadly interpreted as referring to any general-purpose semiconductor patterning device that can be used to impart a patterned cross-section to an incident radiation beam that corresponds to the pattern desired to be created on a target portion of a substrate; the term "light valve" may also be used in this context. Beyond the classic mask (transmissive or reflective; binary, phase-shifting, hybrid, etc.), examples of other such patterning devices include programmable mirror arrays and programmable LCD arrays.

[0024]

[0052] As used herein, the term "patterning process" refers to a process of creating an etched substrate by applying a specified pattern of light, typically as part of a lithography process. However, "patterning process" can also include (e.g., plasma) etching, as many of the features described herein can provide advantages in forming printed patterns using etching (e.g., plasma) processes.

[0025]

[0053] As used herein, the term "pattern" refers to an ideal pattern that is etched onto a substrate (e.g., a wafer) based on, for example, a design layout as described above. The pattern may include, for example, various shapes, configurations of features, contours, etc.

[0026]

[0054] As used herein, "printed pattern" refers to a physical pattern on a substrate that is etched based on a target pattern. The printed pattern can include, for example, grooves, channels, depressions, edges, or other two- and three-dimensional features resulting from a lithographic process.

[0027]

[0055] As used herein, the terms "predictive model," "process model," "electronic model," and / or "simulation model" (which may be used interchangeably) refer to a model that includes one or more models that simulate a patterning process. For example, the models may include an optical model (e.g., which may include modeling a lens system / projection system used to deliver light in a lithography process and modeling the final optical image of the light traveling onto a photoresist), a resist model (e.g., which models the physical effects of resist, such as chemical effects of light), an OPC model (e.g., which may be used to create a target pattern and may include sub-resolution resist features (SRAF), etc.), an etch (or etch bias) model (e.g., which simulates the physical effects of an etch process on a printed wafer pattern), a source mask optimization (SMO) model, and / or other models.

[0028]

[0056] As used herein, the term "calibrate" means to modify (e.g., improve or adjust) and / or validate models, algorithms, and / or other components of the present systems and / or methods.

[0029]

[0057] A patterning system may be a system that includes any or all of the components described above, as well as other components configured to perform any or all of the operations associated with these components. A patterning system may include, for example, a lithographic projection apparatus, a scanner, a system configured to apply and / or remove resist, an etching system, and / or other systems.

[0030]

[0058] Reference is now made to Figure 1, which illustrates an exemplary electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure. As shown in Figure 1, the charged particle beam inspection system 100 includes a main chamber 110, a load lock chamber 120, an electron beam tool 140, and an equipment front end module (EFEM) 130. The electron beam tool 140 is located within the main chamber 110. Although this description and drawings are directed to an electron beam, it will be understood that the embodiments are not used to limit the present disclosure to any particular charged particle.

[0031]

[0059] The EFEM 130 includes a first loading port 130a and a second loading port 130b. The EFEM 130 may include additional loading ports. The first loading port 130a and the second loading port 130b receive wafer front-opening unified pods (FOUPs) containing wafers (e.g., semiconductor wafers or wafers made of other materials) or samples to be inspected (wafers and samples are collectively referred to below as "wafers"). One or more robotic arms (not shown) within the EFEM 130 transport the wafers to the load lock chamber 120.

[0032]

[0060] The load lock chamber 120 is connected to a load lock vacuum pumping system (not shown) that removes gas molecules in the load lock chamber 120 to reach a first pressure that is less than atmospheric pressure. After the first pressure is reached, one or more robot arms (not shown) transfer the wafer from the load lock chamber 120 to the main chamber 110. The main chamber 110 is connected to a main chamber vacuum pumping system (not shown) that removes gas molecules in the main chamber 110 to reach a second pressure that is less than the first pressure. After the second pressure is reached, the wafer is subjected to inspection by an electron beam tool 140. In some embodiments, the electron beam tool 140 may include a single beam inspection tool.

[0033]

[0061] The controller 150 may be electronically connected to the electron beam tool 140 and may also be electronically connected to other components. The controller 150 may be a computer configured to perform various controls of the charged particle beam inspection system 100. The controller 150 may also include processing circuitry configured to perform various signal and image processing functions. Although the controller 150 is shown in FIG. 1 as being external to the structure including the main chamber 110, the load lock chamber 120, and the EFEM 130, it will be understood that the controller 150 may be part of the structure.

[0034]

[0062] It should be noted that although this disclosure provides an example of a main chamber 110 housing an electron beam inspection system, aspects of the disclosure are not limited to chambers housing electron beam inspection systems in their broadest sense, rather it will be appreciated that the principles described above may also be applied to other chambers, such as chambers in deep ultraviolet (DUV) lithography or extreme ultraviolet (EUV) lithography systems.

[0035]

[0063] Reference is now made to FIG. 2, which illustrates a schematic diagram illustrating an exemplary configuration of an electron beam tool 140 that may be part of the exemplary charged particle beam inspection system 100 of FIG. 1 consistent with an embodiment of the present disclosure. The electron beam tool 140 (also referred to herein as the apparatus 140) may include an electron emitter, which may include a cathode 203, an extraction electrode 205, a gun aperture 220, and an anode 222. The electron beam tool 140 may further include a Coulomb aperture array 224, a focusing lens 226, a beam limiting aperture array 235, an objective lens assembly 232, and an electron detector 244. The electron beam tool 140 may further include a sample holder 236 supported by a motorized stage 234 for holding a sample 250 to be inspected. It should be understood that other associated components may be added or omitted as desired.

[0036]

[0064] In some embodiments, the electron emitter may include a cathode 203 and an anode 222, and primary electrons may be emitted from the cathode and extracted or accelerated to form a primary electron beam 204 that forms the primary beam crossover 202. The primary electron beam 204 may be visualized as being emitted from the primary beam crossover 202.

[0037]

[0065] In some embodiments, the electron emitter, collection lens 226, objective lens assembly 232, beam-limiting aperture array 235, and electron detector 244 may be aligned with a primary optical axis 201 of apparatus 40. In some embodiments, the electron detector 244 may be positioned off-axis from the primary optical axis 201 along a secondary optical axis (not shown).

[0038]

[0066] In some embodiments, the objective lens assembly 232 may include a modified swinging objective deceleration immersion lens (SORIL) including pole pieces 232a, control electrodes 232b, a beam manipulator assembly including deflectors 240a, 240b, 240d, and 240e, and an excitation coil 232d. In a typical imaging process, a primary electron beam 204 emanating from the tip of the cathode 203 is accelerated by an acceleration voltage applied to the anode 222. A portion of the primary electron beam 204 passes through the apertures of the gun aperture 220 and the Coulomb aperture array 224, and is focused by the condenser lens 226 to pass fully or partially through the apertures of the beam-limiting aperture array 235. Electrons passing through the apertures of the beam-limiting aperture array 235 may be focused by a modified SORIL lens to form a probe spot on the surface of the sample 250 and deflected by one or more deflectors of the beam manipulator assembly to scan the surface of the sample 250. Secondary electrons emitted from the sample surface may be collected by the electron detector 244 to form an image of the scanned region of interest.

[0039]

[0067] In the objective lens assembly 232, the excitation coil 232d and the pole piece 232a can generate a magnetic field. A portion of the sample 250 being scanned by the primary electron beam 204 can be immersed in the magnetic field and can be charged, which creates an electric field. The electric field can reduce the energy of the primary electron beam 204 impinging near and on the surface of the sample 250. The control electrode 232b, which is electrically isolated from the pole piece 232a, can control the electric field on and above the sample 250 to, for example, reduce the aberration of the objective lens assembly 232, adjust the focus of the signal electron beam to obtain high detection efficiency, or avoid arcing to protect the sample. To facilitate beam scanning on the sample 250, one or more deflectors of the beam manipulator assembly can deflect the primary electron beam 204. For example, in a scanning process, deflectors 240a, 240b, 240d, and 240e can be controlled to deflect the primary electron beam 204 onto different locations on the top surface of the sample 250 at different times to provide data for image reconstruction of different portions of the sample 250. It is noted that in different embodiments, the order of 240a-e can be different.

[0040]

[0068] Upon receiving the primary electron beam 204, backscattered electrons (BSE) and secondary electrons (SE) may be emitted from a portion of the sample 250. The beam separator 240c may direct the secondary or scattered electron beam, including the backscattered and secondary electrons, to a sensor surface of the electron detector 244. The detected secondary electron beam may form a corresponding beam spot on the sensor surface of the electron detector 244. The electron detector 244 may generate a signal (e.g., voltage, current) representative of the intensity irradiance of the received secondary electron beam spot and provide the signal to a processing system such as the controller 150. The intensity irradiance of the secondary or backscattered electron beam and the resulting secondary electron beam spot may vary depending on the external or internal structure of the sample 250. Furthermore, as discussed above, the primary electron beam 204 may be deflected onto different locations on the top surface of the sample 250 to generate secondary or scattered electron beams (and resulting beam spots) of different intensity irradiance. Thus, by mapping the intensity irradiance of the secondary electron beam spot with the position on the sample 250, the processing system can reconstruct an image reflecting the internal or external structure of the sample 250, which may include a wafer sample.

[0041]

[0069] In some embodiments, the controller 150 may include an image processing system including an image acquirer (not shown) and storage (not shown). The image acquirer may include one or more processors. For example, the image acquirer may include a computer, a server, a mainframe host, a terminal, a personal computer, any type of mobile computing device, etc., or a combination thereof. The image acquirer may be communicatively coupled to the electronic detector 244 of the device 40 by a medium such as electrical conductors, fiber optic cables, portable storage media, IR, Bluetooth, Internet, wireless networks, wireless radio, etc., among others, or a combination thereof. In some embodiments, the image acquirer may receive signals from the electronic detector 244 and may construct an image. In this manner, the image acquirer may acquire an image of an area of ​​the sample 250. The image acquirer may also perform various post-processing functions such as generating contours, overlaying indicia on the acquired image, etc. The image acquirer may be configured to make adjustments to the acquired image, such as brightness and contrast. In some embodiments, the storage may be a storage medium such as a hard disk, a flash drive, cloud storage, random access memory (RAM), other types of computer readable memory, etc. A storage may be coupled to the image acquirer and may be used to store the raw scanned image data as original images and processed images.

[0042]

[0070] In some embodiments, the controller 150 may include measurement circuitry (e.g., analog-to-digital converters) to obtain a distribution of detected secondary and backscattered electrons. The electron distribution data collected during the detection time window may be used in combination with corresponding scan path data of the primary electron beam 204 incident on the sample (e.g., wafer) surface to reconstruct an image of the wafer structure under inspection. The reconstructed image may be used to reveal various features of the internal or external structure of the sample 250, and thereby reveal any defects that may be present in the wafer.

[0043]

[0071] In some embodiments, the controller 150 may control the motorized stage 234 to move the sample 250 during inspection. In some embodiments, the controller 150 may enable the motorized stage 234 to continuously move the sample 250 in one direction at a constant speed. In other embodiments, the controller 150 may enable the motorized stage 234 to vary the speed of movement of the sample 250 over time depending on the step of the scanning process.

[0044]

[0072] As is commonly known in the art, interaction of charged particles, such as electrons of a primary electron beam, with a sample (e.g., sample 315 in FIG. 3 described below) can generate signal electrons that contain compositional and topographical information about a probed region of the sample. Secondary electrons (SEs) can be identified as signal electrons with low emission energy, and backscattered electrons (BSEs) can be identified as signal electrons with high emission energy. Due to their low emission energy, the objective lens assembly can guide the SEs along an electron path and focus the SEs onto a detection surface of an in-lens electron detector located in the SEM column. BSEs traveling along the electron path can also be detected by the in-lens electron detector. However, in some cases, BSEs with large emission angles may be detected using additional electron detectors, such as a backscattered electron detector, or may remain undetected, resulting in loss of sample information required to inspect the sample or measure critical dimensions.

[0045]

[0073] Detection and inspection of some defects in semiconductor fabrication processes, such as buried particles during photolithography, metal deposition, dry or wet etching, among others, can benefit from inspection of surface features and compositional analysis of defective particles. In such scenarios, information obtained from secondary electron detectors and backscattered electron detectors may be desirable for users to, among other things, identify the defects, analyze the composition of the defects, and adjust process parameters based on the obtained information.

[0046]

[0074] The emission of SEs and BSEs follows Lambert's law and has a large energy spread. SEs and BSEs arise from different depths of the sample upon interaction of the primary electron beam with the sample and have different emission energies. For example, secondary electrons arise from the surface and may have emission energies of 50 eV or less depending on the material of the sample or the volume of interaction, among others. SEs are useful for providing information about surface features or surface geometry. BSEs, on the other hand, are generated primarily by elastic scattering phenomena of the incident electrons of the primary electron beam and have higher emission energies compared to SEs, typically ranging from 50 eV to approximately the landing energy of the incident electrons, providing compositional and contrast information of the material being examined. The number of BSEs generated may depend on factors including, but not limited to, the atomic number of the material in the sample, the accelerating voltage of the primary electron beam, among others.

[0047]

[0075] Based on differences in emission energy or angle, among other things, SEs and BSEs can be detected separately using separate electron detectors, split electron detectors, energy filters, etc. For example, an in-lens electron detector can be configured as a split detector including multiple segments arranged in a two-dimensional or three-dimensional configuration. In some cases, the segments of an in-lens electron detector can be arranged radially, circumferentially, or azimuthally about a primary optical axis (e.g., primary optical axis 300-1 in FIG. 3).

[0048]

[0076] Reference is now made to FIG. 3, which illustrates a schematic diagram of an exemplary charged particle beam device 300 (also referred to as device 300) consistent with an embodiment of the present disclosure. The device 300 may be part of the exemplary electron beam tool of FIG. 2 and / or part of the exemplary charged particle beam inspection system 100 of FIG. 1. The device 300 may include a charged particle source, such as an electron source, configured to emit primary electrons from a cathode 301, which are extracted using an extraction electrode 302 to form a primary electron beam 300B1 along a primary optical axis 300-1. The device 300 may further include an anode 303, a condenser lens 304, a beam-limiting aperture array 305, a signal electron detector 306 and 312, a compound objective lens 307, a scanning deflection unit including primary electron beam deflectors 308, 309, 310 and 311, and a control electrode 314. In the context of the present disclosure, one or both of the signal electron detectors 306 and 312 may be in-lens electron detectors located within the electron optical column of the SEM and may be arranged rotationally symmetric about the primary optical axis 300-1. In some embodiments, the signal electron detector 312 may be referred to as a first electron detector and the signal electron detector 306 may be referred to as a through-the-lens detector, an immersion lens detector, a top detector or a second electron detector. It should be understood that associated components may be added, omitted or rearranged as appropriate.

[0049]

[0077] The electron source (not shown) may include a thermionic electron source configured to emit electrons when provided with thermal energy to overcome the work function of the source, a field emission source configured to emit electrons when exposed to a large electrostatic field, and the like. In the case of a field emission source, the electron source may be electrically connected to a controller, such as the controller 150 of FIG. 1, configured to apply and adjust a voltage signal based on the desired landing energy, sample analysis, characteristics of the source, among others. The extraction electrode 302 may be configured to extract or accelerate electrons emitted from the field emission gun to form a primary electron beam 300B1 that forms a virtual or actual primary beam crossover (not shown) along the primary optical axis 300-1. The primary electron beam 300B1 may be visualized as being emitted from the primary beam crossover. In some embodiments, the controller may be configured to apply and adjust a voltage signal to the extraction electrode 302 to extract or accelerate electrons generated from the electron source. The amplitude of the voltage signal applied to the extraction electrode 302 may be different from the amplitude of the voltage signal applied to the cathode 301. In some embodiments, the difference between the amplitude of the voltage signal applied to the extraction electrode 302 and the amplitude of the voltage signal applied to the cathode 301 may be configured to accelerate electrons downstream along the primary optical axis 300-1 while maintaining the stability of the electron source. As used in the context of this disclosure, "downstream" refers to a direction along the path of the primary electron beam 300B1 starting from the electron source and toward the sample 315. With respect to the positioning of elements of a charged particle beam device (e.g., device 300 of FIG. 3), "downstream" can refer to the position of an element located below or after another element along the path of the primary electron beam starting from the electron source, and "immediately downstream" refers to the position of a second element below or after a first element along the path of the primary electron beam 300B1 such that there are no other active elements between the first and second elements. For example, as shown in FIG. 3, the signal electron detector 306 may be positioned immediately downstream of the beam-limiting aperture array 305 such that no other optical or electronic optical elements are located between the beam-limiting aperture array 305 and the electron detector 306.As used in the context of this disclosure, "upstream" can refer to the position of an element located above or in front of another element along the path of the primary electron beam originating from the electron source, and "immediately upstream" refers to the position of a second element above or in front of a first element along the path of the primary electron beam 300B1 such that there are no other active elements between the first and second elements. As used herein, "active element" can refer to any element or component whose presence can modify the electromagnetic field between the first and second elements by generating an electric, magnetic or electromagnetic field.

[0050]

[0078] The apparatus 300 may include a condenser lens 304 configured to receive a portion or a substantial portion of the primary electron beam 300B1 and focus the primary electron beam 300B1 onto a beam-limiting aperture array 305. The condenser lens 304 may be substantially similar to the condenser lens 226 of FIG. 2 and may perform substantially similar functions. Although illustrated in FIG. 3 as a magnetic lens, the condenser lens 304 may be an electrostatic, magnetic, electromagnetic, or hybrid electromagnetic lens, among others. The condenser lens 304 may be electrically coupled to a controller, such as the controller 150 of FIG. 2. The controller may apply an electrical excitation signal to the condenser lens 304 to adjust the focusing power of the condenser lens 304 based on factors including the mode of operation, the application, the desired analysis, the sample material being examined, among others.

[0051]

[0079] The apparatus 300 may further include a beam-limiting aperture array 305 configured to limit the beam current of the primary electron beam 300B1 passing through one of a plurality of beam-limiting apertures of the beam-limiting aperture array 305. Although only one beam-limiting aperture is illustrated in FIG. 3, the beam-limiting aperture array 305 may include any number of apertures having uniform or non-uniform aperture size, cross-section, or pitch. In some embodiments, the beam-limiting aperture array 305 may be positioned downstream of the condenser lens 304 or immediately downstream of the condenser lens 304 (as shown in FIG. 3) and approximately perpendicular to the primary optical axis 300-1. In some embodiments, the beam-limiting aperture array 305 may be configured as a conductive structure including a plurality of beam-limiting apertures. The beam-limiting aperture array 305 may be electrically connected to the controller 150 by a connector (not shown), and the controller 150 may be configured to instruct the beam-limiting aperture array 305 to provide a voltage. The supplied voltage may be, for example, a reference voltage such as ground potential. The controller may also be configured to maintain or adjust the supplied voltage. The controller 150 may be configured to adjust the position of the beam-limiting aperture array 305.

