Automatic selection of high-quality average SEM images from raw images using machine learning

Abstract

A method for evaluating an image of a printed pattern is implemented by at least one programmable processor. The method includes obtaining (2310, 2320) a first average image of the printed pattern, wherein the first average image is generated by averaging original images of the printed pattern. The method also includes identifying (2330) one or more features of the first average image. The method also includes executing, by the programmable processor, an image quality grading model and evaluating (2340) the first average image based at least on the one or more features. The evaluating includes determining (2350) by the image quality grading model whether the first average image meets a criterion.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application 62 / 764,664, filed on August 15, 2018, and the entire contents of said application are incorporated herein by reference. Technical Field

[0003] The description herein relates generally to metrology as used with process models for lithography processes, and more particularly to apparatus, methods, and computer program products for automatically selecting images acquired for performing high-quality metrology using machine learning algorithms. Background Art

[0004] Lithographic projection apparatus may be used, for example, in the manufacture of integrated circuits (ICs). In this case, a patterning device (e.g., a mask) may include or provide a pattern corresponding to a single layer of the IC (a "design layout"), and this pattern may be transferred to a target portion (e.g., including one or more dies) on a substrate (e.g., a silicon wafer) that has been coated with a layer of radiation-sensitive material ("resist") by methods such as irradiating the target portion through the pattern on the patterning device. Typically, a single substrate includes a plurality of adjacent target portions to which the pattern is transferred successively, one target portion at a time, by the lithographic projection apparatus. In one type of lithographic projection apparatus, the pattern on the entire patterning device is transferred to one target portion at a time; such an apparatus is typically referred to as a stepper. In an alternative apparatus (typically referred to as a step-and-scan apparatus), the projection beam is scanned over the patterning device in a given reference direction (the "scanning" direction) while synchronously moving the substrate in a direction parallel or antiparallel to the reference direction. Different portions of the pattern on the patterning device are gradually transferred to one target portion. Since typically a lithographic projection apparatus will have a reduction ratio M (eg 4), the rate F at which the substrate is moved will be 1 / M times the rate at which the projection beam scans the patterning device.More information on lithographic apparatus can be found, for example, in US 6,046,792, which is incorporated herein by reference.

[0005] Before the pattern is transferred from the pattern forming device to the substrate, the substrate may undergo various processes, such as primer coating, resist coating and soft baking. After exposure, the substrate may undergo other processes ("post-exposure processes"), such as post-exposure baking (PEB), development, hard baking and measurement / inspection of the transferred pattern. This series of processes is used as the basis for manufacturing a single layer of a device (e.g., IC). The substrate may then undergo various processes, such as etching, ion implantation (doping), metallization, oxidation, chemical mechanical polishing, etc., all of which are intended to ultimately complete a single layer of the device. If the device requires multiple layers, the entire process or its variation is repeated for each layer. Ultimately, the device will be set in each target portion on the substrate. These devices are then separated from each other by techniques such as slicing or cutting, whereby individual devices can be mounted on a carrier, connected to pins, etc.

[0006] Thus, manufacturing devices (such as semiconductor devices) typically involves using multiple manufacturing processes to process a substrate (e.g., a semiconductor wafer) to form various features and multiple layers of the device. These layers and features are typically manufactured and processed using, for example, deposition, lithography, etching, chemical mechanical polishing, and ion implantation. Multiple devices can be manufactured on multiple dies on a substrate and then separated into separate devices. This device manufacturing process can be considered a patterning process. The patterning process involves a patterning step, such as optical and / or nanoimprint lithography using a patterning device in a lithographic apparatus to transfer the pattern on the patterning device to the substrate, and typically but optionally involves one or more related pattern processing steps, such as resist development by a developing apparatus, substrate baking using a baking tool, etching using a pattern using an etching apparatus, etc.

[0007] As mentioned, photolithography is a core step in the manufacture of devices such as ICs, where patterns formed on a substrate define the functional elements of the device, such as microprocessors, memory chips, etc. Similar photolithography techniques are also used to form flat panel displays, microelectromechanical systems (MEMS), and other devices.

[0008] As semiconductor manufacturing processes continue to advance, over the decades, the size of functional elements has continued to decrease while the number of functional elements (such as transistors) per device has steadily increased, following a trend commonly referred to as "Moore's Law." In the current state of the art, multiple layers of a device are fabricated using a lithography projection apparatus that projects a design layout onto a substrate using illumination from a deep ultraviolet illumination source, thereby forming a single functional element having a size well below 100 nm (i.e., less than half the wavelength of the radiation from the illumination source (e.g., a 193 nm illumination source)).

[0009] This process in which features with dimensions smaller than the classical resolution limit of the lithographic projection apparatus are printed is generally referred to as low-k1 lithography, and is based on the resolution formula CD=k1×λ / NA, where λ is the wavelength of the radiation employed (currently 248 nm or 193 nm in most cases), NA is the numerical aperture of the projection optics in the lithographic projection apparatus, CD is the "critical dimension" (usually the smallest feature size printed), and k1 is an empirical resolution factor. In general, the smaller k1 is, the more difficult it becomes to reproduce a pattern on a substrate that resembles the shape and dimensions planned by the designer to achieve specific electrical functionality and performance. To overcome these difficulties, complex 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 phase-shifting patterning devices, optical proximity correction (OPC, sometimes also called "optical and process correction") in the design layout, or other methods generally defined as "resolution enhancement techniques" (RET).

[0010] The term "projection optical element" as used herein should be broadly interpreted to cover various types of optical systems, including, for example, refractive optics, reflective optics, apertures, and reflective-refractive optics. The term "projection optical element" may also include components that operate according to any of these design types for guiding, shaping, or controlling a projection radiation beam, either collectively or individually. The term "projection optical element" may include any optical component in a lithographic projection apparatus, regardless of where the optical component is located on the optical path of the lithographic projection apparatus. Projection optical elements may include optical components for shaping, adjusting, and / or projecting radiation from a source before the radiation passes through a pattern forming device, or optical components for shaping, adjusting, and / or projecting the radiation after the radiation passes through a pattern forming device. Projection optical elements typically do not include a source and a pattern forming device. Summary of the invention

[0011] A method for improving a process model for a patterning process includes obtaining a) a measured profile from an image capture device, and b) a simulated profile generated from a simulation of the process model. The method also includes aligning the measured profile with the simulated profile by determining an offset between the measured profile and the simulated profile. The process model is calibrated to reduce a difference between the simulated profile and the measured profile calculated based on the determined offset.

[0012] In some variations, the offset may be further determined based on a measurement coordinate that substantially defines a portion of the measurement profile. In addition, the offset may be further determined based on a distance between the measurement coordinate and the simulated profile, the distance being a distance along a direction perpendicular to the measurement profile at the measurement coordinate. The alignment may also include reducing a cost function calculated based on the distance.

[0013] In other variations, edge placement (EP) coordinates may be generated on the measured profile, wherein the offset may be further determined based on the EP coordinates. The EP coordinates may be generated by interpolating between two or more measured coordinates. The EP coordinates may be generated by extrapolating from two or more measured coordinates. Thus, the calibration may also include modifying features of the process model to reduce the difference, the modification causing a change in the shape of the simulated profile.

[0014] In some variations, the measurement profile may be identified based on a change in intensity of pixels in the measurement image. The identification may be performed based on the change exceeding a grayscale threshold.

[0015] In yet other variations, the model may include obtaining the simulated contour from a Graphics Database System (GDS) polygon; and also includes converting edge placement coordinates or measured coordinates comprising the measured contour into GDS coordinates. The GDS polygon may be in one or more formats selected from the GDS Streaming Format (GDSII) and the Open Artifact System Interchange Standard (OASIS).

[0016] In a related aspect, a method for improving an optical proximity correction (OPC) model for a patterning process includes obtaining a) a measured profile from an image capture device, and b) a simulated profile generated from a simulation of the OPC model. The method also includes aligning the measured profile with the simulated profile by determining an offset between the measured profile and the simulated profile. Additionally, the method also includes modifying features of the OPC model to reduce a difference between the simulated profile and the measured profile calculated based on the determined offset.

[0017] In some variations, the feature includes one or more of a diffusion rate, a diffusion range, a deprotection ratio, and an acid / base concentration. The method may also include: obtaining the simulation profile based on the simulation of the OPC model, wherein the OPC model is a preliminary model that includes an optical model and does not include a resist model.

[0018] In other variations, the method may include: obtaining an initial simulated profile using a preliminary model including an optical model and a resist model; and modifying features of the resist model to reduce the difference between the initial simulated profile and the measured profile.

[0019] In a related aspect, a method for improving a process model for a patterning process includes: obtaining a) a plurality of measurement images from an image capture device, and b) a simulated profile generated from a simulation of the process model. The method also includes: aligning the measurement images; generating a combined measurement image from the aligned plurality of measurement images; extracting a measurement profile from the combined measurement image by an image analysis method; aligning the measurement profile with the simulated profile by determining an offset between the measurement profile and the simulated profile; and calibrating the process model to reduce a difference between the simulated profile and the measurement profile calculated based on the determined offset.