[0052]

[0080] The apparatus 300 may include one or more signal electron detectors 306 and 312. The signal electron detectors 306 and 312 may be configured to detect substantially all secondary electrons and in particular a portion of the backscattered electrons based on the emission energy, emission polar angle, and emission azimuth angle of the backscattered electrons. In some embodiments, the signal electron detectors 306 and 312 may be configured to detect secondary electrons, backscattered electrons, or Auger electrons. The signal electron detector 312 may be located downstream of the signal electron detector 306. In some embodiments, the signal electron detector 312 may be located downstream or immediately downstream of the primary electron beam deflector 311. Signal electrons with low emission energy (typically 50 eV or less) or small emission polar angle emitted from the sample 315 may include the secondary electron beam 300B4, and signal electrons with high emission energy (typically above 50 eV) and medium emission polar angle may include the backscattered electron beam 300B3. In some embodiments, 300B4 may include secondary electrons with small emission polar angles, low energy backscattered electrons, or high energy backscattered electrons. Although not shown, it will be understood that a portion of the backscattered electrons may be detected by signal electron detector 306 and a portion of the secondary electrons may be detected by signal electron detector 312. In overlay metrology and inspection applications, signal electron detector 306 may be useful for detecting secondary electrons generated from surface layers and backscattered electrons generated from deeper underlying layers, such as deep trenches or high aspect ratio holes.

[0053]

[0081] The apparatus 300 may further include a compound objective lens 307 configured to focus the primary electron beam 300B1 on the surface of the sample 315. The controller may apply an electrical excitation signal to the coil 307C of the compound objective lens 307 to adjust the focusing power of the compound objective lens 307 based on factors including the primary beam energy, the application needs, the desired analysis, the sample material being inspected, among others. The compound objective lens 307 may further be configured to focus signal electrons, such as secondary electrons with low emission energy or backscattered electrons with high emission energy, onto a detection surface of a signal electron detector (e.g., in-lens signal electron detector 306 or detector 312). The compound objective lens 307 may be substantially similar to or perform substantially similar functions as the objective lens assembly 232 of FIG. 2. In some embodiments, the compound objective lens 307 may include an electromagnetic lens including a magnetic lens 307M and an electrostatic lens 307ES formed by the control electrode 314, the pole piece 307P, and the sample 315.

[0054]

[0082] As used herein, a compound objective lens is an objective lens that generates magnetic and electrostatic fields that overlap together near the sample to focus the primary electron beam. In this disclosure, the focusing lens 304 can also be a magnetic lens, but references to a magnetic lens such as 307M refer to the objective magnetic lens, and references to an electrostatic lens such as 307ES refer to the objective electrostatic lens. As shown in FIG. 3, for example, a magnetic lens 307M and an electrostatic lens 307ES that function together to focus the primary electron beam 300B1 onto the sample 315 can form a compound objective lens 307. The magnetic lens 307M and the lens body of the coil 307C can generate a magnetic field, while the electrostatic field can be generated, for example, by creating a potential difference between the sample 315 and the pole piece 307P. In some embodiments, the control electrode 314 or other electrodes located between the pole piece 307P and the sample 315 can also be part of the electrostatic lens 307ES.

[0055]

[0083] In some embodiments, the magnetic lens 307M may include a cavity defined by the space between the imaginary planes 307A and 307B. It should be understood that the imaginary planes 307A and 307B shown in dashed lines in FIG. 3 are merely visual aids for illustrative purposes. The imaginary plane 307A located near the collecting lens 304 may define the upper boundary of the cavity, and the imaginary plane 307B located near the sample 315 may define the lower boundary of the cavity of the magnetic lens 307M. As used herein, the "cavity" of the magnetic lens refers to the space defined by the elements of the magnetic lens configured to allow the passage of the primary electron beam, which space is rotationally symmetric about the primary optical axis. The terms "within the cavity of the magnetic lens" or "inside the cavity of the magnetic lens" refer to the space bounded within the imaginary planes 307A and 307B and the inner surfaces of the magnetic lens 307M that are directly exposed to the primary electron beam. Planes 307A and 307B may be approximately perpendicular to primary optical axis 300-1. Although Figure 3 shows a conical cavity, the cross section of the cavity may be cylindrical, conical, twisted cylindrical, twisted conical, or any suitable cross section.

[0056]

[0084] The apparatus 300 may further include a scanning deflection unit including primary electron beam deflectors 308, 309, 310, and 311 configured to dynamically deflect the primary electron beam 300B1 onto the surface of the sample 315. In some embodiments, the scanning deflection unit including the primary electron beam deflectors 308, 309, 310, and 311 may be referred to as a beam manipulator or beam manipulator assembly. The dynamic deflection of the primary electron beam 300B1 may scan a desired area or a desired region of interest of the sample 315, for example in a raster scan pattern, to generate SEs and BSEs for sample inspection. The one or more primary electron beam deflectors 308, 309, 310, and 311 may be configured to deflect the primary electron beam 300B1 in the X-axis or Y-axis or a combination of the X-axis and Y-axis. As used herein, the X and Y axes form a Cartesian coordinate system, and the primary electron beam 300B1 propagates along the Z axis or primary optical axis 300-1.

[0057]

[0085] Electrons are negatively charged particles that travel through an electron optical column and can do so at high energy and high speed. One way to deflect electrons is to pass them through an electric or magnetic field, for example, generated by a pair of plates held at two different potentials or by passing a current through a deflection coil, among other techniques. Varying the electric or magnetic field across a deflector (e.g., primary electron beam deflectors 308, 309, 310, and 311 in FIG. 3) can change the deflection angle of electrons in primary electron beam 300B1 based on factors including, but not limited to, electron energy, magnitude of the applied electric field, and dimensions of the deflector, among others.

[0058]

[0086] In some embodiments, one or more of the primary electron beam deflectors 308, 309, 310, and 311 may be located within the cavity of the magnetic lens 307M. As shown in FIG. 3, all of the primary electron beam deflectors 308, 309, 310, and 311 may be located within the cavity of the magnetic lens 307M as a whole. In some embodiments, at least one of the primary electron beam deflectors may be located within the cavity of the magnetic lens 307M as a whole. In some embodiments, a significant portion of the magnetic field generated by passing a current through the coil 307C may be within the magnetic lens 307M, so that each deflector is located within or influenced by the magnetic field of the magnetic lens 307M. In such a scenario, the sample 315 may be considered to be outside the magnetic field lines and may not be influenced by the magnetic field of the magnetic lens 307M. Beam deflectors (e.g., primary electron beam deflector 308 in FIG. 3) may be arranged circumferentially along the inner surface of magnetic lens 307M. One or more primary electron beam deflectors may be arranged between signal electron detector 306 and signal electron detector 312. In some embodiments, all of the primary electron beam deflectors may be arranged between signal electron detector 306 and signal electron detector 312.

[0059]

[0087] As disclosed herein, the pole piece of a magnetic lens (e.g., magnetic lens 307M) is the piece of magnetic material near the pole of the magnetic lens, whereas the pole is the end of the magnetic material where the external magnetic field is strongest. As shown in FIG. 3, the apparatus 300 includes magnetic pole pieces 307P and 307O. As an example, the magnetic pole piece 307P can be the piece of magnetic material near the north pole of the magnetic lens 307M, and the magnetic pole piece 307O can be the piece of magnetic material near the south pole of the magnetic lens 307M. When the current in the magnetic lens coil 307C changes direction, the polarity of the magnetic poles can also change. In the context of the present disclosure, the positioning of the electronic detector (e.g., signal electronic detector 312 in FIG. 3), the beam deflectors (e.g., beam deflectors 308-311 in FIG. 3), and the electrodes (e.g., control electrode 314 in FIG. 3) can be described with respect to the position of the magnetic pole piece 307P located closest to the point where the primary optical axis 300-1 intersects the sample 315. Pole piece 307P of magnetic lens 307M may include a magnetic pole made of a soft magnetic material, such as magnetic iron, that concentrates a magnetic field generally within the cavity of magnetic lens 307M. Pole pieces 307P and 307O may be, for example, high resolution pole pieces, general purpose pole pieces, or high contrast pole pieces.

[0060]

[0088] As shown in FIG. 3, the pole piece 307P may include an opening 307R configured to allow the primary electron beam 300B1 to pass through and to allow the signal electrons to reach the signal detectors 306 and 312. The opening 307R in the pole piece 307P may be circular, approximately circular, or non-circular in cross section. In some embodiments, the geometric center of the opening 307R in the pole piece 307P may be aligned with the primary optical axis 300-1. In some embodiments, as shown in FIG. 3, the pole piece 307P may be the most downstream horizontal section of the magnetic lens 307M and may be approximately parallel to the plane of the sample 315. The pole pieces (e.g., 307P and 307O) are one of several distinguishing features of magnetic lenses over electrostatic lenses. An electrostatic lens does not have pole pieces because the pole pieces are the magnetic components adjacent to the poles of the magnetic lens and electrostatic lenses do not generate a magnetic field.

[0061]

[0089] One of several methods for separately detecting signal electrons such as SEs and BSEs based on their emission energy includes passing the signal electrons generated from the probe spot on the sample 315 through an energy filter device. In some embodiments, the control electrode 314 can be configured to function as an energy filter device and can be disposed between the sample 315 and the signal electron detector 312. In some embodiments, the control electrode 314 can be disposed between the sample 315 and the magnetic lens 307M along the primary optical axis 300-1. The control electrode 314 can be biased with respect to the sample 315 to form a potential barrier for signal electrons with a threshold emission energy. For example, the control electrode 314 can be negatively biased with respect to the sample 315 such that some of the negatively charged signal electrons with an energy below the threshold emission energy can be deflected back to the sample 315. As a result, only signal electrons with an emission energy higher than the energy barrier formed by the control electrode 314 propagate toward the signal electron detector 312. It will be appreciated that the control electrodes 314 may also perform other functions, such as affecting the angular distribution of detected signal electrons on the signal electron detectors 306 and 312 based on a voltage applied to the control electrodes. In some embodiments, the control electrodes 314 may be electrically connected to a controller (not shown) by a connector (not shown), and the controller may be configured to apply a voltage to the control electrodes 314. The controller may also be configured to apply, maintain, or adjust the applied voltage. In some embodiments, the control electrodes 314 may include one or more pairs of electrodes configured to provide further flexibility in signal control, for example, to adjust the trajectory of the signal electrons emitted from the sample 315.

[0062]

[0090] In some embodiments, the sample 315 may be positioned on a plane approximately perpendicular to the primary optical axis 300-1. The position of the plane of the sample 315 may be adjusted along the primary optical axis 300-1 such that the distance between the sample 315 and the signal electronic detector 312 may be adjusted. In some embodiments, the sample 315 may be electrically connected to a controller (not shown) by a connector, and the controller may be configured to supply a voltage to the sample 315. The controller may also be configured to maintain or adjust the supply voltage.

[0063]

[0091] In existing SEMs, the signals generated by the detection of secondary and backscattered electrons are used in combination for, among other things, imaging surfaces, detecting and analyzing defects, obtaining topographical information, morphological and compositional analysis. By detecting secondary and backscattered electrons, the top few layers and the layers below can be imaged simultaneously, thus potentially capturing potential defects such as buried particles, overlay errors, among other things. However, the overall image quality can be affected by the detection efficiency of secondary and backscattered electrons. Although a high efficiency secondary electron detection can provide a high quality image of the surface, the overall image quality can be insufficient due to poor detection efficiency of backscattered electrons. Therefore, it can be beneficial to improve the detection efficiency of backscattered electrons to obtain high quality imaging while maintaining a high throughput.

[0064]

[0092] 3, the apparatus 300 may include a signal electron detector 312 located immediately upstream of the pole piece 307P and within the cavity of the magnetic lens 307M. The signal electron detector 312 may be located between the primary electron beam deflector 311 and the pole piece 307P. In some embodiments, the signal electron detector 312 may be located within the cavity of the magnetic lens 307M such that there is no primary electron beam deflector between the signal electron detector 312 and the sample 315.

[0065]

[0093] In some embodiments, the pole piece 307P may be electrically grounded or maintained at ground potential to minimize the effect of retarding electrostatic fields associated with the sample 315 on the signal electronic detector 312 and thus minimize electrical damage, such as arcing, that may occur to the signal electronic detector 312. In a configuration such as that shown in FIG. 3, the distance between the signal electronic detector 312 and the sample 315 may be reduced to improve detection efficiency and image quality of the BSE while minimizing the occurrence of electrical failure or damage to the signal electronic detector 312.

[0066]

[0094] In some embodiments, the signal electron detectors 306 and 312 may be configured to detect signal electrons having a wide range of emission polar angles and emission energies. For example, due to the proximity of the signal electron detector 312 to the sample 315, the signal electron detector 312 may be configured to collect backscattered electrons having a wide range of emission polar angles, and the signal electron detector 306 may be configured to collect or detect secondary electrons having low emission energies.

[0067]

[0095] The signal electron detector 312 may include an aperture configured to allow passage of the primary electron beam 300B1 and the signal electron beam 300B4. In some embodiments, the aperture of the signal electron detector 312 may be aligned such that a central axis of the aperture may substantially coincide with the primary optical axis 300-1. The aperture of the signal electron detector 312 may be circular, rectangular, elliptical, or any other suitable shape. In some embodiments, the size of the aperture of the signal electron detector 312 may be selected accordingly. For example, in some embodiments, the size of the aperture of the signal electron detector 312 may be smaller than the aperture of the pole piece 307P closer to the sample 315. In some embodiments, where the signal electron detector 306 is a single channel detector, the aperture of the signal electron detector 312 and the aperture of the signal electron detector 306 may be aligned with each other and with the primary optical axis 300-1. In some embodiments, the signal electron detector 306 may include multiple electron detectors or one or more electron detectors with multiple detection channels. In embodiments where the signal electron detector 306 includes multiple electron detectors, one or more of the detectors may be located off-axis relative to the primary optical axis 300-1. In the context of this disclosure, "off-axis" may refer to the position of an element, such as a detector, such that its primary axis forms a non-zero angle with the primary optical axis of the primary electron beam. In some embodiments, the signal electron detector 306 may further include an energy filter configured to allow a portion of the input signal electrons having a threshold energy to pass through and be detected by the electron detector.

[0068]

[0096] 3 may further allow for easier assembly and alignment of the signal electron detector 312 with respect to other electron-optical components of the apparatus 300. The electrically grounded pole piece 307P may substantially shield the signal electron detector 312 from the effects of the retarding electrostatic field of the electrostatic lens 307ES formed by the pole piece 307P, the control electrode 314, and the sample 315.

[0069]

[0097] One of several ways to improve image quality and signal-to-noise ratio may include detecting more backscattered electrons emitted from the sample. The emission angle distribution of backscattered electrons may be described by a cosine dependence of the emission polar angle (cos(θ), where θ is the emission polar angle between the backscattered electron beam and the primary optical axis). While a signal electron detector can efficiently detect backscattered electrons with moderate emission polar angles, backscattered electrons with large emission polar angles may remain undetected or may be insufficiently detected to contribute to the overall imaging quality. Therefore, it may be desirable to add another signal electron detector to capture the backscattered electrons with large angles.

[0070]

[0098] As a further brief introduction, Figure 4 diagrammatically depicts a lithographic apparatus LA. The lithographic apparatus LA comprises an illumination system (also referred to as illuminator) IL configured to condition a radiation beam B (e.g. UV, DUV or EUV radiation), a mask support (e.g. mask table) T constructed to support a patterning device (e.g. mask) MA and connected to a first positioner PM configured to accurately position the patterning device MA according to certain parameters, a substrate support (e.g. wafer table) WT configured to hold a substrate (e.g. resist coated wafer) W and coupled to a second positioner PW configured to accurately position the substrate support according to certain parameters, and a projection system (e.g. refractive projection lens system) PS configured to project a pattern imparted to the radiation beam B by the patterning device MA onto a target portion C of the substrate W (e.g. comprising one or more dies).

[0071]

[0099] In operation, the illumination system IL receives a radiation beam from the radiation source SO, for example via the beam delivery system BD. The illumination system IL may include various types of optical components, such as refractive, reflective, magnetic, electromagnetic, electrostatic and / or other types of optical components or any combination thereof, for directing, shaping and / or controlling the radiation. The illuminator IL is used to condition the radiation beam B to have a desired spatial and angular intensity distribution in its cross-section in the plane of the patterning device MA.

[0072]

[0100] The term "projection system" PS as used herein should be interpreted broadly to encompass various types of projection systems including refractive, reflective, catadioptric, anamorphic, magnetic, electromagnetic and / or electrostatic optical systems or any combination thereof as appropriate to the exposure radiation being used and / or other factors such as the use of an immersion liquid or the use of a vacuum. Any use of the term "projection lens" herein can be considered as synonymous with the more general term "projection system" PS.

[0073]

[0101] The lithographic apparatus LA may be of a type in which at least a part of the substrate may be covered with a liquid having a relatively high refractive index, such as water, so as to fill a space between the projection system PS and the substrate W (also referred to as immersion lithography). More information on immersion techniques is given in U.S. Patent No. 6,952,253, which is incorporated herein by reference.

[0074]

[0102] The lithographic apparatus LA may be of a type that has two or more substrate supports WT (also called "dual stage") In such a "multi-stage" machine, the substrate supports WT can be used in parallel and / or a substrate W located on one of the substrate supports WT can be subjected to preparatory steps for a subsequent exposure of the substrate W, whilst another substrate W on the other substrate support WT can be used for exposing a pattern onto the other substrate W.

[0075]

[0103] In addition to the substrate support WT, the lithographic apparatus LA may include a measurement stage. The measurement stage is configured to hold a sensor and / or a cleaning device. The sensor may be configured to measure a property of the projection system PS or a property of the radiation beam B. The measurement stage may hold multiple sensors. The cleaning device may be configured to clean part of the lithographic apparatus, for example part of the projection system PS or part of the system for providing immersion liquid. When the substrate support WT is remote from the projection system PS, the measurement stage may be moved below the projection system PS.

[0076]

[0104] In operation, the radiation beam B is incident on a patterning device, e.g. mask MA, which is held on the mask support MT, and is patterned by a pattern (design layout) present on the patterning device MA. Having traversed the mask MA, the radiation beam B passes through the projection system PS, which focuses the beam onto a target portion C of the substrate W. Using the second positioner PW and the position measurement system IF, the substrate support WT can be accurately moved, e.g. to position various target portions C in the path of the radiation beam B at focused and aligned positions. Similarly, the first positioner PM and possibly further position sensors (not explicitly shown in FIG. 1 ) can be used to accurately position the patterning device MA with respect to the path of the radiation beam B. The patterning device MA and substrate W can be aligned using mask alignment marks M1, M2 and substrate alignment marks P1, P2. Although the illustrated substrate alignment marks P1, P2 occupy dedicated target portions, they may also be located in spaces between the target portions. When located between target portions C, the substrate alignment marks P1, P2 are known as scribe-lane alignment marks.