[0020] In some variations, the combined image may be produced by averaging the aligned measurement images. The measurement images may be obtained from printed patterns from at least two different dies made from a target pattern. Each of the measurement images producing the combined image may be acquired by scanning a different die.

[0021] In other variations, the image capture device may be a scanning electron microscope. Obtaining the measurement images may be performed by scanning an electron beam across the printed pattern at a plurality of angles, the plurality of angles including approximately +45 degrees and -45 degrees. Furthermore, half of the plurality of measurement images may be scanned at approximately +45 degrees, and the other half of the plurality of measurement images may be scanned at approximately -45 degrees.

[0022] In other variations, the obtaining may be performed using the scanning electron microscope operating at a dose lower than the dose required to obtain a scan sufficient to resolve critical dimensions. The image capture device may be an electron beam inspection system. The electron beam inspection system may have a large field of view, and the measurement image may be obtained at least in part from within the large field of view. The large field of view may be approximately 1 to 50 microns on a side or may be approximately 6 to 12 microns on a side. The electron beam inspection system may detect hot spots or weak points in a printed pattern.

[0023] In yet other variations, the method may further include: determining a common area among the plurality of measurement images captured from the image capture device; and generating the combined measurement image based on the common area.

[0024] In a related aspect, a method for evaluating an image of a printed pattern is implemented by at least one programmable processor. The method includes obtaining a first average image of the printed pattern, wherein the first average image is generated by averaging original images of the printed pattern. The method also includes identifying one or more features of the first average image. The method also includes executing, by the programmable processor, an image quality grading model and evaluating the first average image based at least on the one or more features. The evaluating includes determining, by the image quality grading model, whether the first average image meets a criterion.

[0025] In some variations, the first average image is generated by at least aligning the raw images and generating the first average image from the aligned raw images.

[0026] In other variations, the method may also include: generating a tag for at least one of the original images, the tag indicating whether the first average image meets the criterion. In addition, the tag may be associated with the original image in a computer memory.

[0027] In yet other variations, the method may include generating a second average image, the generating a second set of original images including printed patterns marked as meeting the criteria, wherein the second average image meets the criteria.

[0028] In some variations, the method may further include: training the image quality grading model using a data set that identifies at least one example of a first average image that does not meet the metric. The method may also include: generating a residual image corresponding to one of the plurality of first original images by subtracting at least one of the plurality of first original images from the first average image. The determination may also be based on the first average image and the residual image.

[0029] In other variations, the one or more features of the first average image evaluated by the image quality grading model may include at least one of an edge, an assist feature, and a sub-resolution assist feature. The metric may be based on at least one or more of a signal-to-noise ratio, a contrast, and an alignment quality, and the metric may correspond to the original images that are averaged to generate the average image.

[0030] In yet another variation, the image quality grading model further comprises a machine learning algorithm, wherein the determination may be based on an output of the machine learning algorithm. The label may be based on an output of the machine learning algorithm and may be at least one of a score or a Boolean value indicating whether the first average image satisfies the criterion.

[0031] In some variations, the machine learning algorithm may include at least one of a neural network algorithm, a nearest neighbor algorithm, a naive Bayes algorithm, a decision tree algorithm, a linear regression algorithm, and a support vector machine algorithm, a k-means clustering and an association rule algorithm.

[0032] In other variations, the raw image may be acquired using an image capture device, which may be at least one of a scanning electron microscope, an atomic force microscope, and an electron beam inspection system. The method may include providing active feedback during operation of the image capture device to improve at least one of a focus of the image capture device and a contrast of a raw image acquired by the image capture device.

[0033] According to an embodiment, a computer program product is provided, the computer program product comprising a non-transitory computer-readable medium having instructions recorded thereon, wherein the instructions, when executed by a computer, implement the method listed in the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate certain aspects of the subject matter disclosed herein and, together with the embodiments, help explain some principles associated with the disclosed embodiments.

[0035] Figure 1 A block diagram illustrating various subsystems of a lithographic projection apparatus according to an embodiment.

[0036] Figure 2 An exemplary flow chart for simulating lithography in a lithographic projection apparatus according to an embodiment is illustrated.

[0037] Figure 3 An exemplary measurement profile obtained from an image of a printed pattern is illustrated according to an embodiment.

[0038] Figure 4 An exemplary method of generating a measurement profile by averaging multiple images according to an embodiment is illustrated.

[0039] Figure 5 An exemplary method of aligning a measured profile with a simulated profile according to an embodiment is illustrated.

[0040] Figure 6 An exemplary method of determining an offset between a measured profile and a simulated profile according to an embodiment is illustrated.

[0041] Figure 7 An exemplary improvement in matching a simulated profile to a measured profile according to an embodiment is illustrated.

[0042] Figure 8An exemplary improvement of alignment based on improving a preliminary model is illustrated according to an embodiment.

[0043] Fig. 9 An exemplary method of calibrating a process model according to an embodiment is illustrated.

[0044] Fig.10 An exemplary method of calibrating an OPC model according to an embodiment is illustrated.

[0045] Fig.11 An exemplary method of acquiring multiple images of a target and calibrating a process model according to an embodiment is illustrated.

[0046] Fig.12 is a block diagram of an example computer system according to an embodiment.

[0047] Fig.13 is a block diagram of an example metrology system according to an embodiment.

[0048] Fig.14 is a process flow diagram of an example implementation of an improved metrology process according to an embodiment.

[0049] Fig.15 is a schematic diagram of a lithographic projection apparatus according to an embodiment.

[0050] Fig.16 is a schematic diagram of another lithography projection apparatus according to an embodiment.

[0051] Fig.17 is a detailed view of a lithographic projection apparatus according to an embodiment.

[0052] Fig.18 is a detailed view of a source collector module of a lithographic projection apparatus according to an embodiment.

[0053] Fig.19 Embodiments of electron beam inspection apparatus according to embodiments are schematically depicted.

[0054] Fig. 20 Further embodiments of an inspection device according to an embodiment are schematically illustrated.

[0055] Fig.21 are diagrams showing an original image and an average image of a printed pattern according to an embodiment.

[0056] Fig. 22 is a graph showing "good" and "bad" average images of printed patterns according to an embodiment.

[0057] Fig.23 is a process flow diagram illustrating an exemplary implementation of an image quality grading model according to an embodiment.

[0058] Fig.24 is a process flow diagram illustrating an exemplary image quality grading model for iteratively improving an average image in accordance with an embodiment. DETAILED DESCRIPTION

[0059] Although 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 to manufacture integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid crystal display panels, thin film magnetic heads, etc. Those skilled in the art will appreciate that in the context of such alternative applications, 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.

[0060] In this document, the terms "radiation" and "beam" are used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g., having a wavelength of 365, 248, 193, 157 or 126 nm) and EUV (i.e., extreme ultraviolet radiation, e.g., having a wavelength in the range of about 5 to 100 nm).

[0061] The pattern forming device may include or may form one or more design layouts. The design layout may be generated using a CAD (i.e., computer-aided design) process. This process is often referred to as EDA (i.e., electronic design automation). Most CAD programs follow a set of predetermined design rules in order to generate a functional design layout / pattern forming device. These rules are set by processing and design restrictions. For example, the design rules define the spatial tolerance between devices (such as gates, capacitors, etc.) or interconnects to ensure that the devices or lines do not interact with each other in an undesirable manner. One or more of the restrictions in the design rule restrictions may be referred to as a "critical dimension" (CD). The critical dimension of a device may be defined as the minimum width of a line or hole, or the minimum space / gap between two lines or two holes. Thus, CD determines the total size and density of the designed device. Of course, one of the goals in device manufacturing is to faithfully reproduce the original design intent (via a pattern forming device) on a substrate.

[0062] The term "mask" or "patterning device" as used herein may be broadly interpreted as referring to a general patterning device that can be used to impart a patterned cross-section to an incident radiation beam, the patterned cross-section corresponding to the pattern to be produced in a target portion of the substrate; the term "light valve" may also be used in this context. In addition to classical masks (transmissive or reflective, binary, phase-shifting, hybrid, etc.), examples of other such patterning devices include programmable mirror arrays and / or programmable LCD arrays.

[0063] An example of a programmable mirror array may be a matrix-addressable surface having a viscoelastic control layer and a reflective surface. The basic principle on which such a device is based is that (for example) addressed areas of the reflective surface reflect incident radiation as diffracted radiation, while unaddressed areas reflect incident radiation as non-diffracted radiation. Using suitable filters, the non-diffracted radiation can be filtered out of the reflected beam, so that only the diffracted radiation remains; in this way, the beam is patterned according to the addressing pattern of the matrix-addressable surface. The required matrix addressing can be performed by using suitable electronic means.

[0064] An example of a programmable LCD array is given in US Pat. No. 5,229,872, which is incorporated herein by reference.

[0065] Figure 1 A block diagram of various subsystems of a lithographic projection apparatus 10A according to an embodiment is illustrated. The main components are: a radiation source 12A, which may be a deep ultraviolet excimer laser source or other type of source, including an extreme ultraviolet (EUV) source (as discussed above, the lithographic projection apparatus itself need not have a radiation source); an illumination optical device, which, for example, defines a partial coherence (expressed as a standard deviation) and may include optics 14A, 16Aa, and 16Ab that shape the radiation from source 12A; a patterning device 18A; and a transmission optical device 16Ac that projects an image of the patterning device pattern onto a substrate plane 22A. An adjustable filter or aperture 20A at a pupil plane of the projection optics may constrain the range of beam angles impinging on the substrate plane 22A, with the maximum possible angle defining the numerical aperture NA=n sin(Θ max ), where n is the refractive index of the medium between the substrate and the last element of the projection optics, and θ max is the maximum angle of the beam emerging from the projection optics that can still impinge on the substrate plane 22A.

[0066] In a lithographic projection apparatus, a source providing illumination (i.e., radiation) to a pattern forming device and projection optics directs and shapes the illumination onto a substrate via the pattern forming device. The projection optics may include at least some of components 14A, 16Aa, 16Ab, and 16Ac. An aerial image (AI) is a distribution of radiation intensity at the substrate level. A resist model may be used to calculate a resist image based on the aerial image, and an example of this situation may be found in U.S. Patent Application Publication No. US2009-0157630, the entire disclosure of which is hereby incorporated by reference. The resist model is only related to the properties of the resist layer (e.g., the effects of chemical processes occurring during exposure, post-exposure baking (PEB), and development). The optical properties of the lithographic projection apparatus (e.g., the properties of the illumination, pattern forming device, and projection optics) specify the aerial image and may be defined in an optical model. Since the patterning device used in a lithographic projection apparatus can be varied, it is desirable to decouple the optical properties of the patterning device from the optical properties of the rest of the lithographic projection apparatus, including at least the source and the projection optics. Details of techniques and models for transforming a design layout into various lithographic images (e.g., aerial images, resist images, etc.), using which OPC is applied and performance (e.g., in terms of process windows) is evaluated are described in U.S. Patent Application Publication Nos. US 2008-0301620, 2007-0050749, 2007-0031745, 2008-0309897, 2010-0162197, and 2010-0180251, the disclosure of each of which is hereby incorporated by reference in its entirety.

[0067] One aspect of understanding the lithography process is understanding the interaction of the radiation with the patterning device. The electromagnetic field of the radiation after the radiation passes through the patterning device can be determined based on the electromagnetic field of the radiation before it reaches the patterning device and a function that characterizes the interaction. Such a function can be called a mask transmission function (which can be used to describe the interaction by the transmission patterning device and / or the reflection patterning device).

[0068] The mask transmission function can have a variety of different forms. One form is binary. A binary mask transmission function has any of two values ​​(e.g., zero and a positive constant) at any given location on the pattern forming device. A mask transmission function in binary form can be referred to as a binary mask. Another form is continuous. That is, the modulus of the transmittance (or reflectance) of the pattern forming device is a continuous function of the location on the pattern forming device. The phase of the transmittance (or reflectance) can also be a continuous function of the location on the pattern forming device. A mask transmission function in continuous form can be referred to as a continuous tone mask or a continuous transmission mask (CTM). For example, a CTM can be represented as a pixelated image, in which each pixel can be assigned, that is, assigned a value between 0 and 1 (e.g., 0.1, 0.2, 0.3, etc.) instead of a binary value of 0 or 1. In an embodiment, the CTM may be a pixelated grayscale image where each pixel has multiple values ​​(eg, in the range [-255, 255], normalized values ​​in the range [0, 1] or [-1, 1] or other suitable range).

[0069] The thin mask approximation, also known as the Kirchhoff boundary condition, is widely used to simplify the determination of the interaction of the radiation with the patterning device. The thin mask approximation assumes that the thickness of the structures on the patterning device is very small compared to the wavelength, and the width of the structures on the mask is very large compared to the wavelength. Therefore, the thin mask approximation assumes that the electromagnetic field after the patterning device is the multiplication of the incident electromagnetic field and the mask transmission function. However, as lithography processes use radiation with shorter and shorter wavelengths, and the structures on the patterning device become smaller and smaller, the assumption or assumption of the thin mask approximation breaks down. For example, the interaction of radiation with structures (such as the edge between the top surface and the sidewalls) can become significant due to the finite thickness of the structures ("mask 3D effect" or "M3D"). Including this scattering in the mask transmission function may enable the mask transmission function to better capture the interaction of the radiation with the patterning device. The mask transmission function under the thin mask approximation may be referred to as a thin mask transmission function. A mask transmission function covering M3D may be referred to as an M3D mask transmission function.

[0070] According to an embodiment of the present disclosure, one or more images may be generated. The image includes various types of signals that may be characterized by a pixel value or an intensity value of each pixel. Depending on the relative value of the pixels within the image, the signal may be referred to as, for example, a weak signal or a strong signal, which may be understood by a person of ordinary skill in the art. The terms "strong" and "weak" are relative terms based on the intensity values ​​of the pixels within the image, and the specific value of the intensity may not limit the scope of the present disclosure. In an embodiment, a strong signal and a weak signal may be identified or recognized based on a selected threshold. In an embodiment, the threshold may be fixed (e.g., the midpoint between the highest intensity and the lowest intensity of the pixels within the image). In an embodiment, a strong signal may refer to a signal having a value greater than or equal to an average signal value across the image, and a weak signal may refer to a signal having a value less than the average signal value. In an embodiment, a relative intensity value may be based on a percentage. For example, the weak signal may be a signal having an intensity less than 50% of the highest intensity of the pixels within the image (e.g., a pixel corresponding to a target pattern may be considered as a pixel having the highest intensity). In addition, each pixel within the image may be considered as a variable. According to this embodiment, a derivative or partial derivative may be determined with respect to each pixel within the image, and the value of each pixel may be determined or modified based on an evaluation based on a cost function and / or a gradient-based calculation of the cost function. For example, a CTM image may include pixels, where each pixel is a variable that may take any real value.

[0071] Figure 2 An exemplary flow chart for simulating lithography in a lithographic projection apparatus according to an embodiment is illustrated. A source model 31 represents the optical properties of the source (including radiation intensity distribution and / or phase distribution). A projection optics model 32 represents the optical properties of the projection optics (including changes to the radiation intensity distribution and / or phase distribution caused by the projection optics). A design layout model 35 represents the optical properties of a design layout (including changes to the radiation intensity distribution and / or phase distribution caused by the design layout 33), which is a representation of the arrangement of features on or formed by a pattern forming device. An aerial image 36 can be simulated based on the design layout model 35, the projection optics model 32, and the design layout model 35. A resist image 38 can be simulated based on the aerial image 36 using a resist model 37. The simulation of lithography can, for example, predict the contour and CD in the resist image.

[0072] More particularly, it should be noted that the source model 31 can represent the optical properties of the source, including but not limited to numerical aperture settings, illumination mean square deviation or standard deviation (σ) settings, and any specific illumination shape (e.g., off-axis radiation sources such as toroidal, quadrupole, dipole, etc.). The projection optics model 32 can represent the optical properties of the projection optics, including aberrations, deformations, one or more refractive indices, one or more physical sizes, one or more physical dimensions, etc. The design layout model 35 can represent one or more physical properties of a physical pattern forming device, such as described in, for example, U.S. Patent No. 7,587,704, which is incorporated herein by reference in its entirety. The goal of the simulation is to accurately predict, for example, edge placement, aerial image intensity slope, and / or CD, which can then be compared to an intended design. An intended design is typically defined as a pre-OPC design layout that can be provided in a standardized digital file format such as GDSII or OASIS or other file formats.

[0073] From this design layout, one or more portions referred to as "clips" may be identified. In an embodiment, a set of clips are extracted that represent complex patterns in the design layout (typically about 50 to 1000 clips, but any number of clips may be used). These patterns or clips represent small portions of the design (i.e., circuits, cells, or patterns), and more specifically, the clips typically represent small portions that require special attention and / or verification / checking. In other words, a clip may be a portion of the design layout, or may be similar or have similar behavior of multiple portions of the design layout, where one or more critical features are identified through experience (including clips provided by customers), through trial and error, or by running full-chip simulations. A clip may contain one or more test patterns or gauge patterns.

[0074] An initial larger set of segments may be provided a priori by the customer based on one or more known critical feature areas in the design layout that require specific image optimization. Alternatively, in another embodiment, an initial larger set of segments may be extracted from the entire design layout using some automated (such as machine vision) or manual algorithm that identifies one or more critical feature areas.