[0077]

[0105] FIG. 5 shows a schematic overview of a lithography cell LC. As shown in FIG. 5, the lithography apparatus LA may form part of a lithography cell LC, which may also be called a lithocell or (litho)cluster, which often also includes apparatus for performing pre-exposure and post-exposure processes on a substrate W. Conventionally, these apparatuses include a spin coater SC configured to deposit a resist layer, a developer DE for developing the exposed resist, and a cooling plate CH and a baking plate BK for adjusting the temperature of the substrate W, e.g., to adjust the solvent in the resist layer. A substrate handler or robot RO picks up substrates W from input / output ports I / O1, I / O2, moves them between different processing equipment, and delivers the substrates W to a loading bay LB of the lithography apparatus LA. The devices in the lithocell, often collectively referred to as a track, are typically under the control of a track control unit TCU, which itself may be controlled by a supervisory control system SCS, which may also control the lithography apparatus LA, e.g., by a lithography control unit LACU.

[0078]

[0106] In order for a substrate W (FIG. 4) to be exposed by lithographic apparatus LA to be correctly and consistently exposed, it is desirable to inspect the substrate to measure characteristics of the patterned structures, such as overlay error between subsequent layers, line width, critical dimension (CD), etc. For that purpose, an inspection tool (not shown) may be included in the lithocell LC. If an error is detected, particularly if inspection is performed before other substrates W of the same batch or lot are subsequently exposed or processed, adjustments may be made, for example, to the exposure of subsequent substrates or other processing steps performed on substrate W.

[0079]

[0107] In order to characterize the properties of the substrate W (FIG. 4), in particular how the properties of various substrates W differ or how the properties associated with different layers of the same substrate W vary from layer to layer, an inspection apparatus, which may also be called a metrology apparatus, is used. The inspection apparatus may alternatively be constructed to identify defects on the substrate W and may for example be part of the lithocell LC, or integrated in the lithography apparatus LA, or even be a stand-alone device. The inspection apparatus may measure properties on a latent image (image in the resist layer after exposure), or a semi-latent image (image in the resist layer after a post-exposure bake step PEB), or a developed resist image (where exposed or unexposed parts of the resist have been removed) or even an etched image (after a pattern transfer step such as etching).

[0080]

[0108] FIG. 6 shows a schematic representation of holistic lithography, which represents the cooperation between three techniques for optimizing semiconductor manufacturing. Typically, the patterning process in the lithographic apparatus LA is one of the most critical steps in the process, which requires highly accurate dimensioning and positioning of structures on the substrate W (FIG. 1). To ensure this high precision, three systems can be combined (in this example) in a so-called "holistic" control environment, which is shown diagrammatically in FIG. 6. One of these systems is the lithographic apparatus LA, which is (virtually) connected to a metrology apparatus (e.g. metrology tool) MT (second system) and a computer system CL (third system). The "holistic" environment can be configured to optimize the cooperation between these three systems to improve the overall process window and provide a tight control loop in order to ensure that the patterning performed by the lithographic apparatus LA stays within the process window. A process window defines the range of process parameters (e.g., dose, focus, overlay) within which a particular manufacturing process produces a defined result (e.g., a functional semiconductor device) and typically the range within which the process parameters in a lithography or patterning process are allowed to vary.

[0081]

[0109] The computer system CL can use (part of) the design layout to be patterned to predict which resolution enhancement technique to use and perform computational lithography simulations and calculations to determine which mask layout and lithography apparatus settings will achieve the overall maximum process window of the patterning process (shown in FIG. 3 by the double-headed arrow in the first scale SC1). Typically, the resolution enhancement technique is configured to match the patterning capabilities of the lithography apparatus LA. The computer system CL can also be used to detect where in the process window the lithography apparatus LA is currently operating (e.g., using input from the metrology tool MT) (shown in FIG. 3 by the arrow pointing to "0" in the second scale SC2), for example, to predict whether defects may exist due to suboptimal processing.

[0082]

[0110] The metrology tool MT can provide input to the computer system CL to enable accurate simulation and prediction, and can provide feedback to the lithographic apparatus LA to identify possible drifts in the calibration status of the lithographic apparatus LA (shown in Figure 6 by multiple arrows in the third scale SC3).

[0083]

[0111] In lithography processes, it is desirable to frequently measure the created structures, e.g., for process control and verification. Various types of metrology tools MT are known for making such measurements, including scanning electron microscopes or various forms of optical metrology tools, image-based or scatterometer-based metrology tools. Image analysis on images obtained from optical metrology tools and scanning electron microscopes can be used to measure various dimensions (e.g., CD, overlay, edge placement error (EPE), etc.) and detect defects in the structure. In some cases, features of one layer of the structure may obscure features of another layer or the same layer of the structure in the image. This can happen when one layer is physically above another layer or when one layer is electronically rich and therefore brighter than another layer, e.g., in a scanning electron microscope (SEM) image. If a feature is partially obscured in the image, its location can be determined based on template matching.

[0084]

[0112] Template matching is an image or pattern recognition method or algorithm in which an image, which includes a set of pixels with pixel values, is compared to an image template. The image template may include a set of pixels with pixel values ​​or may include a function of pixel values ​​over a region (such as a smoothing function). In template matching, the image template is compared to various locations on the image to determine the region of the image that best matches the image template. The image template may be stepped across the image across a first and second dimension (i.e. across both the x-axis and y-axis of the image) and a similarity index may be determined at each location. The similarity index compares the pixel values ​​of the image to the pixel values ​​of the image template for each location of the image template and measures how well the values ​​match. A normalization factor, which is an example of a similarity index, is given by the following Equation 1:

number

[0085]

[0113] SEM images often provide the highest resolution and most sensitive images for multi-layer structures, so top-down SEM images can be used to determine relative offsets between features in the same or different layers, but template matching can also be used against optical or other electromagnetic images.

[0086]

[0114] The overall measurement quality of a lithography parameter using a particular target is at least partially determined by the measurement recipe used to measure this lithography parameter. The term "substrate measurement recipe" may include one or more parameters of the measurement itself, one or more parameters of the pattern or patterns being measured, or both. For example, if the measurement used in the substrate measurement recipe is a diffraction-based optical measurement, one or more of the parameters of the measurement may include the wavelength of the radiation, the polarization of the radiation, the angle of incidence of the radiation to the substrate, the orientation of the radiation to the pattern on the substrate, etc. One of the criteria for selecting a measurement recipe may be, for example, the sensitivity of one of the measurement parameters to process variations. Further examples are described in U.S. Patent Application Publication No. 2016 / 0161863 and U.S. Patent Application Publication No. 2016 / 0370717A1, which are incorporated by reference in their entirety.

[0087]

[0115] FIG. 7 illustrates an overlay determination method based on template matching according to an embodiment. A reference image 702 is obtained for a reference measurement structure 700. For example, the reference structure can be an "ideal" or "gold" version of the first measurement structure (e.g., without layer misalignment or other distortions). The reference image 702 can be generated based on a model of the fabrication process, based on a design structure of the GDS of the features, or can be an image of a well or "best" aligned device as described in more detail below. The reference measurement structure 700 can be any feature of the structure of an IC that can be imaged (the measurement structure does not have to be an alignment structure or an optical target structure), and the example shown here should not be considered limiting. The reference measurement structure 700 is composed of three layers: a top layer 704a with a feature 706a, a middle layer 704b with a feature 706b, and a bottom layer 704c without features.

[0088]

[0116] A test image 712 is acquired of a test measurement structure 710, which is an as-fabricated version of the reference measurement structure 700. The test image 712 shows that the test measurement structure 710 is not aligned in the same way that the reference measurement structure 700 is aligned. The test measurement structure 710 is made up of three layers: a top layer 714a with feature 716a, a middle layer 714b with feature 716b, and a bottom layer 714c, which is shown without any features.

[0089]

[0117] Each feature (i.e., features 706a, 706b, 716a, and 716b) can be located individually by template matching. An image template for a feature in the top layer can be matched to both feature 706a and feature 706b. Once the image templates are matched, an offset 720 between the reference position of feature 706a and the test position of feature 716a is determined. The offset 720 corresponds to a vector by which feature 716a is "offset" from the reference or planned position. An image template for a feature in the middle layer can also be matched to both feature 716b and feature 716b. After the image templates are matched, an offset 730 between the reference position of feature 706b and the test position of feature 716b is determined. In some embodiments, features 706a and 706b of the reference measurement structure 700 can have known positions, and the offset can be determined based on template matching of the known positions and the test positions.

[0090]

[0118] When features of both layers of the test image are located (e.g., by template matching), a measure of overlay can be determined. An "overlay" measure is determined for features of two layers of the same measurement structure and measures the layer-to-layer shift between layers that are designed to be aligned or have a fixed or known relationship. Because offset 720 is the offset of feature 716a from a fiducial and offset 730 is the offset of feature 716b, a measure of overlay 740 can be determined based on the sum of the offset vectors. An example calculation of the offset vector is shown in Equation 2 below.

number

[0091]

[0119] Due to design tolerances and construction requirements of the structure, some layers of the structure may physically or electronically obscure other layers when viewed in a two-dimensional plane as captured in an SEM image or optical image. For example, during construction of a multi-layer via, metal connections may obscure the image of a contact hole. Template matching of a block feature is more difficult if the feature is blocked or obscured by another feature of the IC. The block feature has a reduced surface area (and reduced contour length) when viewed in an image, which reduces the match between the template and the block feature, thus complicating template matching. Although the method of the present disclosure may be described with respect to SEM images, it should be understood that it may be applied to or based on any suitable image, such as SEM images, x-ray images, ultrasound images, optical images from image-based overlay metrology, optical microscope images, etc. Additionally, template matching may be applied at multiple metrology devices, steps, or decisions. For example, template matching may be applied at EPE, overlay (OVL), and CD metrology.

[0092]

[0120] FIG. 8A shows a schematic representation of template matching of a block layer with minimal offset, according to one embodiment. FIG. 8A includes an exemplary image 800a, which corresponds to an SEM image acquired, for example, near the center of a wafer. The exemplary image 800a is composed of measurement structures 802a-i. Each of the measurement structures 802a-i corresponds to a feature in a block layer 810 and a blocking layer 820. The block layer 810 may be above or below the blocking layer 820 with the measurement structure. The block layer 810 includes an exemplary feature having a shape 812. The blocking layer includes an exemplary feature having a shape 822. An exemplary template 814 is also shown, which corresponds to the shape 812 of the exemplary feature of the block layer 810. The exemplary template 814 can be used to locate the shape 812 of the block layer 810 by template matching. However, because the example template 814 corresponds to a feature that may be blocked (such as by a feature having shape 822 of blocking layer 820), the example template 814 may not perfectly match the example image 800a. That is, the example template 814 may not perfectly correspond to the shape 812 of the feature in image 800a.

[0093]

[0121] The measurement structures 802a-802i are periodic and their overlay values ​​are approximately equal within a small area, such as within an SEM image size. The size of the small area where the overlay values ​​are approximately equal may be affected by fabrication parameters such as optical lens uniformity, feature size, dose uniformity, focal length uniformity, etc. However, the overlay values ​​may be quite different at different wafer locations or over a relatively large area, such as between the wafer center location and the wafer edge location. Due to semiconductor process variations, the overlay values ​​may vary between different wafers and different wafer lots.

[0094]

[0122] To illustrate, FIG. 8B shows a schematic representation of template matching of a block layer with an offset according to one embodiment. FIG. 8B includes an exemplary image 800b corresponding to an SEM image acquired, for example, near the edge of a wafer. The exemplary image 800b is composed of measurement structures 840a-840i. In FIG. 8B (similar to FIG. 8A), each of the measurement structures 840a-840i corresponds to a feature in a block layer 810 and a blocking layer 820. The block layer includes an exemplary feature having a shape 812 (as in FIG. 8A) and the blocking layer includes an exemplary feature having a shape 822 (as in FIG. 8A). An exemplary template 814 is shown corresponding to the shape 812 of the feature of the block layer 810. The portion of the shape 812 visible in the image 800b of FIG. 8B is different from the portion of the shape 812 visible in the image 800a of FIG. 8A. This may occur due to differences in alignment, focus, material properties, etc. between portions of the wafer (in this example, between the center and a location closer to the edge of the wafer) or between the wafer itself. Example template 814 may not completely correspond to shape 812 of feature in image 800b due to portions of shape 812 that are blocked by shape 822. Furthermore, changing the shape of example template 814 to correspond only to the visible portion of shape 812 of blocking layer 810 (i.e., the portion of the rounded bar of shape 812 of blocking layer 810 minus the elliptical overlap of shape 822 of blocking layer 820) would also prevent template matching, since the visible portion of shape 812 of feature in blocking layer 810 will change based on differences in location, size, orientation, etc. of features in blocking layer 810 and blocking layer 820 and differences in relative positions between blocking layer 810 and blocking layer 820. 8A and 8B, where the visible portion of blocking layer shape 812 is not consistent between test images 800a and 800b. If the process variation is high enough, the shape of the visible portion of the feature can change even within the test images.

[0095]

[0123] According to an embodiment of the present disclosure, a weight map can be used to improve the accuracy of template matching in the block layer. The weight map generates separate weighting values ​​that can be adjusted to account for areas of the image template that correspond to block areas or other areas that cannot be matched well. In some embodiments, the weight map can be adjusted, updated, or adapted based on the location of the image template on the image or other characteristics of the image as well. For example, a weight map for an example template 814 in the block layer 810 can be weighted higher in areas where the example template 814 does not overlap features in the blocking layer 820 and weighted lower in areas where the example template 814 overlaps features in the blocking layer 820. To generate adaptive weighting and enable matching of the image template to one or more best locations (even if the image template is blocked or obscured), the weight map can be updated for each location of the image template (e.g., as the image template slides across the image or is otherwise compared to multiple locations on the image).

[0096]

[0124] 9 shows a schematic representation of two-layer template matching for a set of periodic images, according to one embodiment. The exemplary image 900 corresponds to nine semi-identical (e.g., substantially identical by design, but possibly not identical by fabrication) cells in a 3×3 grid. Each of the semi-identical cells includes buried or blocked features 902a-902i corresponding to a blocking image template 912 and non-buried overlying or blocking features 904a-904i corresponding to a blocking image template 914. The first step in determining the overlay includes matching the blocking image template 914 to a location on the exemplary image 900. Matching the blocking image template 914 to the blocking features 904a-904i can be accomplished by suitable template matching or other image recognition algorithms according to embodiments of the present disclosure.

[0097]

[0125] In a second step, a weight map is used to achieve a match between the blocking image template 912 and the blocking features 902a-902i. In some embodiments, the weight map is applied to the image and may be determined for the image or other features of the image. For example, an image weight map may be determined and the weight map of the image template may be an image weight map that corresponds to the location of the image template. In such a case, the image template essentially cuts out and selects a portion of the image weight map to be the image template. For example, a weight map may be generated for all or a portion of the image 900 based on the identified or matched locations of the blocking features 904a-904i.

[0098]

[0126] An exemplary weight map 920 is shown corresponding to the blocking image template 914. In some embodiments, the weight map may be a weight map corresponding to the blocking image template 912 and may be adaptively updated. For example, the weight map corresponding to the blocking image template 912 may be updated at each sliding position that is compared to the exemplary image 900 during template matching. The weight map for an image template may be updated based on pixel values ​​(e.g., luminance) of the exemplary image 900 at the position being tested for matching, distance from the blocking image template 912 that was previously matched to the exemplary image 900, etc.

[0099]

[0127] In some embodiments, a weight map may be applied to the image and an additional weight map may be applied to the image template. In such a case, during template matching, a total adaptive weight map at a location may be determined based on both the weight map applied to the image and the additional weight map applied to the image template. For example, a total adaptive weight map may be determined at each location tested for matching by adding or multiplying the weight map of the image and the weight map of the template. In this way, template matching may take into account both the weighting of the image (certain parts are de-emphasized relative to other parts) and the weighting of the image template (e.g., certain parts are more reliable).

[0100]

[0128] Then, at one or more occurrences, the block image template 912 is matched to the example image 900 based on the weight map, where the three elements of the match are (1) the image, (2) the image template, and (3) the weight map. In some embodiments, for each position compared during template matching, a similarity index is determined that depends on the weight map. The similarity index can be determined in multiple ways (including being user-defined during operation). One example of a similarity index is described in Equation 3 below.

number

[0101]

[0129] In some embodiments, one or more steps may be omitted, performed in a different order, or combined; the steps set forth above are sequentially labeled for illustrative purposes only, and the labeled order of the steps should not be considered limiting. For example, blocking features may be found by segmentation methods rather than template matching methods.

[0102]

[0130] FIG. 10A shows an exemplary image template according to one embodiment. FIG. 10A illustrates an exemplary image template. This exemplary image template includes pixels in an x-direction 1002 and a y-direction 1004. Each pixel has a pixel value defined at a scale 1006. An image template need not include pixels, but can instead be expressed as a function relating pixel value to position or distance (i.e., f(x,y)=pixel value). The function may be smooth, but may also be discrete, piecewise, or even discontinuous, or may not be well defined. An image template may correspond to a measured image of a feature, a composite image made up of multiple measured images of a feature, a synthetic image of a feature, etc. An image template may include portions of a feature that are expected to be blocked by other features in the measured structure. An image template may include a contour template or a hollow or otherwise discontinuous template.

[0103]

[0131] FIG. 10B illustrates a weight map for an exemplary image template according to one embodiment. FIG. 10B illustrates an exemplary weight map corresponding to the exemplary image template of FIG. 10A. The exemplary weight map includes pixels in an x-direction 1042 and a y-direction 1044. Each pixel has a weight value defined by a scale 1046. As shown, the exemplary weight map has a different pixel size than the exemplary image template of FIG. 10A. The exemplary weight map and the exemplary image template can instead have the same pixel size (or resolution). The exemplary weight map and the exemplary image template have the same exterior dimensions. In some cases, the exemplary weight map and the exemplary image template can have different dimensions. The weight map need not include pixels, but can instead be described as a function (e.g., a sigmoid function as a function of distance from the edge of the image template) and can have similar mathematical properties as the image template. The weight map can be adjusted based on the relative positions of the image template and the image, so the exemplary weight map can be a starting or null weight map that is then adjusted as the image template is matched to various portions of the image. In some cases, the weight map may be adjusted based on an image template, such as adjusting the size, scale, angle, or rotation.