[0075] In a lithographic projection apparatus, as an example, the cost function can be expressed as

[0076]

[0077] where (z1,z2,…,z N ) are N design variables or their values. p (z1,z2,…,z N) can be the design variables (z1,z2,…,z N ), such as for (z1,z2,…,z N ) is the difference between the actual and expected values ​​of a characteristic for a set of values ​​of a design variable. p is with f p (z1,z2,…,z N ) is associated with a weight constant. For example, the characteristic may be the position of the edge of the pattern measured at a given point on the edge. Different f p (z1,z2,…,z N ) can have different weights w p For example, if a particular edge has a narrow range of allowed positions, then the value for f that represents the difference between the actual position of the edge and the expected position is p (z1,z2,…,z N )’s weight w p can be given a higher value. p (z1,z2,…,z N ) can also be a function of the interlayer characteristics, which in turn are the design variables (z1, z2, ..., z N ). Of course, CF(z1,z2,…,z N ) is not limited to the form in Equation 1. CF(z1,z2,…,z N ) may be in any other suitable form.

[0078] The cost function may represent any one or more suitable characteristics of the lithographic projection apparatus, the lithographic process, or the substrate, such as focus, CD, image shift, image deformation, image rotation, random variation, throughput, local CD variation, process window, inter-layer characteristics, or a combination thereof. In one embodiment, the design variables (z1, z2, ..., z N ) includes one or more selected from dose, global bias of the patterning device, and / or shape of the irradiation. Since the resist image often dictates the pattern on the substrate, the cost function may include a function representing one or more characteristics of the resist image. For example, f p (z1,z2,…,z N ) may simply be the distance between a point in the resist image and the expected position of that point (i.e., the edge placement error EPE p (z1,z2,…,z N )). The design variables may include any adjustable parameters, such as adjustable parameters of the source, the patterning device, the projection optics, dose, focus, etc.

[0079] The lithographic apparatus may include components commonly referred to as "wavefront manipulators" that can be used to adjust the shape of the wavefront and intensity distribution and / or the phase shift of the radiation beam. In an embodiment, the lithographic apparatus can adjust the wavefront and intensity distribution at any position along the optical path of the lithographic projection apparatus, such as before the pattern forming device, near the pupil plane, near the image plane, and / or near the focal plane. The wavefront manipulator can be used to correct or compensate for certain deformations of the wavefront and intensity distribution and / or phase shift caused by, for example, the source, the pattern forming device, temperature changes in the lithographic projection apparatus, thermal expansion of components of the lithographic projection apparatus, etc. Adjusting the wavefront and intensity distribution and / or phase shift can change the value of the characteristic represented by the cost function. Such changes can be simulated according to a model, or actually measured. The design variables may include parameters of the wavefront manipulator.

[0080] The design variables may have constraints, which may be expressed as (z1, z2, ..., z N )∈Z, where Z is a set of possible values ​​for the design variables. A possible constraint on the design variables may be imposed by the desired throughput of the lithographic projection apparatus. In the absence of such a constraint imposed by the desired throughput, the optimization may result in a set of values ​​for the design variables that are unrealistic. For example, if dose is a design variable, then in the absence of such a constraint, the optimization may result in dose values ​​that make throughput economically impossible. However, the usefulness of a constraint should not be interpreted as necessity. For example, the throughput may be affected by the pupil fill ratio. For some irradiation designs, a low pupil fill ratio may discard radiation, resulting in lower throughput. The throughput may also be affected by the chemical reaction of the resist. Slower resists (e.g., resists that require a higher amount of radiation to be properly exposed) result in lower throughput.

[0081] As used herein, the term "patterning process" generally means a process that produces an etched substrate by applying a specified pattern of light as part of a photolithography process. However, a "patterning process" may also include plasma etching, as many of the features described herein may provide benefits for using plasma processing to form printed patterns.

[0082] As used herein, the term "target pattern" means an idealized pattern to be etched on a substrate.

[0083] As used herein, the term "printed pattern" means a physical pattern on a substrate etched based on a target pattern. The printed pattern may include, for example, grooves, channels, recesses, edges, or other two-dimensional and three-dimensional features resulting from a photolithographic process.

[0084] As used herein, the term "process model" means a model that includes one or more models that simulate a patterning process. For example, a process model may include an optical model (e.g., the optical model models the lens system / projection system used to transmit light during the lithography process and may include modeling the final optical image of the light reaching the resist), a resist model (e.g., it models the physical effects of the resist, such as due to the chemical effects of light), and an OPC model (e.g., it may be used to manufacture a target pattern and may include sub-resolution resist features (SRAFs), etc.).

[0085] As used herein, the term "calibrate" means to modify (eg, improve or tune) and / or validate something, such as a process model.

[0086] The present disclosure describes, among other things, a method for improving a process model for a patterning process. Improving metrology during process model calibration may include obtaining an accurate image of a printed pattern (e.g., a printed wafer or portion thereof) based on a target pattern. Contours corresponding to features on the printed pattern may be extracted from the image. The contours (also referred to as measured contours) may then be aligned with simulated contours generated by the process model to allow calibration of the process model. The process model may be improved by adjusting parameters in the process model so that the simulated contours more accurately match the measured contours.

[0087] Figure 3 An exemplary measurement profile 330 obtained from an image of a printed pattern is illustrated according to an embodiment.

[0088] The photolithography process may be based on, for example, a target pattern 310 (in Figure 3 310) to produce a printed pattern (e.g., a circuit pattern for an integrated circuit or computer chip). Due to limitations in the patterning process, the printed pattern will typically only be an approximation of the target pattern 310.

[0089] The printed pattern may be imaged by an image capture device to produce a measurement image 320 (in Figure 3 ), which contains a profile corresponding to an idealized shape in the target pattern 310. In one embodiment, a method may include obtaining a measurement profile 330 from an image capture device such as a scanning electron microscope (also referred to as an electron beam inspection system). Fig.19 and Fig. 20An exemplary embodiment of an electron beam inspection system is described in more detail. The electron beam inspection system can be similar to a scanning electron microscope, but with a large field of view (LFOV) and high throughput for obtaining the measurement image 320. A non-limiting example of an electron beam inspection system can be the HMI eP5, which is particularly configured to have an LFOV. In some embodiments, the LFOV can measure, for example, approximately 1 to 1000 microns, 100 to 500 microns, 1 to 50 microns, 6 to 12 microns on a side, etc. The image capture device can be configured to detect hot spots and / or weak points in a printed pattern, as well as gates and active areas of a memory array such as a static random access memory (SRAM). As Figure 3 As illustrated in , the measured image 320 resembles the printed pattern, but the rectangular features in the measured image 320 show rounded and slightly deformed lines.

[0090] Some embodiments may include identifying a measurement profile 330 based on intensity changes of pixels in the measurement image 320 (in Figure 3 3). Image analysis techniques may be used to identify a measurement profile 330 in the measurement image 320. Changes in intensity, gradient, etc. may identify changes in height (or depth) of features in a printed pattern, such as used with edge determination. For example, when the measurement image is expressed as a grayscale image, this may identify an edge (i.e., measurement profile 330) when the change exceeds a grayscale threshold (i.e., the intensity is above or below a defined value).

[0091] Figure 4 An exemplary method of generating a measurement profile 330 by averaging a plurality of images is illustrated according to an embodiment.

[0092] As described above, the measurement profile 330 may be extracted from a single measurement image 320. However, in other embodiments, multiple images of the printed pattern may be combined into a combined image 420. Combining the images reduces the effects of noise or other forms of error that may be present in any of the individual images. In one embodiment, the combined image 420 may be generated by averaging the multiple measurement images 410. In other embodiments, the multiple measurement images 410 may be aligned prior to combining or averaging. Image alignment may be based on, for example, image registration of the multiple measurement images 410 (such as using registration marks), an image analysis program that determines the best match between the multiple measurement images 410, calculating a correlation coefficient based on the multiple measurement images 410, and the like.

[0093] Once the multiple measurement images 410 captured from the image capture device are aligned, a common area can be determined. As used herein, the term "common area" means a collection of pixels in the multiple measurement images 410 that reference the same physical area of ​​the printed pattern. Subsequently, a combined measurement image 320 can be generated based on the common area. This can result in some images that may have a larger or smaller field of view to have edge pixels removed so that the averaging process is uniform (i.e., the same number of pixels are averaged at each pixel).

[0094] Resist shrinkage from repeated electron beam exposure can occur when multiple images are acquired. Resist shrinkage can be reduced, for example, by an embodiment in which multiple measurement images 410 are obtained from printed patterns of at least two different tube cores manufactured by a target pattern. As used herein, the term "a tube core" or "multiple tube cores" means a block of semiconductor material on which a given functional circuit is or will be made. In such an embodiment, the tube core has a similar printed pattern and can therefore be used to produce a substantially equivalent image while reducing the physical exposure of the printed pattern to the electron beam. In other embodiments, each of the multiple measurement images 410 that produce a combined image 420 can be acquired by scanning different tube cores. In this way, each image used to produce the combined image 420 is based on a single printed pattern. In this way, in some embodiments, resist shrinkage can be reduced by using a scanning electron microscope to obtain the measurement image 320, and the scanning electron microscope is operated at a dose lower than the dose required to obtain a scan sufficient to resolve the critical dimension.

[0095] In some embodiments, obtaining the measurement image 320 may be performed by scanning the electron beam across the printed pattern at several angles. The angles may include approximately +45 degrees and -45 degrees. As used herein, the term "approximately" (e.g., with respect to a scanning angle) means an exact angle or an angle very close to an exact angle (e.g., within a few degrees or tens of degrees). In another embodiment, half of the measurement image 320 may be scanned at approximately +45 degrees and the other half of the measurement image 320 may be scanned at approximately -45 degrees.

[0096] Figure 5 An exemplary method of aligning the measured profile 330 with the simulated profile 510 is illustrated according to an embodiment.

[0097] The process model for simulating the printed pattern may include any combination of a resist model, an optical model, an optical proximity correction model, etc. Therefore, a simulated profile 510 may be generated based on the simulation of the process model. As used herein, a "simulated profile 510" means a profile generated by one or more computational models and representing a predicted result of a lithography process (whether a final stage or an intermediate stage).

[0098] To calibrate the process model, the measured profile 330 may be compared to the simulated profile 510. As part of the calibration process, the measured profile 330 may be aligned with the simulated profile 510. Figure 5 In the example of , the measured profile 330 (as represented by the graphical representation of the measured image 320 in this particular illustration) can be compared to the simulated profile 510. Figure 5 In the upper illustration of FIG. 5 , the measured profile 330 is not properly aligned with the simulated profile 510. The measured profile 330 can be translated and / or rotated across the simulated profile 510 until the measured profile 330 is in a substantially correct position, such as Figure 5 This can provide a rough alignment which can be further improved as described below.

[0099] Figure 6 An exemplary method of determining an offset 610 between a measured profile 330 and a simulated profile 510 is illustrated in accordance with an embodiment.

[0100] For example, in the above Figure 5 Other alignment methods are implemented after the described rough alignment. Such an embodiment may include aligning the measured contour 330 with the simulated contour 510 by determining an offset 610 between the measured contour 330 and the simulated contour 510. As used herein, "offset 610" means the distance between a point on the measured contour 330 and another point on the simulated contour 510. The present disclosure provides various methods for determining the offset 610. For example, the offset 610 can be further determined based on measurement coordinates 620 that roughly define a portion of the measured contour 330. As used herein, the term "measurement coordinates" means coordinates that define part or all of the measured contour. The measurement coordinates can be generated by an imaging device, by analyzing an image obtained by an imaging device, etc. For example, the measurement coordinates can be pixel positions that have been determined to correspond to the edge of the contour. Therefore, the edge detection program can generate the measurement coordinates 620 based on image processing of the measured image 320. Examples of measurement coordinates 620 are shown in FIG. Figure 6 This is illustrated by a circle on the measurement profile 330 .

[0101] In one embodiment, the offset 610 may be further determined based on the distance between the measured coordinates 620 and the simulated profile 510. In some specific embodiments, the distance may be in a direction perpendicular to the measured profile 330 at the measured coordinates 620. In other embodiments, the degree of alignment may be determined by, for example, summing the squares of the distances of some or all of the offsets, or summing some or all of the offsets. This may be performed, for example, using the x-component and / or y-component of the vertical offset vector.

[0102] In some embodiments, the alignment may also include reducing: a cost function calculated based on the distance. Examples of cost functions are described above (e.g., the description of Equation 1). The cost function may be reduced by, for example, fine-tuning the location of the measured profile 330. When the cost function is at a minimum value (or otherwise satisfactory value), the alignment of the measured profile 330 with the simulated profile 510 may be used for additional processes or as a measure of process model calibration.

[0103] To provide additional points for the alignment methods described herein, some embodiments may include generating any number of additional points (e.g., edge placement (EP) coordinates) on the measurement profile 330. As used herein, EP coordinates 630 (also referred to herein as EP gauges) are additional points that define the measurement profile 330. An example of EP coordinates 630 is Figure 6 60 is illustrated by a solid square located on the measurement profile 330. In some embodiments, the EP coordinates 630 may be generated by interpolating between two or more measurement coordinates 620. In other embodiments, the EP coordinates 630 may be generated by extrapolating from two or more measurement coordinates 620. Thus, the offset 610 may be further determined based on the EP coordinates 630 instead of or in addition to the measurement coordinates 620.

[0104] In some embodiments, the simulated contour 510 may be obtained from a Graphic Database System (GDS) polygon (i.e., a polygon generated by the process model and corresponding to the contour shape). In other embodiments, the GDS polygon may be in one or more formats selected from the GDS stream format (GDSII) and the Open Artifact System Interchange Standard (OASIS). Next, as part of the calibration process, the edge placement coordinates 630 and / or the measured coordinates 620 including the measured simulated contour 510 may be converted to GDS coordinates. Such a conversion may allow for a more direct comparison between the simulated contour 510 and the measured contour 330.

[0105] Figure 7 An exemplary improvement in matching simulated profile 510 to measured profile 330 is illustrated according to an embodiment.

[0106] Embodiments described herein may facilitate calibration of a process model to provide a more accurate match between a simulated profile 510 (generated by the process model) and a measured profile 330. In some embodiments, calibrating the process model may include reducing a difference calculated based on a determined offset 610 between the simulated profile 510 and the measured profile 330.

[0107] As used herein, "difference" means a quantitative measure of the degree of deviation between two or more contours. A non-limiting example of a difference is the aforementioned cost function. Another example of a difference may also be the offset or distance between points on two contours without using these points in the cost function.

[0108] In some embodiments, the method may include modifying features of the process model to reduce the difference. In some embodiments, the modification may result in a change in the shape of the simulated profile 510. Examples of features of the resist model that may be modified may include diffusion rate, diffusion range, deprotection ratio, and acid / base concentration. Modifications performed in this manner may be viewed as "fine tuning" the process model to improve its ability to predict the measured profile 330. In some embodiments, this may result in an improved optical model, resist model, OPC model, and the like.

[0109] Figure 8 An exemplary alignment improvement based on improving preliminary model 810 is illustrated according to an embodiment.

[0110] Improving a process model including an OPC model may start with a preliminary model 810, rather than, for example, a complete process model. For example, some embodiments may include obtaining the simulation profile 510 based on a simulation of the OPC model, which may be the preliminary model 810. The preliminary model 810 may include a reduced number of model components, such as including an optical model and not including a resist model.

[0111] In other embodiments, the method may include obtaining the initial simulated profile 510 using an improved preliminary model 820 (e.g., which includes an optical model and a resist model). In particular, some embodiments may include modifying features of the resist model to reduce the difference between the initial simulated profile 510 and the measured profile 330. Examples of features that may be modified may include diffusion rate, diffusion range, deprotection ratio, and acid / base concentration, as described above with respect to the resist model. In this way, the preliminary model 810 may be used and improved as described herein. In other embodiments, modifications to the preliminary model 810 may produce an improved preliminary model 820, which in turn produces the simulated profile 510. Such an iterative method may provide a gradually improved simulated profile 510 for use in the alignment process described herein.

[0112] In some embodiments of the present disclosure, reduced processing time and computational overhead have been achieved. For example, by taking advantage of features including image averaging (such as Figure 4 described) and accurate image alignment (such as Figure 3 The computation time for image acquisition has been reduced by at least an order of magnitude, from days to hours, as described in the literature. At the same time, the model calibration time for implementing these high-quality EP gauges is roughly the same as when using CD gauges.

[0113] Fig. 9 An exemplary method of calibrating a process model according to an embodiment is illustrated.

[0114] In some embodiments, a method for improving a process model for a patterning process may include, at 910 , obtaining a) a measured profile 330 from an image capture device, and b) a simulated profile 510 generated from a simulation of the process model.

[0115] At 920 , the measured profile 330 is aligned with the simulated profile 510 by determining the offset 610 between the measured profile 330 and the simulated profile 510 .

[0116] At 930 , the process model is calibrated to reduce the difference calculated based on the determined offset 610 between the simulated profile 510 and the measured profile 330 .

[0117] Fig.10 An exemplary method of calibrating an OPC model according to an embodiment is illustrated.

[0118] In some embodiments, a method for improving an optical proximity correction (OPC) model for a patterning process may include, at 1010, obtaining a) a measured profile 330 from an image capture device, and b) a simulated profile 510 generated from a simulation of the OPC model.