[0104]

[0132] FIG. 11 illustrates an exemplary method 1100 for matching an image template to an image based on an adaptive weight map, according to one embodiment. Each of these operations is described in detail below. The operations of method 1100 illustrated below are intended to be exemplary. In some embodiments, method 1100 may be accomplished with one or more additional operations not described and / or without one or more of the operations described. Additionally, the order in which the operations of method 1100 are illustrated in FIG. 11 and described below is not intended to be limiting. In some embodiments, one or more portions of method 1100 may be implemented (e.g., by simulation, modeling, etc.) on one or more processing devices (e.g., one or more processors). The one or more processing devices may include one or more devices that perform some or all of the operations of method 1100 in response to instructions electronically stored on an electronic storage medium. The one or more processing devices may include one or more devices configured with hardware, firmware, and / or software, e.g., specifically designed for performing one or more of the operations of method 1100.

[0105]

[0133] In operation 1101, an image of the measurement structure is obtained. The image may be a test image and may be acquired by optical or other electromagnetic imaging or scanning electron microscope. The image may also be obtained from other software or data storage. In operation 1102, a blocking image template is optionally acquired (such as from an imaging system such as an SEM or optical image and / or a template library or other data storage repository) or synthetically generated. The blocking image template may correspond to a blocking layer of the measurement structure. In operation 1103, a weight map for the blocking image template is optionally accessed. The weight map may include weight values ​​based on the blocking image template (as shown, pixel values ​​are based on distance from an edge of the image template) and / or the weight values ​​may be determined or updated based on a position of the blocking image template on or relative to the image. In operation 1104, the blocking image template is matched to a first position on the image of the measurement structure. The blocking image template may be matched based on template matching and optionally a weight map for the blocking image template.

[0106]

[0134] In operation 1105, a buried or blocked image template is obtained, obtained, accessed or synthetically generated as described above. This blocked image template is associated with a weight map. In operation 1106, the blocked image template is placed at a location on the image of the measurement structure and compared to the image of the measurement structure using the weight map as an attenuation factor. A similarity index is calculated for this matching location. The similarity index may include normalized cross-correlation, cross-correlation, normalized correlation coefficient, correlation coefficient, normalized difference, difference, normalized sum of differences, sum of differences, correlation, normalized correlation, normalized squared difference, squared difference and / or combinations thereof. The similarity index may also be user-defined. In some embodiments, multiple similarity indexes may be used or different similarity indexes may be used for different regions of the image template or the image itself.

[0107]

[0135] In operation 1107, the blocking image template is moved or slid to a new position on the image of the measurement structure. At the new sliding position, the overlap (or intersection) area between the block feature and the block image template changes. In one embodiment, a total weight C can be used to calculate a similarity score (i.e., a similarity index or another measure of the matching between the block image template and the image of the measurement structure). The total weight C is calculated by multiplying the weight map A of the image with the weight map B of the block image template. During sliding, A×B changes because the intersection area changes, causing a change in weight C. The weight map B of the block image template can be an initial weight map B′ that remains constant for the block image template, while an adaptive weight map can be generated by multiplication or other convolution of the weight map A of the image with the initial weight map B′ and calculated for each sliding position. In either case (i.e., when the weight map B changes or when the weight map B is the initial weight map B′ where the weight map B is constant), this means that an adaptive weight map is generated for each sliding position and the adaptive weight map is used to calculate the similarity for each sliding position. In other embodiments, the weight map can be updated based on the image of the measurement structure (or characteristics of the image of the measurement structure, such as pixel values, contrast, sharpness, etc.) at the new position, the weight map can be updated based on the blocking image template (such as updating based on overlap or convolution scores), or the weight map can be updated based on the block image template (e.g., updating based on the distance of the block image template from the image or focus center, etc.). From operation 1107, the method 1100 continues back to operation 1106, where the blocking image template is compared to another position on the image of the measurement structure based on the updated weight map. The iteration between operations 1106 and 1107 continues until the block image template is matched to a position on the image of the measurement structure or slides through all test image positions. The matching can be determined based on a threshold or a maximum similarity index. The matching can include matching multiple occurrences based on a threshold similarity score.In operation 1108, the block image template is matched to a location on the image of the measurement structure. After the block image template is matched, a measure of offset or process stability (overlay, edge placement error, offset measures, etc.) may be determined based on the matched location. As described above, method 1100 (and / or other methods and systems described herein) is configured to provide a general framework for matching an image template to a location on the image of the measurement structure based on a weight map.

[0108]

[0136] 12 illustrates an exemplary contour image template, according to one embodiment. The contour image template 1200 includes an inner contour 1202 and an outer contour 1204. The inner contour 1202 and the outer contour 1204 are illustrated as continuous, but may instead be discontinuous. The contour image template 1200 may be filled with or associated with pixel values, including grayscale values, and used as an image template for template matching. For example, within the inner contour 1202, the contour image template 1200 may have a grayscale value corresponding to a black value. Outside the outer contour 1204, the contour image template 1200 may have a grayscale value corresponding to a white value. Between the inner contour 1202 and the outer contour 1204, the pixel values ​​may correspond to a grayscale value of gray. The pixel values ​​of the contour image template 1200 may be adjusted based on user input. Alternatively, pixel values ​​of the contour image template 1200 may be defined by a function such as a sigmoid function as a function of position (examples of position functions include distance from template edge, distance from template center, distance to contour, etc.) The contour image template 1200 may also include "hot spots" or reference points 1206 that are used to determine a measure of offset relative to features of images of other templates, patterns, or structures.

[0109]

[0137] 13A and 13B show an exemplary composite image template for template matching with polarity matching, according to one embodiment. FIG. 13A shows a composite image template 1300 with pixel values ​​(or colors on a grayscale). FIG. 13B shows a composite image template 1310 that corresponds to the composite image template 1300 of FIG. 13A with the image polarity inverted. Because the images are acquired at different times and / or different positions, the image polarity may vary from image to image for the same structure. In optical images, the polarity may be a function of the illumination direction and the position of the focal plane. In SEM images, the polarity may be a function of the electron energy and layer thickness. In some cases, the polarity of the image template may be fully inverted. In some cases, the polarity of the image may not be fully inverted, but instead may be scaled or the dynamic range may be reduced or expanded. With scaled polarity, the contrast between features may be reduced and black and white features may appear in grayscale. The image template may have a single polarity value (e.g. ranging from -1 to 1) that linearly adjusts the pixel values ​​of the image template (pixel values ​​are typically 0 to 255). The image template may also include a polarity map, where one part of the image may have one polarity and another part of the image may have the opposite polarity. This may be useful in matching images with different thicknesses of underlying layers, as the thickness of the substrate may affect the polarity. Polarity may be a feature of the composite image template and the image template generated from one or more acquired images.

[0110]

[0138] FIG. 14 illustrates an exemplary method 1400 for generating an image template based on a composite image, according to one embodiment. Each of these operations is described in detail below. The operations of method 1400 illustrated below are intended to be exemplary. In some embodiments, method 1400 may be accomplished with one or more additional operations not described and / or without one or more of the operations described. Additionally, the order in which the operations of method 1400 are illustrated in FIG. 14 and described below is not intended to be limiting. In some embodiments, one or more portions of method 1400 may be implemented (e.g., by simulation, modeling, etc.) on one or more processing devices (e.g., one or more processors). The one or more processing devices may include one or more devices that perform some or all of the operations of method 1400 in response to instructions electronically stored on an electronic storage medium. The one or more processing devices may include one or more devices configured with hardware, firmware, and / or software, e.g., specifically designed for performing one or more of the operations of method 1400.

[0111]

[0139] In operation 1421, an artifact is selected from a layer of the measurement structure. The artifact or feature may be a physical feature, such as a contact hole, a metal line, an implant region, etc. The artifact may also be an image artifact, such as edge blooming, or a buried or blocked artifact. A shape of the artifact is determined. The shape may be defined by a GDS format, a shape simulated by a lithographic model, a detected shape, etc. In operation 1422, one or more process models are used to generate a top-down view of the artifact. The process models may include deposition models, etch models, implant models, stress-strain models, etc. The one or more process models may generate a simulated shape of the as-fabricated artifact. In a parallel operation 1423, one or more graphical inputs are selected for the artifact. The graphical inputs may be images of the as-fabricated artifact. The graphical inputs may also be based on user input or user knowledge, where the user updates the as-fabricated shape based in part on experience with similar as-fabricated elements. For example, the graphical input may be rounding or smoothing of corners.

[0112]

[0140] In operation 1424, the top-down view of the artifact is updated based on the graphical or user input. In operation 1425, a synthetic SEM image of the artifact is generated using the scanning electron microscope model. An image template is then generated based on the synthetic SEM image. In operation 1426, the image template is updated based on the acquired SEM image for the as-fabricated artifact. In operation 1427, the image template is matched to the image of the as-fabricated artifact. The image template may further include a weight map and may be matched to the as-fabricated artifact even if the artifact is partially blocked. As described above, method 1400 (and / or other methods and systems described herein) is configured to provide a general framework for generating image templates based on synthetic images.

[0113]

[0141] 15A-15E show an example composite template generated based on an image, according to one embodiment. FIG. 15A shows an example image 1500 of a non-repeating device structure on an IC. However, it will be understood that the present disclosure is not limited thereto. Non-repeating device structures, such as those found in random logic layers, do not have regular or periodic artifacts on which template matching or offset measurements can be performed. For image templates with multiple features on the same layer, template matching may include matching multiple of the features of the image template with the test image. Mapping multiple features may increase the robustness of the match.

[0114]

[0142] A composite template is selected to create multiple matching points. Various artifacts of the exemplary image 1500 are selected for matching. The artifacts are selected based on whether they are suitable for matching. Suitability includes factors such as artifact stability and robustness, where artifacts that are not repeatable or have a large natural variability (e.g., metal lines) are not useful for matching. Suitability includes image characteristics factors, where artifacts must be visible in the image to be used for template matching. Artifacts can be selected based on size, average brightness, contrast with neighboring elements, edge thickness, intensity logarithmic slope (ILS), etc. A reference image, such as the exemplary image 1500, can be analyzed to identify artifacts in the composite template. For a layer that includes multiple elements, the most suitable element can be selected.

[0115]

[0143] Groups of process layer patterns or artifacts can be selected based on pattern size, contrast, ILS, stability, etc. This selection can be based on (1) grouping of patterns by pattern size including from GDS data, (2) one or more of predicted ILS, cross-sectional area, edge characteristics, process stability, etc. determined by a process model, and / or (3) estimated SEM image contrast by a SEM simulator or model such as eScatter or CASINO.

[0116]

[0144] The composite template may further include weight maps and non-emphasis areas. A composite template containing a group of patterns may be assigned weight maps indicating differences in priority or emphasis, differences in ILS, differences in contrast, differences between edge regions or contours and central or filled parts of the image template, blocked parts within the template area, etc. By de-emphasizing areas of the composite template, e.g., by weighting them relatively less than other areas, various "ignored" or non-emphasis areas are created. These non-emphasis areas may correspond to artifacts on the image that are not matched (because they are not stable enough to be matched or are not regular, e.g., may vary by location). The exemplary image 1500 includes line drawings corresponding to various features 1502a-1502e of a non-repeating device. As shown, features 1502a-1502e exhibit different levels of variation, with elongated features exhibiting ripple marks and other variations (such as features 1502a, 1502b) and rounded shapes being more regular (such as features 1502b, 1502d, 1502e). The level of stability of a feature can be determined based on multiple images acquired for different fabrication versions of an exemplary image (e.g., for multiple locations of the same pattern on a wafer or multiple wafers containing instances of the same pattern). Also shown is a "hot spot" or reference point 1510, which can be selected based on the image (e.g., in the center of the image) or added to the image and need not be part of the structure or image itself.

[0117]

[0145] FIG. 15B illustrates an example composite template 1520 corresponding to a first layer of the structure of the example image 1500. The composite template 1520 includes various example patterns 1522a-e with defined spatial relationships between the patterns. Each of the example patterns 1522a-e in the composite template 1520 is circular, however, any suitable shape may be selected or otherwise used. Additionally, each of the patterns 1522a-e in the composite template 134 may have pixel values, contours, weight maps, polarities, etc. as generally described with respect to image templates. The patterns in the composite template 1520 may further include weight maps that may be in addition to the weight maps of the composite template 1520 (e.g., the example patterns 1522a-e may each have the same or different weight maps, and the composite template 1520 may have an additional weight map corresponding to the composite template 1520 as a whole). The weight map of the composite template 1520 is heavily weighted towards the (selected) pattern (or identified artifact) and de-emphasized or lightly weighted towards areas outside the pattern. Thus, the weighting creates "ignored" or non-emphasized regions that are essentially excluded from template matching, focusing the template matching on matching the constituent patterns. Non-emphasized regions or areas can be weighted equal to or near zero, or otherwise lower or lighter than the selected artifacts or patterns. Also shown are "hot spots" or fiducial points 1510b, which can be selected based on the image (i.e. the entire image including multiple layers) or added to the image, and may not be part of the structure itself. For example, in the composite template 1520, the fiducial points 1510b do not correspond to features of the template.

[0118]

[0146] FIG. 15C illustrates an example composite template 1530 corresponding to a second layer of the structure of the example image 132. The first and second layers can have any spatial or blocking relationship, and in some cases the first and second layers can be the same layer of the measurement structure. The composite template 1530 includes various example patterns 1532a-e with a defined spatial relationship between the patterns. Each of the example patterns 1532a-e is shown as a rounded rectangle, but any suitable shape can be selected, and multiple shapes can be selected within a single composite template. Each of the patterns can have pixel values, contours, weight maps, polarities, etc., as generally described with respect to the image templates. The composite template 1530 further includes a weight map as previously described with respect to the composite template 1520. The "ignore" or non-enhanced regions of multiple composite templates can overlap, and an artifact selected for one composite template can correspond to a non-enhanced region in another composite template. Also shown are "hot spots" or reference points 1510c, which again may be selected based on the image or added to the image and may not be part of the structure itself.

[0119]

[0147] 15D shows the exemplary composite template 1520 of FIG. 15B and the exemplary composite template 1530 of FIG. 15C matched in an overlay image 1540 to a position on the exemplary image 1500 of FIG. 15A. A measure of offset, which may be a measure of alignment, a measure of overlay, a measure of EPE, etc., may be determined based on the relative positions of the fiducial points 1510b, 1510c from each of the composite templates 1520, 1530. Each of the composite templates (i.e., 1520 and 1530) includes fiducial points or "hot spots" 1510b, 1510c that overlap in the xy plane with respect to a reference or "gold" image of the structure. In some embodiments, the measure of overlay or offset may be determined by matching each of the composite templates 1520 and 1530 to the exemplary image 1500. Independent matching of the composite template (i.e., 1520 and 1530) to the exemplary image 1500 identifies locations for fiducial points of the composite template on the exemplary image 1500. Based on a comparison of the two (or more) fiducial points 1510b, 1510c when matched, a measure of offset or overlay can be determined. The fiducial points 1510b, 1510c need not correspond to features or artifacts (i.e., patterns of the composite template) selected for matching in the composite template, but instead may occur within non-enhanced or "ignored" regions. The fiducial points 1510 may not correspond to physical elements of the image of the reference structure. Because the fiducial points 1510b, 1510c are co-located in the "golden" image, the distance or vector between the fiducial points of the opposing layers is a measure of the misalignment between the layers.

[0120]

[0148] FIG. 15E shows fiducial point 1510b of composite template 1520 and fiducial point 1510c of composite template 1530. The fiducial points 1510b and 1510c are enlarged relative to the overlaid image 1540 of FIG. 15D, but maintain the same relationship. An offset vector 1550 is shown, which corresponds to the layer-to-layer misalignment between the composite template 1520 and the composite template 1530 matched to the example image 1500. The fiducial point 1510b of the composite template 1520 of FIG. 15B occurs in a non-enhanced or "ignored" region of that template. The fiducial point 1510c of the composite template 1530 of FIG. 15C corresponds to a feature of that template. Composite templates can be used to match between layers that do not have overlapping features but are interspersed with features from multiple layers. A measure of overlay for overlapping features can also be determined based on a measure of offset or other measure of misalignment between layers.

[0121]

[0149] FIG. 16 illustrates an exemplary method 1600 for generating a composite template, according to one embodiment. Each of these operations is described in detail below. The operations of method 1600 illustrated below are intended to be exemplary. In some embodiments, method 1600 may be accomplished with one or more additional operations not described and / or without one or more of the operations described. Additionally, the order in which the operations of method 1600 are illustrated in FIG. 16 and described below is not intended to be limiting. In some embodiments, one or more portions of method 1600 may be implemented (e.g., by simulation, modeling, etc.) on one or more processing devices (e.g., one or more processors). The one or more processing devices may include one or more devices that perform some or all of the operations of method 1600 in response to instructions electronically stored on an electronic storage medium. The one or more processing devices may include one or more devices configured with hardware, firmware, and / or software, e.g., specifically designed for performing one or more of the operations of method 1600.

[0122]

[0150] In operation 1641, an image of the measurement structure is acquired or obtained. The image may be an optical image, a scanning electron microscope image, another electromagnetic image, etc. The image may include multiple images, such as an averaged image. The image may include information regarding contrast, intensity illumination, stability, and size. In operation 1642, a composite image of the measurement structure is acquired. The composite image may be acquired from one or more models, refined based on the acquired images, or generated based on any of the methods discussed above. In operation 1643, at least two artifacts of the image are acquired or selected. The image may be an acquired image or an as-measured image or a composite image (including any combination thereof). The at least two artifacts may include a physical element of the measurement structure or an image artifact that is not a physical element of the measurement structure or an image artifact that corresponds to an interaction between two or more physical elements, but is not a physical element itself. The artifacts may be selected based on at least one of the size of the artifact, the contrast of the artifact, the process stability of the artifact, the artifact intensity log slope, or a combination of these factors. In operation 1644, a spatial relationship between at least two artifacts is determined. The spatial relationship may be a distance, a direction, a vector, etc. The spatial relationship may be fixed or may be adjustable and matchable to the image. A fixed spatial relationship may still be scaled or rotated during template matching (i.e., the spatial relationship between the patterns of the composite template are linearly adjusted together).

[0123]

[0151] In operation 1645, a composite template is generated based on the at least two artifacts and the spatial relationship. In operation 1646, a weight map is generated for the composite template. The composite template includes a weight map and a non-enhanced area. The non-enhanced area is weighted less than the at least two artifacts. Additional artifacts may be selected for the non-enhanced area based on the size of small artifacts, the size of large artifacts, poor artifact contrast, artifact process instability, poor artifact intensity log-gradient, or combinations thereof. The composite template may include an image template for each of the at least two artifacts, which may further include weight maps for individual elements of the pattern or elements of the entire pattern. In operation 1647, the composite template is matched to a location on the image of the measurement structure. This matching may include any of the matching methods described above. As described above, method 1600 (and / or other methods and systems described herein) is configured to provide a general framework for generating image templates based on a composite image.