[0119] At 1020 , the measured profile 330 is aligned with the simulated profile 510 by determining an offset 610 between the measured profile 330 and the simulated profile 510 .

[0120] At 1030 , features of the OPC model are modified to reduce the difference calculated based on the determined offset 610 between the simulated profile 510 and the measured profile 330 .

[0121] Fig.11 An exemplary method of acquiring multiple images of a target and calibrating a process model according to an embodiment is illustrated.

[0122] In some embodiments, a method for improving a process model for a patterning process may include, at 1110 , obtaining a) a plurality of measurement images 410 from an image capture device, and b) a simulated profile 510 generated from a simulation of the process model.

[0123] At 1120 , the plurality of measurement images 410 are aligned.

[0124] At 1130 , a combined measurement image 320 is generated based on the aligned plurality of measurement images 410 .

[0125] At 1140 , a measurement profile 330 is extracted from the combined measurement image 320 by an image analysis method.

[0126] At 1150 , the measured profile 330 is aligned with the simulated profile 510 by determining an offset 610 between the measured profile 330 and the simulated profile 510 .

[0127] At 1160 , the process model is calibrated to reduce the difference calculated based on the determined offset 610 between the simulated profile 510 and the measured profile 330 .

[0128] Fig.12 is a block diagram of an example metrology system according to an embodiment.The embodiments described herein may be implemented on any number and combination of computing systems, image capture devices, servers, and user interfaces. Fig.12 , wherein a cluster 1210, which may optionally include any number of computers operating in series and / or parallel, may be configured to allow selection and transmission of EP coordinates 630, also referred to herein as EP gauges 630. The EP gauges 630 may be transmitted to one or more management servers 1220, where the recipes 1230 may be sent to an image capture device 1240. The recipes 1230 may include information related to the patterning process and also include instructions for operating the image capture device 1240. The exemplary system thus described improves OPC prediction accuracy and reduces OPC development cycle time.

[0129] Fig.13 is a process flow diagram of an example implementation of an improved metrology process according to an embodiment.

[0130] Methods for improving metrology according to systems and embodiments described herein may include executing a process model on a computing cluster such as cluster 1210. At 1310, the process model may accept a mask design. At 1312, the process model may then generate a GDS layout that specifies the target pattern. At 1314, the process model may then select one or more EP gauges, such as for example, Figure 6as described.

[0131] At 1320, an image capture device, such as image capture device 1240, may generate a profile 1230. Profile 1230 may be used by image capture device 1240 to perform high quality metrology on the printed pattern at 1322, including generating any number of high resolution measurement images. The measurement images may be transmitted to cluster 1210 for image processing.

[0132] Image processing may include, for example, performing image filtering at 1330, performing image alignment and averaging at 1332, performing contour extraction at 1334, and performing EP gauge extraction at 1336. Image filtering may include, for example, automatically removing misprinted images and / or low contrast images based on, for example, an allowable benchmark or tolerance. Optionally, as part of the contour extraction process, the matching solution 1230 and the measurement image 320 may be input from 1322 to increase metrology consistency by comparing the measurement image 320 before and after image filtering and averaging the alignment process.

[0133] Model calibration and validation may be performed at 1340, where the extracted EP gauges at 1336 may be received by one or more computing systems. The calibrated and validated model may be optimized at 1340 to support a large number of EP gauges, such as 2, 3, 3.6, 5, 10 times or more increase in the number of CD gauges. At 1342, the process model may be calibrated, and at 1344, the calibrated process model may be provided to a graphical user interface (GUI) for user review, improvement, transfer, or further processing.

[0134] Fig.14 is a block diagram of an example computer system CS according to an embodiment.

[0135] 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 coupled to the bus BS for storing information and instructions to be executed by the processor PRO, such as a random access memory (RAM) or other dynamic storage device. The main memory MM can also be used to store temporary variables or other intermediate information during the execution of the instructions to be executed by the processor PRO. The computer system CS also 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 disk or optical disk is provided and coupled to the bus BS for storing information and instructions.

[0136] The computer system CS can be coupled to a display DS for displaying information to a computer user via a bus BS, such as a cathode ray tube (CRT) or a flat panel display or a touch panel display. An input device ID including alphanumeric keys 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 controller CC (such as a mouse, trackball or cursor direction keys) for communicating direction information and command selections to the processor PRO and for controlling cursor movement on the display DS. Such an input device typically has two degrees of freedom on two axes (a first axis (e.g., x) and a second axis (e.g., y)), which allows the device to specify a position in a plane. A touch panel (screen) display can also be used as an input device.

[0137] According to one embodiment, the part of one or more methods described herein can be performed by computer system CS in response to one or more sequences of one or more instructions executed by processor PRO in main memory MM. Such instructions can be read into main memory MM from another computer readable medium (such as storage device SD). The execution of the sequence of instructions contained in main memory MM makes processor PRO perform process steps described herein. One or more processors in multi-processing arrangement can also be used to execute the sequence of instructions contained in main memory MM. In alternative embodiments, hard-wired circuits can be used to replace software instructions or be combined with software instructions. Therefore, the description herein is not limited to any specific combination of hardware circuits and software.

[0138] The term "computer-readable medium" used herein refers to any medium that participates in providing instructions to the processor PRO for execution. Such a medium can take many forms, including but not limited to non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical disks or disks, such as storage devices SD. Volatile media include dynamic memory, such as main memory MM. Transmission media include coaxial cables, copper wires and optical fibers, including wires containing bus BS. Transmission media can also take the form of sound waves or light waves, such as sound waves or light waves generated during radio frequency (RF) and infrared (IR) data communication. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, any other physical media with hole patterns, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or thyristors. Instructions can be recorded on non-temporary computer-readable media. The instructions can implement any of the features described herein when executed by a computer. Temporary computer-readable media can include carriers or other propagating electromagnetic signals.

[0139] Various forms of computer-readable media may involve one or more sequences of one or more instructions being transmitted to the processor PRO for execution. For example, the instruction may initially be carried on a disk of a remote computer. The remote computer may load the instruction into its dynamic memory and send the instruction via a telephone line using a modem. The modem local to the computer system CS may receive data on the telephone line and convert the data into 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 transmits the data to the main memory MM, from which the processor PRO retrieves and executes the instruction. The instruction received by the main memory MM may optionally be stored on a storage device SD before or after being executed by the processor PRO.

[0140] 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 coupled to a network link NDL, which is connected to a local area 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 with 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 with 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.

[0141] The network link NDL typically provides data communication to other data devices through one or more networks. For example, the network link NDL can provide a connection to the host computer HC through a local area network LAN. This can include data communication services provided through the global packet data communication network now commonly referred to as the "Internet" INT. Both the local area network LAN (Internet) and the local area network LAN use electrical signals, electromagnetic signals or optical signals that carry digital data streams. The signals through the various networks and the signals on the network data link NDL and through the communication interface CI (which transmits digital data to and from the computer system CS) are exemplary forms of carrier waves that transport information.

[0142] The computer system CS can send messages and receive data, including program code, through the network, the network data link NDL and the communication interface CI. In the Internet example, the host computer HC can transmit the requested code for the application program through the Internet INT, the network data link NDL, the local area network LAN and the communication interface CI. For example, one such downloaded application can provide all parts of the method described herein. The received code can be executed by the processor PRO when it is received, 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.

[0143] Fig.15 is a schematic diagram of a lithographic projection apparatus according to an embodiment.

[0144] The lithographic projection apparatus may comprise an illumination system IL, a first object table MT, a second object table WT and a projection system PS.

[0145] The illumination system IL may condition the radiation beam B. In this particular case, the illumination system also comprises a radiation source SO.

[0146] A first object table (eg, patterning device table) MT may have a patterning device holder for holding a patterning device MA (eg, mask table) and be connected to a first positioner for accurately positioning the patterning device relative to the object PS.

[0147] A second object table (substrate table) WT may have a substrate holder for holding a substrate W (eg a silicon wafer coated with resist) and is connected to a second positioner for accurately positioning the substrate relative to the item PS.

[0148] A projection system ("lens") PS (eg, a refractive, reflective or catadioptric optical system) may image the irradiated portion of the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0149] As described herein, the apparatus may be of the transmissive type (e.g., employing a transmissive patterning device). However, in general, it may be of the reflective type (e.g., employing a reflective patterning device). The apparatus may employ a different kind of patterning device than a classical mask; examples include a programmable mirror array or an LCD matrix.

[0150] A source SO (e.g. a mercury lamp or an excimer laser, laser produced plasma (LPP) EUV source) generates a radiation beam. This beam is fed into an illumination system (illuminator) IL, for example directly or after having traversed a conditioning device such as a beam expander Ex. The illuminator IL may comprise an adjustment device AD ​​for setting the outer radial extent and / or the inner radial extent (commonly referred to as σ-outer and σ-inner, respectively) of the intensity distribution in the beam. In addition, the illuminator IL will typically comprise various other components, such as an integrator IN and a condenser CO. In this way, the beam B impinging on the patterning device MA has a desired uniformity and intensity distribution in its cross section.