[0124]

[0152] 17 shows a schematic representation of determining a measure of offset based on multiple image templates, each template itself consisting of a group of patterns, according to one embodiment. A first composite template 1701, a second composite template 1702 and a third composite template 1703 are shown. A set of images of the measurement structure (e.g., images 1710a, 1710b and 1710c) is also shown. A measure of offset, which may be a measure of overlay, is determined for the inter-layer misalignment between each of the layers. For example, a measure of offset or overlay is determined between the first composite template and the second composite template, the first composite template and the third composite template and the second composite template and the third composite template.

[0125]

[0153] The composite template may further include partially blocked elements, where a feature of the third composite template 1703 is partially blocked by both a feature of the first composite template 1701 and a feature of the second composite template 1702, and a feature of the second composite template 1702 is blocked by a feature of the first composite template 1701. The weight map including the non-emphasized regions may be supplemented by weight maps of the patterns or individual elements of the patterns. In some embodiments, the weight map of the image template may be adaptively updated during template matching. For example, the weight map of the third composite template 1703 may de-emphasize the areas depicted as blank (i.e., area 1720), but may also adaptively de-emphasize or lightly weight the blocked portions of the image template during template matching.

[0126]

[0154] 18A-18G show schematic representations of layer-by-layer template matching. FIG. 18A shows an example schematic 1800 of a measurement structure. The example schematic 1800 can be a GDS (or "GDSII") or plan view of the structure to be fabricated. The example schematic 1800 includes various layers including a non-patterned layer 1810, a metal layer 1820, a first feature layer 1830, and a second feature layer 1840. The example schematic 1800 is an example geometry provided to facilitate the explanation of layer-by-layer template matching, and thus the structure is merely illustrative. The non-patterned layer 1810 can be a layer on which no fabrication steps are performed on the substrate (e.g., a bare silicon or silicon dioxide layer) or a layer on which one or more uniform fabrication steps are performed on the substrate (e.g., a uniform dielectric deposition). The metal layer 1820 may be a via layer, and the metal of the metal layer 1820 electrically connects one or more layers of the structure represented by the exemplary schematic 1800, or further electrically connects one or more of the layers of the structure of the exemplary schematic 1800 to layers in another wafer or measurement structure. The first feature layer 1830 and the second feature layer 1840 may be composed of the same or different materials. In the exemplary schematic 1800, the first feature layer 1830 and the second feature layer 1840 may be composed of metals and may have similar chemical, optical, or electronic properties. The metal layer 1820, the first feature layer 1830, and the second feature layer 1840 may correspond to different fabrication steps, such as different lithography, etching, deposition, planarization, etc. The layers of the exemplary schematic 1800 may include more or fewer layers with more or fewer features, and may include multi-layer features. In this exemplary schematic diagram, features of metal layer 1820 and second feature layer 1840 overlap, which can lead to features of one layer being blocked by features of another layer in an image (optical image, electron microscope image, etc.).

[0127]

[0155] FIG. 18B shows a cross section 1850 of the exemplary schematic diagram 1800 of FIG. 18A. The cross section 1850 shows the substrate 1808, the first non-patterned layer 1812, the metal layer 1820 (similar to that in FIG. 18A), the second non-patterned layer 1814, the first feature layer 1830 (similar to that in FIG. 18A), the third non-patterned layer 1832, the second feature layer 1840 (similar to that in FIG. 18A), and the non-patterned cap layer 1842. The layers in the cross section 1850 are only exemplary layers provided for ease of explanation, and the cross section (or measurement structure) may include more or fewer layers. The metal layer 1820 may be a via embedded in or through the first non-patterned layer 1812 and the second non-patterned layer 1814. The metal layer 1820 may connect the substrate 1808 and the second feature layer 1840. The first feature layer 1830 and the second feature layer 1840 may both include features embedded in or through the third unpatterned layer 1832, but the first feature layer 1830 and the second feature layer 1840 may be patterned using different lithography masks or different lithography steps. Thus, the features of the first feature layer 1830 and the second feature layer 1840 may be offset from one another as a result of variations in alignment, exposure, development, etching, deposition, etc.

[0128]

[0156] 18C illustrates a composite image 1860 corresponding to the exemplary schematic 1800 of FIG. 18A. The composite image 1860 includes modeling of the shape of the created features based on the GDS information included in the schematic 1800. The composite image 1860 illustrates, for example, the rounding of angles that occurs on the square and rectangular features of the exemplary schematic 1800. The composite image includes image areas corresponding to the non-patterned layer 1810 of the exemplary schematic 1800, features corresponding to the metal layer 1820 of the schematic 1800, features corresponding to the first feature layer 1830 of the schematic 1800, and features corresponding to the second feature layer 1840 of the schematic 1800. In the composite image 1860, the features of the metal layer 1820 are shown in white, while the areas corresponding to the non-patterned layer 1810 are shown in black. The white of the features of the metal layer 1820 may correspond to the expected image intensity in the composite image 1860 and may correspond to scattered electrons reflected from the metal in contact with the grounded electron source in the SEM image. The black of the areas of the non-patterned layer 1810 may correspond to the expected image intensity in the composite image 1860 and may correspond to fewer scattered electrons emitted from the insulating layer. The colors of the composite image 1860 are only exemplary colors. The features of the first feature layer 1830 are shown as hatched areas, while the features of the second feature layer 1840 are shown as gray rounded rectangles. The difference in shape between the composite image 1860 and the exemplary schematic 1800 of FIG. 18A may be due to optical effects, fabrication effects, etc. The difference between the exemplary schematic 1800 and the composite image 1860 may be determined based on optical modeling, process modeling, microscope modeling, etc.

[0129]

[0157] 18D illustrates an exemplary acquired image 1870 corresponding to the exemplary schematic 1800 of FIG. 18A. The acquired image 1870 may be an SEM image, an optical image, or the like. The acquired image 1870 includes areas of the non-patterned layer 1810, features of the metal layer 1820, and features 1872 of the first and second feature layers (e.g., the first and second feature layers 1830 and 1840 of the exemplary schematic 1800 of FIG. 18A). The non-patterned layer 1810, represented by black fill, may correspond to areas of low electron or photon scattering. The metal layer 1820 features, represented by white fill, may correspond to areas of high electron or photon scattering. The features of the metal layer 1820 may be very bright or may cause a large amount of photon or electron scattering, and therefore have soft edges (as depicted). The features 1872 of the first and second feature layers may comprise different material properties (i.e., different thicknesses of the same material, different roughness of the same material, etc.) or entirely different materials. However, even though the features 1872 of the first and second feature layers are different, they may have the same or different image quality, e.g., brightness, sharpness, etc. The features 1872 of the first and second feature layers, represented by gray fill, may correspond to areas of moderate electron or photon scattering. The features of the metal layer 1820 may be so bright (e.g., reflective or scattering), reflective or scattering) that even if the features of the metal layer 1820 are buried beneath the features 1872 of the first and second feature layers, the buried features of the metal layer 1820 may obscure or otherwise block the features 1872 of the first and second feature layers.

[0130]

[0158] 18E illustrates an exemplary template 1880 for features of the metal layer 1820 of the acquired image 1870. The exemplary template 1880 includes multiple individual templates 1882 or sub-templates that each correspond to features of the metal layer 1820 of the acquired image 1870 and are spatially organized into a composite template. The spatial relationship between the individual templates 1882 may be configured with information stored in the exemplary template 1880. The exemplary template 1880 may further include one or more weight maps, such as a weight map for each of the individual templates 1882, a total weight map, etc. The exemplary template 1880 may be matched to the acquired image 1870 based on template matching, including using the methods described above. The exemplary template 1880 corresponding to the non-block features may be matched to the acquired image 1870 before the template corresponding to the block features.

[0131]

[0159] 18F illustrates an exemplary template 1884 for features of the second feature layer 1840 of the composite image 1860 (e.g., a portion of features 1872 of the first and second feature layers of the acquired image 1870). The exemplary template 1884 includes multiple individual templates 1886 or sub-templates that each correspond to a feature of the second feature layer 1840 of the composite image 1860 (that are a portion of features 1872 of the first and second feature layers of the acquired image 1870) and are spatially configured into a composite template. The spatial relationship between the individual templates 1886 may be configured with information stored in the exemplary template 1884. The exemplary template 1884 may further include one or more weight maps, e.g., a weight map for each of the individual templates 1886, a total weight map, etc. The exemplary template 1884 may be matched to the acquired image 1870 based on template matching, including using the methods described above. An exemplary template 1884 corresponding to both blocking and non-blocking features can be matched to acquired image 1870 after an exemplary template 1880 for features of metal layer 1820 that blocks some but not all of the features of the second feature layer of composite image 1860. Even though features 1872 of the first and second feature layers of acquired image 1870 have similar image characteristics, these features are matched to separated templates. The features of the template correspond to features of a single lithography step (or a subset of features of a single lithography step) whose spatial relationship is known and that are set by lithography (optical lithography, DUV lithography, e-beam assisted lithography, etc.).

[0132]

[0160] FIG. 18G illustrates an exemplary template 1888 for features of the first feature layer 1830 of the composite image 1860 (e.g., a portion of features 1872 of the first and second feature layers of the acquired image 1870). The exemplary template 1888 includes multiple individual templates 1890 or sub-templates that each correspond to a feature of the first feature layer 1830 of the composite image 1860 (that are a portion of features 1872 of the first and second feature layers of the acquired image 1870) and are spatially configured into a composite template. In this case, the individual templates may be approximately one-dimensional. The spatial relationship between the individual templates 1890 may be configured with information stored in the exemplary template 1888. The exemplary template 1888 may further include one or more weight maps, such as a weight map for each of the individual templates 1890, a total weight map, etc. The exemplary template 1888 may be matched to the acquired image 1870 based on template matching, including using the methods described above. An exemplary template 1888 corresponding to a non-block feature may be matched to the acquired image 1870 before or after the exemplary template 1880 for the feature in the metal layer 1820 and before or after the exemplary template 1884 for the feature in the second feature layer 1840 of the composite image 1860. As described above, even if the features 1872 in the first and second feature layers of the acquired image 1870 have similar image characteristics, they are matched to separate templates.

[0133]

[0161] 19A-19F show schematic representations of using template matching to select regions of interest. FIG. 19A shows an exemplary schematic 1900 for a multi-layer structure. The exemplary schematic 1900 is a schematic provided for ease of explanation and does not limit structure orientation. The multi-layer structure includes a first layer 1901, a second layer 1902, a third layer 1903, and a fourth layer 1904. The features of the first layer 1901 are shown as multiple repeating grey ellipses at angles to the major and minor axes of the exemplary schematic 1900. The features of the second layer 1902 are shown as multiple repeating ellipses with white fill and black borders approximately in the center of the features of the first layer 1901. The features of the third layer 1903 are shown as multiple repeating bars filled with upward diagonal hatching oriented parallel to the minor axis of the exemplary schematic 1900. The features of the fourth layer 1904 are shown as a plurality of repeating bars filled with diagonal downward hatching oriented parallel to the long axis of the exemplary schematic diagram 1900. The plurality of bars of the third layer 1903 and the plurality of bars of the fourth layer 1904 intersect approximately at the centers of the features of the first layer 1901 and the second layer 1902. As shown, the features of the fourth layer 1904 block the features of the first layer 1901, the second layer 1902, and the third layer 1903. The features of the third layer 1903 block the features of the first layer 1901 and the second layer 1902. The features of the second layer 1902 block the features of the first layer 1901. The blocking of one layer by another can be physical (i.e., the first layer 1901 is a buried layer and the fourth layer 1904 is the top layer), but can also or instead be electronic, optical or other image-induced blocking (e.g., if the first layer 1901 is not an electron scattering material and the material of the fourth layer 1904 is a good electron scattering material).

[0134]

[0162] FIG. 19B shows an exemplary image 1910 obtained for the multi-layer structure described by the exemplary schematic 1900 of FIG. 19A. The exemplary image 1910 is a grayscale image, but could alternatively be a color image or other multi-wavelength image. This exemplary image includes information about features of the first layer 1901, the second layer 1902, the third layer 1903, and the fourth layer 1904 of the exemplary schematic 1900. The unblocked portion of the first layer 1901 corresponds to the dark gray area 1911 of the exemplary image 1910. The unblocked portion of the second layer 1902 corresponds to the medium gray area 1912 of the exemplary image 1910. The unblocked portion of the third layer 1903 corresponds to the light gray area 1913 of the exemplary image 1910. The unblocked portion of the fourth layer 1904 (i.e., the entire area of ​​the fourth layer 1904) corresponds to the white area 1914 of the exemplary image 1910. Non-pattern areas of the example schematic 1900 of FIG. 19A are displayed as black areas 1915 in the example image 1910. The example image 1910 is provided for ease of explanation only and not for limitation. The example image 1910 is provided as an example image in which regions of interest may be identified based on template matching and further image enhancement may be performed. The example image 1910 is a grayscale image in which some features are difficult to distinguish (e.g., close in color or pixel value). Furthermore, the example image 1910 includes some areas that are very dark (e.g., black areas 1915 and dark gray areas 1911) and some areas that are very light (e.g., white areas 1914). Because the pixel values ​​of the example image 1910 may have a wide range, simply increasing the range of pixel values ​​(e.g., making the example image 1910 brighter or darker) may not make the features clearer or more distinguishable. In this example, saturation is used as the image quality element to enhance, but similar explanations can be made for image sharpening, image softening, and other image enhancement techniques. To select a region of interest (for image enhancement or other reasons such as segmentation, template matching, etc.), a region of the image corresponding to a particular layer can be selected.

[0135]

[0163] FIG. 19C illustrates an example template matching 1920. The example template matching 1920 includes a first image template, shown as a dotted rectangle 1922 corresponding to the fourth layer 1904 of the example schematic diagram 1900 of FIG. 19A, matched to the example image 1910 of FIG. 19B. The fourth layer 1904 of the example schematic diagram 1900 corresponds to the white area 1914 of the example image 1910. By matching a template corresponding to the fourth layer 1904 (e.g., corresponding to a feature of the fourth layer 1904) to the example image 1910, the white area 1914 of the example image 1910 can be selected or deselected. The example template matching 1920 can be used to select a region within the dotted rectangle 1922, to exclude a region within the dotted rectangle 1922, to locate an area or region based on the location (e.g., proximity) of a region within the dotted rectangle 1922, to segment an image, etc. One or more regions of interest may be identified based on the area within the dotted rectangle 1922. In the following example, the area within the dotted rectangle 1922 is excluded from the region of interest.

[0136]

[0164] FIG. 19D illustrates an exemplary region of interest 1930 determined based on the template matching 1920 of FIG. 19C. In this example, the white area 1914 of the exemplary image 1910 and the area of ​​the dotted rectangle 1922 corresponding to the fourth layer 1904 of the exemplary schematic diagram 1900 are excluded from the region of interest 1930. Although the region of interest 1930 in this example includes areas of the exemplary image 1910 that do not correspond to the dotted rectangle 1922 of FIG. 19C, this is for ease of illustration only, and the region of interest may instead be smaller or may be a subset of areas of the image that may or may not correspond to features of the layer identified by template matching. The areas excluded from the region of interest are identified by a dotted grey rectangle 1935 with a black fill. Excluding one or more areas from the region of interest may be accomplished by blocking the image of the portions to be excluded, such as by masking pixel values ​​within the excluded region. Areas not included in the region of interest may not be masked or blocked, but may instead be identified by a border or other image artifact. The region of interest 1930 may correspond to an area identified by template matching to correspond to features of a single layer, a subset of features of a single layer, features of multiple layers, etc. The region of interest 1930 may be used to increase the accuracy or speed of template matching of subsequent layers. For example, the region of interest 1930 may be used to generate a probability map of the locations of features of the first layer 1901, the second layer 1902, and the third layer 1903, as the features of each of these layers may intersect with areas excluded from the region of interest 1930. Thus, template matching for features of the first layer 1901, the second layer 1902, and the third layer 1903 may be concentrated near the boundaries of the region of interest 1930.

[0137]

[0165] Additionally, the region of interest 1930 can be used to perform image enhancement (as shown). Excluding the areas identified by the black filled dotted grey rectangle 1935 may allow the region of interest 1930 to be brightened or otherwise enhanced or adjusted (as shown). Excluding the areas identified by the black filled dotted grey rectangle 1935 is shown as if those areas were blocked or otherwise masked from the image. The remaining areas (e.g., the area of ​​the region of interest 1930) can then be color adjusted to make the colors more distant or more distinct. As an example, the deblocked portions of the first layer 1901 that correspond to the dark grey areas 1911 of the exemplary image 1910 can be brightened to correspond to the medium grey areas 1931. The deblocked portions of the second layer 1902 that correspond to the medium grey areas 1912 of the exemplary image 1910 can be brightened to correspond to the light grey areas 1932. The deblocked portions of the third layer 1903 that correspond to the light gray areas 1913 of the exemplary image 1910 may be lightened to correspond to the white areas 1933. The non-patterned areas of the exemplary schematic 1900 of FIG. 18A that appear as black areas 1915 in the exemplary image 1910 may remain as black areas 1915. The regions of interest 1930 may be subjected to image enhancement as described above or using other standard algorithms. Multiple regions of interest may be identified in the image by template matching. Overlapping regions of interest may also be identified based on matching of multiple templates. In some cases, the union, intersection, complement, etc. of multiple regions of interest may provide even greater specificity or may further identify another region of interest in the image.

[0138]

[0166] Figure 19E illustrates an example histogram 1940 showing pixel counts along a y-axis 1944 versus pixel value along an x-axis 1942 for the example image 1910 of Figure 19A. A curve 1946 represents pixel counts versus pixel value for the example image 1910. The peak identified by the dotted ellipse 1948 represents the white area 1914 of the example image. This curve 1946 has both very low and very high pixel values ​​representing black and white pixels, which may be difficult to adjust to improve image quality, such as brightening the image.

[0139]

[0167] FIG. 19F illustrates an example histogram 1950 showing pixel counts along a y-axis 1954 versus pixel values ​​along an x-axis 1952 for the region of interest 1930 of FIG. 19D. Curve 1956 represents pixel counts versus pixel values ​​for the region of interest 1930. Black boxes 1958 represent pixel values ​​that were previously present in the example histogram 1940 of FIG. 19E that have been removed from the region of interest 1930. Black boxes 1958 obscure pixel values ​​of pixels in the white area 1914 of image 1910. Once these values ​​have been removed from the histogram, the values ​​of the remaining pixels can be adjusted, such as by expanding the range of pixel values ​​along the direction of arrow 1960 or along the direction of arrow 1962. Other image enhancement techniques can also be applied.