[0151] In some embodiments, the source SO may be within the housing of the lithographic projection device (this is often the case when the source SO is, for example, a mercury lamp), but it may also be remote from the lithographic projection device, into which the radiation beam it generates is directed (for example, with the aid of appropriate directional mirrors); the latter may be the case when the source SO is an excimer laser (for example, based on KrF, ArF or F2 laser action).

[0152] The beam PB may then intercept the patterning device MA held on the patterning device table MT. Having traversed the patterning device MA, the beam PB may pass through a lens PL which focuses the beam B onto a target portion C of the substrate W. With the aid of the second positioning device (and the interferometric measurement device IF), the substrate table WT may be accurately moved, for example, in order to position different target portions C in the path of the beam PB. Similarly, the first positioning device may be used to accurately position the patterning device MA relative to the path of the beam B, for example after mechanically retrieving the patterning device MA from a patterning device library or during scanning. Typically, movement of the object tables MT, WT may be achieved with the aid of a long-stroke module (coarse positioning) and a short-stroke module (fine positioning). However, in the case of a stepper (as opposed to a step-and-scan tool), the patterning device table MT may be connected only to a short-stroke actuator, or may be fixed.

[0153] The depicted tool can be used in two different modes, a step mode and a scan mode. In the step mode, the patterning device table MT is held substantially stationary and the entire patterning device image is projected at once (i.e. a single "flash") onto a target portion C. The substrate table WT can be shifted in the x and / or y direction so that a different target portion C can be illuminated by the beam PB.

[0154] In scan mode, essentially the same situation applies, except that a given target portion C is not exposed in a single "flash". Alternatively, the patterning device table MT can be moved in a given direction (the so-called "scanning direction", e.g. the y-direction) at a velocity v, so that the projection beam B is scanned across the patterning device image; while the substrate table WT is simultaneously moved in the same or opposite direction at a velocity V=Mv, where M is the magnification of the lens PL (typically M=1 / 4 or 1 / 5). In this way, a relatively large target portion C can be exposed without having to compromise resolution.

[0155] Fig.16 is a schematic diagram of another lithographic projection apparatus (LPA) according to an embodiment.

[0156] The LPA may include a source collector module SO, a

[0157] An illumination system (illuminator) IL for EUV radiation), a support structure MT, a substrate table WT and a projection system PS.

[0158] a support structure (e.g., patterning device table) MT, which may be configured to support a patterning device (e.g., mask or reticle) MA and is connected to a first positioner PM configured to accurately position the patterning device;

[0159] A substrate table (eg, wafer stage) WT may be configured to hold a substrate (eg, a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate.

[0160] The projection system (eg, a reflective projection system) PS may be configured to project the pattern imparted to the radiation beam B by the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0161] As depicted here, the LPA can be of the reflective type (e.g., using a reflective patterning device). It should be noted that since most materials are absorptive in the EUV wavelength range, the patterning device can have a multilayer reflector including, for example, multiple stacks of molybdenum and silicon. In one example, the multilayer reflector has 40 layer pairs of molybdenum and silicon, where each layer is a quarter wavelength thick. Even smaller wavelengths can be produced using x-ray lithography. Since most materials are absorptive at EUV and x-ray wavelengths, a thin sheet of patterned absorbing material on the patterning device topography (e.g., a TaN absorber on top of a multilayer reflector) defines where features will be printed (positive resist) or not (negative resist).

[0162] The illuminator IL may receive a beam of extreme ultraviolet radiation from a source collector module SO. Methods for generating EUV radiation include, but are not necessarily limited to, converting a material into a plasma state having at least one element having one or more emission lines in the EUV range (e.g., xenon, lithium, or tin). In one such method, commonly referred to as laser produced plasma ("LPP"), the plasma may be generated by irradiating a fuel, such as a droplet, stream, or cluster of a material having a line emitting element, with a laser beam. The source collector module SO may be a laser ( Fig.11 The laser and source collector module may be separate entities, such as when a CO2 laser is used to provide the laser beam for fuel excitation.

[0163] In these cases, the laser is not considered to form part of the lithographic apparatus, and the radiation beam may be delivered from the laser to the source collector module by means of a beam delivery system including, for example, suitable directing mirrors and / or a beam expander. In other cases, the source may be an integral part of the source collector module, for example when the source is a discharge produced plasma EUV generator (commonly referred to as a DPP source).

[0164] The illuminator IL may comprise an adjuster for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer radial extent and / or the inner radial extent (commonly referred to as σ-outer and σ-inner, respectively) of the intensity distribution in a pupil plane of the illuminator may be adjusted. Furthermore, the illuminator IL may comprise various other components, such as a faceted field mirror arrangement and a faceted pupil mirror arrangement. The illuminator may be used to adjust the radiation beam to have a desired uniformity and intensity distribution in its cross section.

[0165] The radiation beam B may be incident on a patterning device (e.g. a mask) MA held on a support structure (e.g. a patterning device table) MT and patterned by the patterning device. Having been reflected by the patterning device (e.g. a mask) MA, the radiation beam B passes through a projection system PS which focuses the radiation beam onto a target portion C of the substrate W. With the aid of a second positioning device PW and a position sensor PS2 (e.g. an interferometer device, a linear encoder or a capacitive sensor), the substrate table WT may be accurately moved, for example in order to position a different target portion C in the path of the radiation beam B. Similarly, the first positioning device PM and a further position sensor PS1 may be used to accurately position the patterning device (e.g. a mask) MA relative to the path of the radiation beam B. The patterning device (e.g. a mask) MA and the substrate W may be aligned using patterning device alignment marks M1, M2 and substrate alignment marks P1, P2.

[0166] The depicted device LPA can be used in at least one of the following modes: a stepping mode, a scanning mode and a stationary mode.

[0167] In step mode, the support structure (e.g. patterning device table) MT and the substrate table WT are held substantially stationary while an entire pattern imparted to the radiation beam is projected once onto a target portion C (i.e. a single static exposure). The substrate table WT is then moved in the X and / or Y direction so that a different target portion C can be exposed.

[0168] In scan mode, the support structure (e.g. patterning device table) MT and the substrate table WT are scanned synchronously while a pattern imparted to the radiation beam is projected onto a target portion C (i.e. a single dynamic exposure). The velocity and direction of the substrate table WT relative to the support structure (e.g. patterning device table) MT may be determined by the (de-)magnification and image reversal characteristics of the projection system PS.

[0169] In stationary mode, the support structure (e.g. patterning device table) MT holding the programmable patterning device is held substantially stationary and the substrate table WT is moved or scanned while a pattern imparted to the radiation beam is projected onto a target portion C. In this mode, a pulsed radiation source is typically employed and the programmable patterning device is updated as required after each movement of the substrate table WT or between successive radiation pulses during a scan. This mode of operation may be readily applicable to maskless lithography using a programmable patterning device such as a programmable mirror array of the type mentioned above.

[0170] Fig.17 is a detailed view of a lithographic projection apparatus according to an embodiment.

[0171] As shown, the LPA may include a source collector module SO, an illumination system IL, and a projection system PS. The source collector module SO is constructed and arranged to maintain a vacuum environment in an enclosure 220 of the source collector module SO. A plasma 210 emitting EUV radiation may be formed by a discharge-generated plasma source. EUV radiation may be generated by a gas or vapor, such as xenon, lithium vapor, or tin vapor, wherein an extremely hot plasma 210 is generated to emit radiation in the EUV range of the electromagnetic spectrum. For example, an extremely hot plasma 210 is generated by causing a discharge of an at least partially ionized plasma. In order to effectively generate radiation, it may be necessary to have, for example, a partial pressure of 10Pa of Xe, Li, Sn vapor, or any other suitable gas or vapor. In an embodiment, an excited tin (Sn) plasma is provided to generate EUV radiation.

[0172] The radiation emitted by the hot plasma 210 is transferred from the source chamber 211 to the collector chamber 212 via an optional gas barrier or contaminant trap 230 (also referred to as a contaminant barrier or foil trap in some cases) positioned in or behind an opening in the source chamber 211. The contaminant trap 230 may include a channel structure. The contaminant trap 230 may also include a gas barrier, or a combination of a gas barrier and a channel structure. As is known in the art, the contaminant trap or contaminant barrier 230 further shown herein includes at least a channel structure.

[0173] The collector chamber 211 may include a radiation collector CO which may be a so-called grazing incidence collector. The radiation collector CO has an upstream radiation collector side 251 and a downstream radiation collector side 252. Radiation traversing the collector CO may be reflected by the grating spectral filter 240 and then focused at a virtual source point IF along the optical axis indicated by the dash-dot line 'O'. The virtual source point IF is often referred to as an intermediate focus, and the source collector module is arranged so that the intermediate focus IF is located at or near an opening 221 in the enclosure structure 220. The virtual source point IF is an image of the radiation emitting plasma 210.