[0140]

[0168] FIG. 20 shows a schematic representation of image segmentation. FIG. 20 shows an example image 2000 with false coloring, similar to the composite image 1860 of FIG. 18C. The example image 2000 is shown for ease of illustration, and the method of image segmentation described can be applied to other images and structures. The example image 2000 also corresponds to a structure based on the example schematic 1800 of FIG. 18A. The example image 2000 includes black areas 2001 corresponding to non-patterned areas on the multi-layer structure, white areas 2002 corresponding to metal vias of the multi-layer structure, hatched areas 2003 corresponding to features of a first feature layer of the multi-layer structure, and grey areas 2004 corresponding to features of a second feature layer of the multi-layer structure. In the example image, the hatched areas 2003 of the features of the first feature layer and the grey areas 2004 of the features of the second feature layer are shown with different fills for ease of illustration. In an acquired image, such as that shown in acquired image 1870 of FIG. 18D, features of a first feature layer and features of a second feature layer may include approximately the same pixel value or intensity illuminance.

[0141]

[0169] As shown in the first exemplary template matching 2020, a first image template 2010 corresponding to the metal vias in the white area 2002 is matched to the exemplary image 2000. The first image template 2010 may include multiple templates corresponding to the metal via features. The first image template 2010 may match to one location on the exemplary image 2000, match to multiple locations on the exemplary image 2000, or even partially match to a location or a portion of a location on the exemplary image 2000. The first image template 2010 may be matched to the exemplary image 2000 by using one or more adaptive weight maps. The first image template 2010, including the area corresponding to the first layer labeled with a "1", may be used to segment the exemplary image 2000. The first exemplary template matching 2020 shows the areas or segments identified as corresponding to the first image template 2010, also labeled with a "1".

[0142]

[0170] As shown in the second exemplary template matching 2040, a second image template 2030 corresponding to the gray area 2004 of the feature of the second feature layer is matched to the exemplary image 2000. The second image template may include multiple templates corresponding to the features of the second feature layer. The second image template 2030 may match to one location on the exemplary image 2000, match to multiple locations on the exemplary image 2000, or even partially match to a location or part of a location on the exemplary image 2000. The second image template 2030 may alternatively or additionally be matched to one or more locations on the first exemplary template matching 2020 (e.g., the second image template 2030 may be matched to the exemplary image 2000 to which the first image template 2010 has already been matched). The second image template 2030 may be matched to the exemplary image 2000 by using one or more adaptive weight maps. A second image template 2030, including regions corresponding to the second layer labeled with a "2," can be used to segment the example image 2000. A second example template match 2040 shows regions or segments identified as corresponding to the second image template 2030, also labeled with a "2."

[0143]

[0171] As shown in the second exemplary template matching 2060, a third image template 2050 corresponding to the hatched area 2003 of the feature of the first feature layer is matched to the exemplary image 2000. The third image template may include multiple templates corresponding to the features of the first feature layer. The third image template 2050 may match to one location on the exemplary image 2000, match to multiple locations on the exemplary image 2000, or even partially match to a location or part of a location on the exemplary image 2000. The third image template 2050 may alternatively or additionally be matched to one or more locations on the second exemplary template matching 2040 (e.g., the third image template 2050 may be matched to the exemplary image 2000 to which the first image template 2010 and the second image template 2030 have already been matched). The third image template 2050 may be matched to the exemplary image 2000 by using one or more adaptive weight maps. A third image template 2050, including a region corresponding to the third layer, labeled with a "3," can be used to segment the example image 2000. A third example template match 2060 shows a region or segment identified as corresponding to the third image template 2050, also labeled with a "3."

[0144]

[0172] The image can be segmented based on the matched image templates. In some cases, the segmentation can approximately correspond to the configuration of the features of the templates. In other cases, the segmentation can include regions outside the individual elements of one or more templates, or can exclude regions inside the individual elements of one or more templates. For example, the second example template matching 2040 can exclude regions from the second segmentation that are inside the features of the second image template 2030 and are also inside the features of the first image template 2010. In another example, the segmentation corresponding to the third image template 2050 can include boundary regions outside the features of the third image template 2050.

[0145]

[0173] 21A-21B show a schematic representation of a template alignment based on a previous template alignment. FIG. 21A shows an example image 2000 with false coloring of FIG. 20. The example image 2000 is used again for ease of explanation, but the described method can be applied to images of any multi-layer structure. The example image 2000 includes black areas 2001 corresponding to non-pattern areas on the multi-layer structure, white areas 2002 corresponding to metal vias of the multi-layer structure, hatched areas 2003 corresponding to features of a first feature layer of the multi-layer structure, and grey areas 2004 corresponding to features of a second feature layer of the multi-layer structure. In the example image, the hatched areas 2003 of the features of the first feature layer and the grey areas 2004 of the features of the second feature layer are shown with different fills for ease of explanation. In an acquired image such as that shown in acquired image 1870 of FIG. 18D, the features of the first feature layer and the features of the second feature layer may include approximately the same pixel values ​​or intensity illuminance.

[0146]

[0174] As shown in the first example template match 2020, a first image template 2010 corresponding to the metal vias in the white area 2002 is matched to the example image 2000 using any suitable method, including those described with respect to Figure 20. The first example template match 2020 shows a region or segment identified as corresponding to the first image template 2010, also labeled with a "1".

[0147]

[0175] Based on the alignment of the first image template 2010 to the example image 2000, potential areas of features of the second image template 2030 (corresponding to features of the second feature layer) are located. A potential weight map 2110 is shown showing areas of probability for the location of features of the second feature layer relative to features of the first image template. The potential weight map 2110 is black in areas where the probability of the feature of the second feature layer being located is low, and white in areas where the probability of the feature of the second feature layer being located is high. The potential weight map 2110 is applied to the example image 2000 based on the location of the first image template 2010 to generate a second layer probability map 2120. The second layer probability map 2120, which contains information about where the feature of the second layer is likely to be located, can be used to select an initial location where the second image template 2030 is matched to the example image 2000, or can be used to exclude potential locations of the second image template 2030 relative to the example image 2000 from template matching or searching. In some embodiments, the second layer probability map 2120 can be used to guide the matching of the second image template 2030 to the example image 2000. The second layer probability map 2120 can further be used in conjunction with a weight map in template matching.

[0148]

[0176] As shown in the second example template matching 2040, a second image template 2030 corresponding to the gray area 2004 of the features in the second feature layer is matched to the example image 2000. Template matching may be performed using any suitable method, including those described with respect to Figure 20. The second example template matching 2040 shows regions or segments identified as corresponding to the second image template 2030, also labeled with a "2".

[0149]

[0177] A depiction of a schematic representation of template alignment based on a previous template alignment continues in FIG. 21B. Based on the alignment of the first image template 2010 to the exemplary image 2000, potential areas of features of the third image template 2050 (corresponding to features of the first feature layer) can be located. A potential weight map 2140 is shown showing the area of ​​probability of the location of features of the first feature layer based on the location of features of the metal layer. Additionally or alternatively, based on the alignment of the second image template 2030 to the exemplary image 2000, potential areas of features of the third image template 2050 (corresponding to features of the first feature layer) are located. A potential weight map 2150 is shown showing the area of ​​probability of the location of features of the first feature layer relative to features of the second image template. The potential weight maps 2140, 2150 are black in areas where there is a low probability that a feature of the first feature layer is located and white in areas where there is a high probability that a feature of the first feature layer is located. Either or both of the latent weight maps 2140, 2150 may be applied to the example image 2000 to generate a third layer probability map 2160. An intersection, union, etc. of the latent weight maps 2140, 2150 may also be used. The third layer probability map 2160, which contains information about where features of the first feature layer are likely to be located, may be used to select an initial location where the third image template 2050 is matched to the example image 2000, or may be used to exclude potential locations of the third image template 2050 relative to the example image 2000 from template matching or searching. In some embodiments, the third layer probability map 2160 may be used to guide the matching of the third image template 2050 to the example image 2000. The third layer probability map 2160 may further be used in conjunction with the weight map in template matching.

[0150]

[0178] As shown in the second example template matching 2060, a third image template 2050 corresponding to a hatched area 2003 of a feature in the first feature layer is matched to the example image 2000 using any suitable method, including those described above with respect to FIG. 20. The third image template may include multiple templates corresponding to features in the first feature layer. The third example template matching 2060 shows a region or segment identified as corresponding to the third image template 2050, also labeled with a "3".

[0151]

[0179] FIG. 22 shows a schematic representation of image-to-image comparison using layer-wise template matching. FIG. 22 shows an example image 2000 with false coloring of FIG. 20. The example image 2000 is again shown for ease of explanation, but the described method can be applied to images of any multi-layer structure. The example image 2000 includes black areas 2001 corresponding to non-pattern areas on the multi-layer structure, white areas 2002 corresponding to metal vias of the multi-layer structure, hatched areas 2003 corresponding to features of a first feature layer of the multi-layer structure, and grey areas 2004 corresponding to features of a second feature layer of the multi-layer structure. In the example image, the hatched areas 2003 of the features of the first feature layer and the grey areas 2004 of the features of the second feature layer are shown with different fills for ease of explanation. In an acquired image such as shown in acquired image 1870 of FIG. 18D, the features of the first feature layer and the features of the second feature layer may include approximately the same pixel values ​​or intensity illuminance.

[0152]

[0180] An image-to-image comparison can be formed from multiple images. Image-to-image comparison can be used to evaluate process control, lithography masks, process probabilities, etc. Several images, such as the exemplary image 2000, can be aligned based on template matching to result in an image-to-image alignment that is aligned layer by layer. For example, N images of a multi-layer structure, such as the exemplary image 2000, can be overlaid based on template matching. A layer of the multi-layer structure can be selected. A template corresponding to the selected layer can then be matched to each of the images. The multiple images can then be overlaid based on the location of the matching template matched to information corresponding to a single layer. Image alignment based on a single layer can essentially eliminate alignment errors due to non-uniformities in a non-selected layer, including overlay errors between any two layers. The use of an adaptive weight map that can improve the matching of the template to the image can also improve the alignment between the images by taking into account blocking and block structures and downweighting portions of the image that do not correspond to the selected layer.

[0153]

[0181] The inter-image alignment 2200 for a selected layer can be created based on multiple images matched to a template for the selected layer. As an example, a template for a second feature layer is used to generate the inter-image alignment 2200. For simplicity, the inter-image alignment shows only the features 2210 for the selected layer. The inter-image alignment 2200 may further include information about the occurrence probability, mean, variance, probability, etc., of the features 2210 for the selected layer. In this example, the average intensity illumination or occurrence probability of the features 2210 is shown, with white areas 2211 representing a low probability that the feature 2210 is present, grey areas 2212 representing a medium probability that the feature 2210 is present, and black areas 2213 representing a high probability that the feature is present. After a template (for a layer or feature) is matched to an image, pixels of the image can be marked as corresponding to the features of the template or as not corresponding to the features of the template. For example, pixels within the area of ​​the template feature can be marked as occurring (e.g., marked as a value of "1" on the occurrence scale or layer), while pixels not within the area of ​​the template feature can be marked as not occurring (e.g., marked as a value of "0" on the occurrence scale or layer). By summing the occurrence values ​​of multiple images after alignment between the images, an occurrence probability map can be generated. The occurrence probability can be used for multiple images even if the imaged image or area is unstable (e.g., in terms of brightness, thickness, etc.) or experiences process variations. For stable images with well-controlled image and process parameters, the average intensity illuminance can be used instead of or in addition to the occurrence probability. In some cases, the occurrence probability can be compared to or used together with the average intensity illuminance, including for purposes of determining image and process stability.

[0154]

[0182] The average intensity illumination or occurrence probability measures the probability of the feature and can be used to control lithography and other processes. The intensity illumination or occurrence probability of feature 2210 is plotted along y-axis 2224 as a function of distance from the center of feature 2210 along x-axis 2222 in graph 2220. Curve 2226 represents the average shape profile of feature 2210 and can be used to calculate average feature size 2228, standard deviation 2230 of feature sizes, etc. The distribution of sizes of feature 2210 can be used to determine probability limits for feature size control and to detect process drift, process limits, etc.

[0155]

[0183] Based on the inter-image alignment for the selected layer (e.g., inter-image alignment 2200), further inter-image alignments can be determined for features in layers other than the selected layer. Once the images are aligned based on template matching of the selected layer, other non-selected layers can also be located by template matching, including using one or more weight maps. Based on the matching templates of the non-selected layers, features of the non-selected layers can be superimposed to determine average location, intensity illumination, occurrence probability, etc. A second inter-image alignment 2240 is shown in which features of the non-selected layers are shown. Since the images are not aligned based on template matching of the non-selected layers, the second inter-image alignment also includes information about the relative displacement between the selected and non-selected layers. The second inter-image alignment may also include information about the mean, variance, and probability of features on the non-selected layers. An intensity illumination map 2250 shows the average intensity illumination of the non-selected features of the second inter-image alignment 2240. The black areas 2252 correspond to metal vias in the multi-layer structure, while the gray areas 2253 correspond to features in a first feature layer of the multi-layer structure. The solid intensity illuminance represents the average intensity illuminance or occurrence probability of the features. The average intensity illuminance or occurrence probability measures the probability of the features and can be used to control lithography and other processes. The intensity illuminance or occurrence probability of the features in the black areas 2252 is plotted along the y-axis 2282 as a function of distance from the center of the feature in the first feature layer along the x-axis 2280 in the graph 2272. Curve 2294 represents the average shape profile of the features in the first feature layer, which can be used to calculate the average feature size 2296, the standard deviation 2298 of the feature sizes, etc. The distribution of the sizes of the features in the first feature layer can be used to determine probability limits for feature size control for the first feature layer, and to detect process drift, process limits, etc. The intensity illumination or occurrence probability of features in the grey area 2253 is plotted along the y-axis 2283 as a function of distance from the center of the metal via feature along the x-axis 2280 in the graph 2273 .Curve 2288 represents the average geometric profile of the metal via features, and can be used to calculate the average feature size 2290, the standard deviation of the feature sizes 2292, etc. The distribution of sizes of the features in the first feature layer can be used to determine probability limits for feature size control for the first feature layer, and to detect process drift, process limitations, etc.

[0156]

[0184] FIG. 23 shows a schematic representation of template matching based on unit cells. FIG. 23 shows an acquired image 2300 for a periodic multi-layer structure. For ease of explanation, the periodic multi-layer structure of acquired image 2300 can be considered as a repeating pattern of acquired image 1870 of FIG. 18D. Acquired image 2300 shows black areas 2310 corresponding to non-patterned areas of the structure, white areas 2320 corresponding to metal vias of the structure, and grey areas 2330 corresponding to other features of patterned areas of the structure. Grey areas 2330 can represent features from multiple feature layers. Template matching can be performed on acquired image 2300. Template matching can be performed based on a composite template composed of multiple templates. For example, template matching can be performed on any of the layers of the multi-layer structure based on templates corresponding to features and layers of areas 2341-2345. For example, templates 1880, 1884, and 1888 of Figures 18E-18G can be used to locate features of corresponding layers in each of areas 2341-2345. The templates used do not need to be the same size or contain the same number of features for each of the layers. Additionally, the composite template can include information regarding the relative location of each of the templates. For example, in a periodic structure, the composite template can include information regarding the dimensions of the repeats and the expected variation in repeat size. In template matching, an initial template can be matched to an initial location, such as area 2341, and additional templates including the composite template can be matched to additional areas, such as areas 2342-2345, that are located based on repeat size. For example, area 2341 can be located based on moving area 2340 four pattern repeats to the right and moving area 2340 three pattern repeats up. Areas 2340-2345 can be distributed throughout acquired image 2300.For example, a central area may be selected (such as area 2340) and areas closer to the edges of acquired image 2300 may be selected (such as areas 2341-2345). In some embodiments, areas may be selected based on a set number of iterations, but if the selected area is not clear enough for template matching or contains any other type of defect (as shown for area 2345), another area may be selected to compose the composite template. For example, instead of area 2345, area 2346 adjacent to area 2345 may be selected. The composite template need not be symmetric. Using multiple templates to compose the composite template may improve the accuracy of template matching while reducing the need for computation that would be present if the template contained many or nearly all features of the image.

[0157]

[0185] FIG. 24 is a diagram of an exemplary computer system CS that can be used for one or more of the operations described herein. The computer system CS includes a bus BS or other communication mechanism for communicating information and a processor PRO (or multiple processors) coupled to the bus BS for processing information. The computer system CS also includes a main memory MM, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus BS for storing information and instructions to be executed by the processor PRO. The main memory MM may also be used to store temporary variables or other intermediate information during execution of instructions by the processor PRO. The computer system CS further includes a read-only memory (ROM) ROM or other static storage device coupled to the bus BS for storing static information and instructions for the processor PRO. A storage device SD, such as a magnetic or optical disk, is provided and coupled to the bus BS for storing information and instructions.

[0158]

[0186] The computer system CS may be coupled via a bus BS to a display DS, such as a cathode ray tube (CRT), or flat panel, or touch panel display, for displaying information to a computer user. An input device ID, including alphanumeric and other keys, is coupled to the bus BS for communicating information and command selections to the processor PRO. Another type of user input device is a cursor control CC, such as a mouse, trackball, or cursor direction keys, for communicating directional information and command selections to the processor PRO and for controlling cursor movement on the display DS. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify a position in a plane. A touch panel (screen) display may also be used as an input device.

[0159]

[0187] In some embodiments, parts of one or more methods described herein may be executed by the computer system CS in response to the processor PRO executing one or more sequences of one or more instructions contained in the main memory MM. Such instructions may be read into the main memory MM from another computer-readable medium, such as a storage device SD. Execution of the sequences of instructions contained in the main memory MM causes the processor PRO to perform the process steps (operations) described herein. One or more processors in a multiprocessing arrangement may also be used to execute the sequences of instructions contained in the main memory MM. In some embodiments, hardwired circuitry may be used in place of or in combination with software instructions. Thus, the description herein is not limited to any specific combination of hardware circuitry and software.

[0160]

[0188] As used herein, the terms "computer-readable medium" and / or "machine-readable medium" refer to any medium that participates in providing instructions to a processor PRO for execution. Such media include, but are not limited to, The medium may take many forms, including non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as the storage device SD. Volatile media include dynamic memory, such as the main memory MM. Transmission media include coaxial cables, copper wire, and optical fibers, including the wires that make up the bus BS. Transmission media may also take the form of sound or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Computer-readable media may be non-transitory, such as, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, a punch card, a paper tape, any other physical medium with a pattern of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge. Non-transitory computer-readable media may have instructions recorded thereon. The instructions, when executed by a computer, may implement any of the operations described herein. Transient computer-readable media may include, for example, a carrier wave or other propagating electromagnetic signal.

[0161]

[0189] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor PRO for execution. For example, the instructions may initially be stored on a magnetic disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system CS may receive the data on the telephone line and convert the data to an infrared signal using an infrared transmitter. An infrared detector coupled to the bus BS may receive the data carried in the infrared signal and place the data on the bus BS. The bus BS carries the data to the main memory MM, from which the processor PRO retrieves and executes the instructions. The instructions received by the main memory MM may optionally be stored on a storage device SD before or after execution by the processor PRO.

[0162]

[0190] The computer system CS may also include a communication interface CI coupled to the bus BS. The communication interface CI provides a bidirectional data communication coupling with a network link NDL connected to a local network LAN. For example, the communication interface CI may be an Integrated Services Digital Network (ISDN) card or a modem for providing a data communication connection to a corresponding type of telephone line. As another example, the communication interface CI may be a Local Area Network (LAN) card for providing a data communication connection to a compatible LAN. A wireless link may also be implemented. In any such implementation, the communication interface CI sends and receives electrical, electromagnetic or optical signals carrying digital data streams representing various types of information.

[0163]

[0191] The network link NDL typically provides data communication by way of one or more networks to other data devices. For example, the network link NDL may provide a connection to the host computer HC by way of a local network LAN. This may include the data communication services provided by the worldwide packet data communication network now commonly referred to as the "Internet" INT. The local network LAN (Internet) may use electrical, electromagnetic or optical signals carrying digital data streams. The signals by the various networks and the signals on the network data link NDL and by the communication interface CI carrying digital data to and from the computer system CS are exemplary forms of carrier waves carrying information.

[0164]

[0192] The computer system CS can send messages and receive data including program code over the network, the network data link NDL and the communication interface CI. In the Internet example, the host computer HC can transmit requested code for an application program over the Internet INT, the network data link NDL, the local network LAN and the communication interface CI. One such downloaded application can provide, for example, all or part of the methods described herein. The received code can be executed by the processor PRO upon receipt and / or stored in the storage device SD or other non-volatile storage for later execution. In this way, the computer system CS can obtain the application code in the form of a carrier wave.

[0165]

[0193] As discussed above with respect to Figures 7-11, template matching can be used in determining the overlay between features of various layers. For example, a first position of a feature in a first layer of an image and a second position of a second feature in a second layer of an image can be determined using template matching. Based on a first offset associated with the first feature (e.g., offset 720 - the determined positional deviation of the first feature from the reference position of the first feature) and a second offset associated with the second feature (e.g., offset 730 - the determined positional deviation of the second feature from the reference position of the second feature), an overlay (e.g., overlay 740) between the first feature and the second feature can be measured.

[0166]

[0194] In conventional template matching, a fixed size template may be used. There may be several drawbacks associated with using a fixed size template. In some embodiments, CD variations due to the patterning process (e.g., global CD variations (die-to-die) and local CD variations (intra-die)) may bias the template matching results (e.g., feature locations) depending on the difference between the template size and the actual size of the features. Differences in the measured location relative to the actual location of the features may lead to overlay measurement errors. For example, a small size template (e.g., template size smaller than the actual size of the features) may result in overestimated overlay, and a large size template (e.g., template size larger than the actual size of the features) may result in underestimated overlay. These and other drawbacks exist.

[0167]

[0195] Embodiments are disclosed for selecting an optimal size of template to minimize errors in determining a parameter of interest (e.g., overlay) using template matching. In some embodiments, templates of various sizes are generated for a feature in an image (e.g., a via layer feature in an SEM image). Template matching can be performed for each of the template sizes, and a performance metric associated with the template matching for the corresponding template size is determined. A particular template size can then be selected based on the performance metric value. The selected template size can be used in template matching to determine the location of the feature in the image, which can be further used in various applications including determining a measure of overlay with other features. In some embodiments, the performance metric can include a similarity metric (e.g., as described above) that indicates the similarity between the feature in the image and the template. For example, the similarity metric can include a normalized squared difference between the template and the image. By dynamically selecting a template size for template matching, the difference between the measured location of the feature and the actual location is minimized, which minimizes any errors in determining the location of the feature in the image using template matching, thereby improving the accuracy in determining the parameter of interest (e.g., overlay).

[0168]

[0196] The following paragraphs, with respect to at least FIGS. 25A-25C and 26, discuss selecting a template of a particular size for template matching.

[0169]

[0197] Figures 25A and 25B show block diagrams for selecting a template size from a library of template sizes for template matching, according to various embodiments. Figure 25C shows a graph of performance index values ​​for various template sizes, according to various embodiments. Figure 26 is a flow diagram of a method 2600 for selecting a template size from a library of template sizes for template matching, consistent with various embodiments.

[0170]

[0198] In process P2605, an image 2505 is obtained. The image 2505 may include information about features of the pattern. The image 2505 may be a test image and may be acquired by optical or other electromagnetic imaging or SEM, or may be obtained from other software or data storage. The image 2505 includes features such as a first feature 2510 and a second feature 2515. As discussed above, the features may be from the same layer or different layers of multiple process layers of fabrication. For example, the first feature 2510 may be on a first layer and the second feature 2515 may be on a second layer. In some embodiments, the first feature 2510 may be a feature on a via layer.

[0171]

[0199] In process P2610, a library of templates 2501 is obtained having templates of various sizes corresponding to features. For example, templates 2501a-2501e of various sizes corresponding to a first feature 2510 are obtained. In some embodiments, if the first feature 2510 is in the shape of a circle, the templates 2501a-2501e may be of different radii. The templates 2501a-2501e may be generated using any of several methods described above. In some embodiments, the templates may be associated with a "hot spot" or reference point 2512 that may be used in determining offsets relative to features of other templates, patterns, or images (e.g., using template matching as described above with respect to at least FIGS. 7-11 above). The reference point 2512 may be determined in any number of ways. In some embodiments, the reference point 2512 may be located at a user-defined location within the template. In some embodiments, the reference point 2512 may be the centroid of the shape of the template 2501. For example, if a first template 2501a is generated for a first feature 2510 that is in the shape of a circle, then the reference point 2512a of the first template 2501a is the centroid of the circle, i.e., the center of the circle. Similarly, the other templates 2501b-2501e may also be associated with reference points 2512b-2512e, respectively. In some embodiments, the reference points of the features in the image 2505 may be determined in a similar manner. It should be noted that the shape of the feature is not limited to a circle, nor the reference location to the centroid of the shape.

[0172]

[0200] In some embodiments, the template size affects the accuracy of determining the location of a feature in the image 2505. For example, if the template size used in determining the location of a first feature 2510 in the image 2505 is smaller than the size of the first feature 2510 (e.g., template matching 2501c), the template matching may determine that the reference point 2511 of the first feature 2510 is located at the measurement location 2532 when in fact the reference point 2511 is located at the actual location 2531 in the image 2505. The measurement location 2532 may be determined based on the location of the reference point 2512c in the template 2501c. The difference between the measurement location 2532 and the actual location 2531 may result in an overestimated measurement value. Similarly, if a template size used in determining the location of a first feature 2510 in an image 2505 is larger than the size of the first feature 2510 (e.g., template 2501e), template matching may determine that the fiducial point 2511 of the first feature 2510 is located at a measurement location 2533 when in fact the fiducial point 2511 is located at an actual location 2531 in the image 2505. The measurement location 2533 may be determined based on the location of the fiducial point 2512e in the template 2501e. The difference between the measurement location 2533 and the actual location 2531 may result in an underestimated measurement of overlay. In some embodiments, the method 2600 may determine the template size such that the difference between the measured location and the actual location of the feature (e.g., the difference between the actual location and the measured location of a fiducial point associated with the feature) is zero or is minimized. Such a template size may minimize any errors in determining the location of a feature in an image using template matching, thereby improving accuracy in determining the parameter of interest (e.g., overlay).

[0173]

[0201] In process P2615, a template of a particular size is selected from the library of templates 2501 and compared to the image using template matching to determine the location of the feature in the image. For example, template matching may be performed using a first template 2501a from the library of templates 2501 to determine the location of a first feature 2510 in the image 2505. In some embodiments, the template matching method described above with respect to at least Figures 7-11 may be used. The template matching may determine the location of the first feature 2510 in the image 2505 and a similarity index, where the similarity index indicates the degree of match between the first template 2501a and the first feature 2510.

[0174]

[0202] Process P2620 determines a value of a performance metric associated with template matching. A performance metric may be any characteristic that indicates or represents the degree of match between a feature in an image and the template. In some embodiments, the performance metric may include a similarity metric (e.g., as described above) that indicates the similarity between a feature in an image and the template. For example, the similarity metric may be the normalized squared difference between the template and the image.

[0175]

[0203] Processes P2615 and P2620 can be repeated for all or some of the template sizes in the library of templates 2501, and performance index values ​​2560 can be obtained for the various template sizes. Graph 2575 of Figure 25C shows example performance index values ​​2560 (represented by y-axis 2550) for various template sizes (represented by x-axis 2555). Graph 2580 shows performance index values ​​2590, such as a similarity index (represented by y-axis 2570), for various template sizes (represented by x-axis 2555).

[0176]

[0204] In process P2625, a template size is selected based on a performance index that meets a specified criterion. In some embodiments, the specified criterion may indicate that the template size associated with the highest performance index value may be selected. For example, as shown in graph 2575, performance index value 2561 may be determined as the highest value among values ​​2560, and thus template size 2565 associated with performance index value 2561 is selected. In some embodiments, the specified criterion may indicate that the template size associated with the lowest performance index value may be selected. For example, as shown in graph 2580, similarity index value 2562 may be determined as the lowest value among values ​​2590, and thus template size 2566 associated with similarity index value 2562 is selected.

[0177]

[0205] After the template size is selected, the selected template size can be used in template matching to determine various parameters of interest, which can include, for example, one or more of CD, CD uniformity, an overlay measure, an overlay uniformity measure, an overlay error measure, a probability measure, an EPE measure, an EPE uniformity measure, an EPE probability measure, or a defect measure.

[0178]

[0206] Further embodiments according to the present invention are described in the following numbered clauses: 1. A method comprising: accessing an image including information from multiple process layers; accessing image templates for the multiple process layers; accessing a weight map for the image templates; and matching the image templates to locations on the image based at least in part on the weight map. 2. The method of claim 1, wherein the image template includes an image template for a first layer of the plurality of process layers. 3. The method of claim 1, wherein matching the image template includes comparing the image template to a plurality of locations on the image, adapting a weight map for the given location, and comparing the image template to the given location based at least in part on the adapted weight map for the given location, and further comprising: comparing; and matching the image template to a location based on the comparison. 4. The method of claim 3, wherein adapting the weight map for the given location further includes updating the weight map for the given location based on at least one of pixel values ​​of the image, blocking structures on the image, previously identified structures located on the image, a position of the image template, a relative position of the image template with respect to the image, or a combination thereof. 5. The method of claim 3, wherein adapting the weight map includes adapting the weight map based on a relative position between the image template and the image. 6. The method of claim 3, further comprising accessing a weight map for the image template and accessing a weight map for the image, and adapting the weight map for the given location comprises adapting the weight map for the given location based on multiplication of the weight map for the image template and the weight map for the image. 7. The method of claim 3, wherein adapting the weight map includes modifying values ​​of the weight map based on the image at the given location. 8. The method of clause 3, wherein the weight map is based on the shape of the image template. 9. The method of claim 3, wherein comparing the image template to a plurality of locations further includes determining a similarity metric of the image template at a plurality of locations on the image, the similarity metric being determined at least in part based on an adaptive weight map for the given location, and matching the image template to a location on the image based at least in part on the similarity metric of the plurality of locations. 10. The method of claim 9, wherein determining the similarity index includes determining, for a given location of the image template on the image, a measure of matching between pixel values ​​of the image template and pixel values ​​of the image, wherein the measure of matching for a given pixel is based at least in part on the value of the adaptive weight map at the given pixel, and determining the similarity index based at least in part on a sum of the measures of matching for pixels encompassed by the image template. 11. The method of claim 9, wherein the similarity measure is at least one of normalized cross-correlation, cross-correlation, normalized correlation coefficient, correlation coefficient, normalized difference, difference, normalized sum of differences, sum of differences, correlation, normalized correlation, normalized squared difference, squared difference, or a combination thereof. 12. The method of claim 9, wherein the similarity metric is user-defined. 13. The method of claim 9, wherein the similarity metric varies for different regions of the image template or image. 14. The method of claim 1, further comprising determining a measure of offset based at least in part on a relationship between a given point on the image and an additional point on the image template that is matched to a position on the image. 15. The method of claim 14, wherein the measure of offset is an overlay value. 16. The method of claim 14, wherein the measure of offset is a deviation from a reference position, such that a given point on the image and an additional point on the image template have an expected separation. 17. The method of claim 1, further comprising matching the second occurrence of the image template to a location on the image based at least in part on the weight map, the weight map being adapted independently for matching the image template and for matching the second occurrence of the image template. 18. The method of clause 1, further comprising: accessing an additional image template; accessing an additional weight map for the additional image template; and matching the additional image template to an additional location on the image based at least in part on the additional weight map. 19. The method of claim 18, wherein the additional image template is substantially similar to the image template. 20. The method of claim 18, wherein the additional image template and the image template are different. 21. The method of claim 18, wherein the image template and the additional image template include an image template for a first layer of a plurality of process layers. 22. The method of claim 18, wherein the image template includes an image template for a first layer of the plurality of process layers, and the additional image template includes an image template for a second layer of the plurality of process layers. 23. The method of clause 18, wherein matching the additional image template includes comparing the additional image template to a plurality of locations on the image, adapting an additional weight map for the given location, and comparing the additional image template to the given location based at least in part on the additional adaptive weight map for the given location, and further comprising: matching the additional image template to the location based on the comparison. 24. The method of clause 18, further comprising determining a measure of the offset based at least in part on a relationship between a given point on the image template matched to a position on the image and an additional point on an additional image template matched to an additional position on the image. 25. The method of claim 18, further comprising determining a plurality of offset measures between a plurality of image templates matched to a position on the image, the plurality of image templates being matched based at least in part on their respective weight maps. 26. The method of claim 1, wherein the image includes at least blocked and non-blocked areas, and the weight map weights blocked areas less than non-blocked areas. 27. The method of claim 26, wherein the image further includes at least partial block areas, and the weight map weights the partial block areas less than the non-block areas, and the weight map weights the blocked areas less than the partial block areas. 28. The method of clause 1, wherein matching the image template further includes matching at least one of a scale of a first dimension of the image template, a scale of a second dimension of the image template, a rotation angle of the image template, or a combination thereof, to the image based at least in part on the weight map. 29. The method of clause 28, wherein matching the image template further includes updating a weight map based on at least one of a scale of a first dimension of the image template, a scale of a second dimension of the image template, a rotation angle of the image template, or a combination thereof, and matching the image template to a position on the image based at least in part on the updated weight map. 30. The method of claim 1, wherein matching the image template further comprises matching a polarity of the image template to the image. 31. The method of clause 30, wherein matching the image template further includes updating a weight map based on a polarity of the image template, and matching the image template to a location on the image based at least in part on the updated weight map. 32. The method of clause 1, wherein accessing the weight map includes determining a weight map for the image of the measurement structure based at least in part on pixel values ​​of the image of the measurement structure. 33. The method of claim 1, further comprising accessing an image weight map for the image, and wherein matching the image template comprises matching the image template based at least in part on multiplication of the image weight map and a weight map for the image template. 34. The method of claim 1, wherein the image includes a plurality of pixels having pixel values, the image template includes a plurality of pixels having pixel values ​​that may be the same as or different from the plurality of pixels of the image, and the weight map includes weight values ​​corresponding to pixels of either the image or the image template. 35. The method of claim 34, wherein the weight values ​​in the weight map are determined based on pixel position. 36. The method of claim 34, wherein the weight values ​​in the weight map are based on distance from features in the image template. 37. The method of claim 34, wherein the weights in the weight map are user-defined. 38. A method including: accessing an image including information from multiple process layers; accessing a composite template for the multiple process layers; accessing a weight map for the composite template, the weight map including at least a first area having a low relative priority; and matching the composite template to a location on the image based at least in part on the weight map. 39. The method of claim 38, wherein the composite template includes a composite template for a first tier of the multiple process tiers. 40. The method of clause 38, wherein matching the composite template further comprises comparing the composite template to a plurality of locations on the image and matching the composite template to a location based on the comparison. 41. The method of clause 38, wherein matching the composite template includes comparing the composite template to a plurality of locations on the image, adapting a weight map for the given location, and comparing the composite template to the given location based at least in part on the adapted weight map for the given location, and further comprising: matching the composite template to a location based on the comparison. 42. The method of clause 38, further comprising determining a measure of offset based at least in part on a relationship between a given point on the image and an additional point on the composite template that is matched to a position on the image. 43. The method of clause 42, wherein the additional points on the composite template correspond to at least a first area of ​​low relative priority. 44. The method of clause 38, further comprising instructions for accessing an additional composite template and accessing an additional weight map for the additional composite template, the additional weight map including at least a first area of ​​lower relative priority, and matching the additional composite template to an additional location on the image based at least in part on the additional weight map, the composite template including at least two image templates and a spatial relationship between the at least two image templates. 45. The method of claim 44, further comprising determining a measure of offset based at least in part on a relationship between a given point on the composite template matched to a position on the image and an additional point on an additional composite template matched to an additional position on the image. 46. ​​A method comprising: generating an image template for a multi-layer structure based at least in part on a composite image of the multi-layer structure; and matching the image template to a location on a test image of the multi-layer structure. 47. The method of clause 46, wherein generating the image template further includes selecting a first artifact of the composite image and generating the image template based at least in part on the first artifact. 48. The method of claim 47, wherein the first artifact corresponds to a physical feature of a multi-layer structure. 49. The method of claim 48, wherein the first artifact corresponds to a physical feature of a first layer of a multi-layer structure. 50. The method of clause 47, wherein the first artifact corresponds to a metrology tool induced artifact. 51. The method of claim 47, wherein the image template is generated based on a plurality of composite images of the first artifact. 52. The method according to clause 51, wherein at least one composite image is obtained from a scanning electron microscope model. 53. The method of claim 51, wherein at least one composite image is obtained from a lithographic model. 54. The method of claim 51, wherein at least one composite image is obtained from an etched model. 55. The method of claim 51, wherein at least one composite image is generated from the GDS geometry. 56. The method of claim 47, wherein selecting a first artifact in the composite image further comprises selecting the first artifact based on at least one of artifact size, artifact contrast, artifact process stability, artifact intensity log slope, or a combination thereof. 57. The method of claim 46, wherein the image template is a contour. 58. The method of clause 46, wherein generating the image template further includes generating a weight map for the image template, and matching the image template to a position on the multi-layered test image further includes matching the image template to a position on the multi-layered test image based at least in part on the weight map. 59. The method of claim 58, wherein generating the weight map further includes generating the weight map based on at least one of a size of the artifact, a contrast of the artifact, a process stability of the artifact, an intensity log-slope of the artifact, or a combination thereof. 60. The method of claim 46, wherein generating an image template further includes generating pixel values ​​for the image template, and matching the image template to a location on the multi-layered test image further includes matching the image template to a location on the multi-layered test image based at least in part on the pixel values. 61. The method of clause 46, further comprising generating at least a second image template for the multi-layer structure based at least in part on the composite image of the multi-layer structure, and matching the at least second image template to a location on a test image of the multi-layer structure. 62. The method of claim 61, wherein the second image template corresponds to a layer of the same multi-layer structure as the image template. 63. The method of clause 61, wherein the second image template corresponds to a different layer of the multi-layer structure than the image template. 64. The method of claim 61, further comprising determining a measure of the offset based at least in part on a position on an image template matched to the test image of the multi-layer structure and a second position on a second image template matched to the test image of the multi-layer structure. 65. The method of claim 64, wherein the measure of the offset is an overlay value. 66. The method of clause 61, further comprising determining a measure of edge placement error based at least in part on a location on an image template matched to the test image of the multi-layer structure and a second location on a second image template matched to the test image of the multi-layer structure. 67. A method comprising: selecting at least two artifacts in a multi-layered image; determining a first spatial relationship between the at least two artifacts in the multi-layered image; generating an image template based at least in part on the at least two artifacts and the first spatial relationship; and matching the image template to a location on a multi-layered test image. 68. The method of claim 67, wherein selecting the at least two artifacts includes selecting the at least two artifacts based on at least one of artifact size, artifact contrast, artifact process stability, artifact intensity log slope, or a combination thereof. 69. The method of clause 67, wherein selecting at least two artifacts includes selecting at least two artifacts by using a grouping algorithm. 70. The method of clause 67, wherein selecting at least two artifacts includes selecting at least two artifacts based on a lithographic model. 71. The method of clause 67, wherein selecting at least two artifacts includes selecting the at least two artifacts based on a process model. 72. The method of clause 67, wherein selecting at least two artifacts includes selecting at least two artifacts based on a scanning electron microscope simulation model. 73. The method of clause 72, wherein selecting at least two artifacts based on the scanning electron microscope simulation model includes selecting at least two artifacts based on artifact contrast. 74. The method of clause 67, wherein generating the image template further includes generating one or more composite images based on models of the at least two artifacts, and generating the image template based on the one or more composite images. 75. The method of claim 67, wherein generating the image template further comprises refining the image template based on a scanning electron microscope image. 76. The method of claim 67, wherein the image template is spatially discontinuous. 77. The method of clause 67, wherein the image template is a composite template. 78. The method of claim 67, wherein the image template further includes a weight map, the weight map including a first highlighted area and a first non-highlighted area, the first highlighted area being weighted more heavily than the first non-highlighted area, and matching the image template to a location on the multi-layered test image includes matching the image template to a location based at least in part on the weight map. 79. The method of clause 78, wherein matching the image template to a location based at least in part on the weight map includes comparing the image template to a plurality of locations on a multi-layered test image, wherein the comparing includes adapting a weight map for the given location and comparing the image template to the given location based at least in part on the adapted weight map for the given location, and matching the image template to the location based on the comparison. 80. The method of claim 78, wherein the at least two artifacts correspond to emphasis areas. 81. The method of clause 67, further comprising determining a measure of offset based at least in part on a relationship between a first location on the test image of the multi-layer structure and a second location on the image template that is matched to the location on the test image of the multi-layer structure. 82. The method of clause 67, further comprising: selecting at least two additional artifacts of the multi-layered image; determining at least an additional spatial relationship between the at least two additional artifacts of the multi-layered image; generating an additional image template based at least in part on the at least two additional artifacts and the at least additional spatial relationship; and matching the additional image template to an additional location on the multi-layered test image. 83. The method of clause 82, further comprising determining a measure of the offset based at least in part on a relationship between a first position on an image template that is matched to a position on the test image of the multi-layer structure and an additional position on an additional image template that is matched to an additional position on the test image of the multi-layer structure. 84. The method of claim 83, wherein the measure of the offset is an overlay value. 85. A method comprising: accessing an image including information from a plurality of process layers; accessing a template for a first layer of the plurality of process layers; and determining locations of features of the first layer on the image based on template matching of the template to the image, the template matching being based on a weight map indicative of blocking of the first layer by layers of the plurality of process layers other than the first layer. 86. The method of claim 85, wherein the first layer is a buried layer of a plurality of process layers. 87. The method of clause 86, including accessing at least one additional image containing information from a plurality of substantially similar process layers, and aligning the image and the at least one additional image based on positions of features of the first layer. 88. The method of claim 87, wherein aligning the image with the at least one additional image includes determining positions of substantially similar features of the first layer on the at least one additional image based on template matching of a template to the at least one additional image, and aligning the image with the at least one additional image based on positions of the features of the first layer on the image and positions of substantially similar features of the first layer on the at least one additional image. 89. The method of clause 87, comprising generating an inter-image alignment based on the image, at least one additional image, and an alignment of the image with the at least one additional image; and determining parameters of interest for the multiple process layers based on the inter-image alignment. 90. The method of claim 89, wherein the parameter of interest comprises a critical dimension, a uniformity of a critical dimension, a measure of overlay, a measure of uniformity of overlay, a measure of overlay error, a measure of probability, a measure of edge placement error, a measure of uniformity of edge placement error, a measure of probability of edge placement error, a defect measurement, or a combination thereof. 91. The method of clause 87, wherein aligning the image and the at least one additional image includes matching rotation, contrast, size, scale, or a combination thereof, of the image and the at least one additional image. 92. The method of clause 85, comprising accessing a pattern design including information corresponding to the first layer, and aligning the image and the pattern design based on positions of features of the first layer. 93. The method of claim 92, wherein aligning the image and the pattern design includes determining positions of substantially similar features of the first layer on the pattern design, and aligning the image and the pattern design based on the positions of the features of the first layer on the image and the positions of the substantially similar features of the first layer on the pattern design. 94. The method of clause 92, wherein the pattern design is based on a GDS design corresponding to the feature. 95. The method of clause 85, including accessing a second template for a second layer of the multiple process layers and determining a second location of a second feature of the second layer on the image based on template matching of the second template to the image, the template matching being based on a weight map indicative of blocking of the second layer by layers of the multiple process layers other than the second layer. 96. The method of clause 95, comprising determining a measure of overlay based on a position of a feature of the first layer on the image and a second position of a second feature of the second layer on the image. 97. The method of claim 85, wherein the image is at least one of a measured SEM image, a simulated SEM image, or a combination thereof. 98. The method of claim 85, wherein the template is generated based on a plurality of images of features of the first layer. 99. The method of claim 85, wherein the template is based on at least one of a process model, an imaging model, or a combination thereof. 100. The method of clause 85, wherein the template is a synthetic template generated based on at least one GDS design from at least one of the multiple process layers. 101. The method of claim 85, wherein the template for the first layer includes a plurality of templates for the first layer, and determining the position of the feature on the first layer further includes determining the position of the plurality of features on the image based on template matching of the plurality of templates to the image. 102. The method of clause 101, wherein the plurality of templates are spaced a known distance apart, and determining the positions of the plurality of features includes determining the positions of the plurality of features approximately spaced a known distance apart. 103. The method of clause 102, wherein the plurality of templates correspond to at least one unit cell of the plurality of process layers, and the known distance is a multiple of a pitch of at least one of the plurality of process layers. 104. The method of clause 101, wherein the plurality of templates are substantially similar and the plurality of features are substantially similar. 105. The method of clause 101, wherein the plurality of templates are different and the plurality of features are substantially similar or different or a combination thereof. 106. The method of clause 85, wherein the weight map is an adaptive weight map. 107. The method of clause 106, wherein the weight map is adapted based on pixel values ​​of the image. 108. The method of clause 85, further comprising segmenting the image based on the location of the template on the image. 109. The method of clause 108, further comprising: accessing a second template for a second layer of the plurality of process layers; determining a second location of a second feature of the second layer on the image based on template matching of the second template to the image; and segmenting the image based on the location of the feature of the first layer of the image and the second location of the second feature of the second layer of the image. 110. The method of clause 85, further comprising locating a region of interest in the image based on a location of the template on the image, and selecting the region of interest from the image. 111. The method of clause 110, further comprising performing image enhancement on a region of interest of the image. 112. The method of clause 111, wherein the image enhancement includes at least one of contrast adjustment, image denoising, image smoothing, tone level adjustment, or a combination thereof. 113. The method of clause 110, further comprising performing at least one of edge detection, edge extraction, contour detection, contour extraction, shape fitting, segmentation, template matching, or a combination thereof based on the region of interest of the image. 114. The method of clause 110, wherein locating the region of interest includes locating multiple regions of interest based on a position of the template on the image, and selecting the region of interest includes selecting multiple regions of interest. 115. The method of clause 110, wherein selecting a region of interest from the image includes masking regions of the image that are not within the region of interest. 116. The method of clause 110, wherein the region of interest at least partially comprises a feature of the first layer. 117. The method of clause 110, wherein the region of interest at least partially excludes features of the first layer. 118. A method including: accessing a plurality of images including information from one or more instances of a plurality of process layers; accessing a template for a first layer of the plurality of process layers; determining locations of features of the first layer on the plurality of images based on template matching of the template for the first layer to the plurality of images; and comparing the plurality of images based on locations of the features on the plurality of images. 119. The method of clause 118, further comprising evaluating at least one of the manufacturing process, the modeling process, or the metrology process based on the comparison. 120. The method of claim 119, wherein the evaluating includes determining an average, a measure of variance or both for an evaluation parameter, the evaluation parameter including at least one of a critical dimension, an average of a critical dimension, a uniformity of a critical dimension, a contour shape, a contour band, an average of a contour, a variance of a contour, a measure of uniformity of a feature, a measure of probability or a combination thereof. 121. The method of clause 118, further comprising identifying a non-ideality in at least one of the plurality of images based on comparing the plurality of images, the non-ideality comprising defects, overlay offsets, critical dimension deviations, contour deviations, edge placement errors, intensity illumination deviations, or combinations thereof. 122. A method including: accessing an image including information from a plurality of process layers; accessing a template for a first layer of the plurality of process layers; determining locations of features of the first layer on the image based on template matching of the template to the image, the template matching being based on a weight map indicative of blocking of the first layer by layers of the plurality of process layers other than the first layer; and identifying regions of the image corresponding to the first layer, regions of the image not corresponding to the first layer, or both based on the locations of the features of the first layer. 123. The method of clause 122, comprising: accessing a second template for a second layer of the plurality of process layers; and determining a second location of a second feature of the second layer on the image based on template matching of the template to the image, the template matching being based on a weight map indicative of blocking of the second layer by layers of the plurality of process layers other than the second layer; and identifying at least a second region of the image corresponding to the second layer, a region of the image not corresponding to the second layer, a region of the image not corresponding to the first layer or the second layer, a region of the image corresponding to the first layer and the second layer, or a combination thereof, based on the location of the feature of the first layer and the second location of the second feature of the second layer. 124. The method of clause 123, wherein determining a second position of the second feature includes determining a preliminary position of the second feature of the second layer on the image based on a position of the feature of the first layer on the image and a spatial relationship between the feature and the second feature, and identifying a second position of the second feature of the second layer on the image based on the preliminary position and template matching. 125. The method of clause 122, comprising performing image enhancement on a region of the image that corresponds to the first layer or a region of the image that does not correspond to the first layer. 126. The method of clause 1, wherein matching the image template includes accessing a plurality of image templates having different sizes and selecting as the image template one of the plurality of image templates associated with a performance metric that meets a specified criterion. 127. The method of clause 126, further comprising comparing the image template to the image in a template matching technique to determine the location of features in the image. 128. The method of clause 127, wherein the feature is on a first layer of a plurality of process layers. 129. The method of clause 126, wherein selecting one of the image templates includes, for each of a plurality of image templates, comparing the image template to the image in a template matching technique to determine a location of a feature in the image, and determining a value of a performance index associated with the comparison. 130. The method of clause 129, further comprising selecting as the image template one of the image templates associated with a performance metric having a value that meets a specified criterion. 131. The method of clause 126, wherein the performance metric comprises a similarity metric that is a measure of matching between pixel values ​​of the image template and pixel values ​​of the image. 132. The method of clause 85, wherein accessing the template includes accessing a plurality of templates having different sizes and selecting as the template one of the plurality of templates associated with a performance metric that meets a specified criterion. 133. The method of clause 132, wherein selecting one of the templates includes, for each of a plurality of templates, comparing the template to the image in a template matching technique to determine a location of the feature, and determining a value of a performance index associated with the comparison. 134. The method of clause 133, further comprising selecting as the template one of the templates associated with a performance metric having a value that meets a specified criterion. 135. The method of clause 132, wherein the performance metric comprises a similarity metric that is a measure of matching between pixel values ​​of the template and pixel values ​​of the image. 136. A method of template matching, comprising: accessing a plurality of templates corresponding to features having various sizes; accessing an image containing the features; and selecting one of the plurality of templates associated with a performance metric that meets a specified criterion as a template for determining a location of the feature in the image using a template matching technique. 137. The method of clause 136, wherein selecting one of the templates includes, for each of a plurality of templates, comparing the template to the image in a template matching technique to determine a location of the feature, and determining a value of a performance index associated with the comparison. 138. The method of clause 137, further comprising selecting as the template one of the templates associated with a performance metric having a value that meets a specified criterion. 139. The method of clause 136, wherein the performance metric comprises a similarity metric that is a measure of matching between pixel values ​​of the template and pixel values ​​of the image. 140. The method of clause 136, wherein the image includes information from multiple process layers, and the feature is on a first layer of the multiple process layers. 141. The method of claim 140, wherein the template matching method is based on a weight map that indicates blocking of the first layer by layers of the plurality of process layers other than the first layer. 142. The method of clause 141, wherein the weight map is an adaptive weight map. 143. The method of clause 142, wherein the weight map is adapted based on pixel values ​​of the image. 144. The method of clause 140, further comprising: accessing a second template for a second layer of the plurality of process layers; and determining a second location of a second feature of the second layer on the image using the second template based on a template matching technique, the template matching technique being based on a weight map indicative of blocking of the second layer by layers of the plurality of process layers other than the second layer. 145. The method of clause 144, further comprising determining a measure of overlay based on a position of the feature of the first layer on the image and a second position of a second feature of the second layer on the image. 146. The method of clause 136, wherein the image is at least one of a measured SEM image, a simulated SEM image, or a combination thereof. 147. The method of clause 136, wherein the template is generated based on a plurality of images of features of the first layer. 148. The method of clause 136, wherein the template is based on at least one of a process model, an imaging model, or a combination thereof. 149. The method of clause 136, wherein the template is a synthetic template generated based on at least one GDS design from at least one of the multiple process layers. 150. One or more non-transitory machine-readable media having instructions thereon, the instructions being configured, when executed by a processor, to perform a method according to any of clauses 1 to 149. 151. A system including a processor and one or more non-transitory machine-readable media having instructions thereon, the instructions being configured, when executed by the processor, to perform a method according to any of clauses 1 to 149.

[0179]

[0207] Although the concepts disclosed herein may be used in manufacturing involving substrates such as silicon wafers, it should be understood that the disclosed concepts may be used with any type of manufacturing system (e.g., as used for manufacturing on substrates other than silicon wafers).

[0180]

[0208] In addition, combinations and subcombinations of the disclosed elements may constitute separate embodiments. For example, one or more of the operations described above may be included in separate embodiments or may be included together in the same embodiment.

[0181]

[0209] The above description is intended to be illustrative rather than limiting. Thus, it will be apparent to one skilled in the art that modifications such as those described may be made without departing from the scope of the appended claims.

Claims

1. Accessing an image containing information from multiple process layers; accessing image templates for the plurality of process layers; accessing a weight map for the image template; comparing the image with the image template by matching the image template to locations on the image based at least in part on the weight map according to a template matching process; The method includes:

2. The image templates include an image template for a first layer of the plurality of process layers, and matching the image templates includes: comparing the image template to the image at a plurality of locations, the comparing including adapting the weight map for a given location and comparing the image template to the given location based at least in part on the adapted weight map for the given location; matching the image template to a location based on the comparison; and The method of claim 1 further comprising:

3. 2. The method of claim 1 , wherein the comparing comprises adapting the weight map by updating the weight map for a given location based on at least one of pixel values ​​of the image, blocking structures on the image, previously identified structures located on the image, a position of the image template, a relative position of the image template with respect to the image, or combinations thereof.

4. accessing the weight map for the image template; accessing a weight map for the image; 4. The method of claim 3, further comprising: adapting the weight map for the given location based on a multiplication of the weight map for the image template and the weight map for the image.

5. Comparing the image template to a plurality of locations includes: determining a similarity metric of the image template at the plurality of locations on the image, the similarity metric being determined based at least in part on the adaptive weight map for the given location; matching the image template to the location on the image based at least in part on the similarity measures of the plurality of locations; The method of claim 3 further comprising:

6. 2. The method of claim 1, further comprising determining a measure of offset based at least in part on a relationship between a given point on the image and an additional point on the image template, the image template being matched to a position on the image, the measure of offset indicating an overlay value or deviation from a reference position, the given point on the image and the additional point on the image template having an expected separation.

7. 10. The method of claim 1, further comprising determining a plurality of offset measures between a plurality of image templates matched to a location on the image, the plurality of image templates being matched based at least in part on their respective weight maps.

8. The method of claim 1 , wherein the image includes at least blocked and non-blocked areas, and the weight map indicates a smaller weight in the blocked areas than in the non-blocked areas.

9. 9. The method of claim 8, wherein the image further comprises at least partial blocked areas, and the weight map weights the partial blocked areas less than the non-blocked areas, and the weight map weights the blocked areas less than the partial blocked areas.

10. 2. The method of claim 1 , wherein matching the image template further comprises matching at least one of a scale of a first dimension of the image template, a scale of a second dimension of the image template, a rotation angle of the image template, or a combination thereof to the image based at least in part on the weight map.

11. Matching the image template includes: updating the weight map based on at least one of the scale of the first dimension of the image template, the scale of the second dimension of the image template, the rotation angle of the image template, or a combination thereof; matching the image template to a location on the image based at least in part on the updated weight map; and The method of claim 10 further comprising:

12. Matching the image template includes updating the weight map based on a polarity of the image template; matching the image template to a location on the image based at least in part on the updated weight map; and The method of claim 1 further comprising:

13. The method of claim 1 , further comprising determining the weight map for the image of the measurement structure based at least in part on pixel values ​​of the image of the measurement structure.

14. Accessing an image weight map for the image.

2. The method of claim 1 , further comprising: matching the image template comprises matching the image template based at least in part on a multiplication of the image weight map and the weight map for the image template.

15. the image includes a plurality of pixels having pixel values; 2. The method of claim 1, wherein the image template includes a plurality of pixels having pixel values ​​that may be the same or different from the plurality of pixels of the image, and the weight map includes weight values ​​corresponding to pixels of either the image or the image template, the weight values ​​of the weight map being determined based on pixel position or distance from a feature in the image template.

16. One or more non-transitory machine-readable media having instructions thereon which, when executed by a processor, perform the method of any one of claims 1 to 15.

Images