[0174] The radiation then traverses an illumination system IL which may include a faceted field mirror arrangement 22 and a faceted pupil mirror arrangement 24 arranged to provide a radiation beam 21 having a desired angular distribution at the patterning device MA, and a radiation intensity having a desired uniformity at the patterning device MA. After reflection of the radiation beam 21 at the patterning device MA held by the support structure MT, a patterned beam 26 is formed and the patterned beam 26 is imaged by the projection system PS via reflective elements 28, 30 onto a substrate W held by a substrate table WT.

[0175] There may typically be more elements than shown in the illumination optics unit IL and the projection system PS. Depending on the type of lithographic apparatus, a grating spectral filter 240 may optionally be present. Furthermore, there may be more mirrors than shown in the figures, for example there may be more than 200 in the projection system PS. Fig.12 1-6 additional reflective elements beyond the elements shown.

[0176] like Fig.12 The collector optics CO shown is depicted as a nested collector with grazing incidence reflectors 253, 254 and 255, merely as an example of a collector (or collector mirror). The grazing incidence reflectors 253, 254 and 255 are arranged axially symmetrically around the optical axis O, and this type of collector optics CO can be used in combination with a discharge produced plasma source, often referred to as a DPP source.

[0177] Fig.18 is a detailed view of a source collector module SO of a lithographic projection apparatus LPA according to an embodiment.

[0178] The source collector module SO can be part of the LPA radiation system. The laser LA can be arranged to deposit laser energy into a fuel such as xenon (Xe), tin (Sn) or lithium (Li), thereby producing a highly ionized plasma 210 with an electron temperature of tens of electron volts. The high-energy radiation produced during the deexcitation and recombination or recombination of these ions is emitted from the plasma, collected by the near normal incidence collector optical device CO, and focused onto the opening 221 in the enclosure structure 220.

[0179] The concepts disclosed herein can simulate or mathematically model any general imaging system for imaging sub-wavelength features, and can be used in particular with emerging imaging technologies that can produce shorter and shorter wavelengths. Emerging technologies already in use include extreme ultraviolet (EUV), DUV lithography that can produce 193nm wavelengths by using ArF lasers and even 157nm wavelengths by using fluorine lasers. In addition, EUV lithography can produce wavelengths in the range of 5nm to 20nm by using synchrotrons or by using high energy electrons to bombard materials (solid or plasma) to produce photons in this range.

[0180] Fig.19An embodiment of an electron beam inspection device 1920 according to an embodiment is schematically depicted. In an embodiment, the inspection device may be an electron beam inspection device (e.g., the same as or similar to a scanning electron microscope (SEM)) that obtains an image of a structure exposed or transferred on the substrate (e.g., a certain structure or all structures of a device such as an integrated circuit). A primary electron beam 1924 emitted from an electron source 1922 is converged by a condenser lens 1926 and then passes through a beam deflector 1928, an EB deflector 1930, and an objective lens 1932 to irradiate a substrate 1910 on a substrate stage 1912 at a focal point.

[0181] When the substrate 1910 is irradiated with the electron beam 1924, secondary electrons or second electrons are generated from the substrate 1910. The secondary electrons are deflected by the E×B deflector 1930 and detected by the secondary electron detector 1934. A two-dimensional electron beam image can be obtained by detecting the electrons generated from the sample, synchronously, for example, two-dimensionally scanning the electron beam by the beam deflector 1928 or repeatedly scanning the electron beam 1924 in the X or Y direction by the beam deflector 1928, and continuously moving the substrate 1910 in the other direction of the X or Y direction by the substrate stage 1912. Thus, in an embodiment, the electron beam inspection device has a field of view for the electron beam defined by an angular range into which the electron beam can be provided by the electron beam inspection device (e.g., an angular range that the deflector 1928 can provide for the electron beam 1924 to pass). Thus, the spatial extent of the field of view is the spatial extent to which the angular range of the electron beam can impinge on a surface (wherein the surface may be stationary or may be moving relative to the field).

[0182] The signal detected by the secondary electron detector 1934 is converted into a digital signal by an analog / digital (A / D) converter 1936, and the digital signal is sent to the image processing system 1950. In an embodiment, the image processing system 1950 may have a memory 1956 for storing all or part of the digital image for processing by a processing unit 1958. The processing unit 1958 (e.g., specially designed hardware, or a combination of hardware and software, or a computer-readable medium including software) is configured to convert or process the digital image into a data set representing the digital image. In an embodiment, the processing unit 1958 is configured or programmed to cause the method described herein to be performed. In addition, the image processing system 1950 may have a storage medium 1956 configured to store the digital image and the corresponding data set in a reference database. A display device 1954 may be connected to the image processing system 1950 so that an operator can perform necessary operations of the equipment with the aid of a graphical user interface.

[0183] Fig. 20Schematically illustrates another embodiment of the inspection device according to the embodiment. The system is used to inspect a sample 90 (such as a substrate) on a sample platform 89, and includes a charged particle beam generator 81, a condenser lens module 82, a probe forming objective lens module 83, a charged particle beam deflection module 84, a secondary charged particle detector module 85, and an image forming module 86.

[0184] The charged particle beam generator 81 generates a primary charged particle beam 91. The condenser lens module 82 condenses the generated primary charged particle beam 91. The probe forming objective lens module 83 focuses the condensed primary charged particle beam into a charged particle beam probe 92. The charged particle beam deflection module 84 causes the formed charged particle beam probe 92 to scan across the surface of the area of ​​interest on the sample 90 fastened on the sample platform 89. In an embodiment, the charged particle beam generator 81, the condenser lens module 82 and the probe forming objective lens module 83 or their equivalent designs, alternatives or any combination thereof together form a charged particle beam probe generator that generates a scanning charged particle beam probe 92.

[0185] The secondary charged particle detector module 85 detects secondary charged particles 93 (also together with other reflected or scattered charged particles from the sample surface) emitted from the sample surface once bombarded by the charged particle beam probe 92 to produce a secondary charged particle detection signal 94. The image forming module 86 (e.g., a computing device) is coupled to the secondary charged particle detector module 85 to receive the secondary charged particle detection signal 94 from the secondary charged particle detector module 85 and thereby form at least one scanning image. In an embodiment, the secondary charged particle detector module 85 and the image forming module 86 or their equivalent designs, alternatives, or any combination thereof together form an image forming device that forms a scanning image from the detected secondary charged particles emitted from the sample 90 bombarded by the charged particle beam probe 92.

[0186] In an embodiment, the monitoring module 87 is coupled to the image forming module 86 of the image forming device to monitor, control, etc. the patterning process and / or derive parameters for patterning process design, control, monitoring, etc. using the scanned image of the sample 90 received from the image forming module 86. Thus, in an embodiment, the monitoring module 87 is configured or programmed to cause the method described herein to be performed. In an embodiment, the monitoring module 87 comprises a computing device. In an embodiment, the monitoring module 87 comprises a computer program for providing the functionality herein and encoded on a computer readable medium, the computer readable medium forming the monitoring module 87 or being disposed within the monitoring module 87.

[0187] In an embodiment, similar to using a probe to inspect a substrate Fig.19 Electron beam inspection tools, such as Fig.19 Compared to the CD SEM depicted in the Fig. 20 The electron current in the system of is significantly larger, so that the probe spot is large enough so that the inspection speed can be faster. However, due to the larger probe spot, the resolution may not be as high as that of a CD SEM.

[0188] Can process from e.g. Fig.19 and / or Fig. 20 The SEM images of the system are extracted to extract contours describing the edges of objects representing device structures in the image. These contours are then quantified via an indicator such as CD, typically at a cut line defined by a user. Thus, the images of device structures are compared and quantified, typically via an indicator such as edge-to-edge distance (CD) measured on the extracted contours or a simple pixel difference between the images. Alternatively, the indicator may include an EP gauge as described herein.

[0189] Now, in addition to measuring the substrate during the patterning process, it is often desirable to use one or more tools to produce results that can be used, for example, to design, control, monitor, etc. the patterning process. In order to perform such operations, one or more tools for computationally controlling, designing, etc. one or more aspects of the patterning process, such as pattern design for a patterning device (including, for example, adding sub-resolution assist features or optical proximity effect correction), illumination for a patterning device, etc., can be provided. Therefore, in a system for computationally controlling, designing, etc. a manufacturing process involving patterning, the main manufacturing system components and / or processes can be described by various functional modules. Specifically, in an embodiment, one or more mathematical models describing one or more steps and / or devices of the patterning process (typically including the pattern transfer step) can be provided. In an embodiment, a simulation of the patterning process can be performed using one or more mathematical models to simulate how the patterning process uses the measured pattern or designed pattern provided by the patterning device to form a patterned substrate.

[0190] Although the concepts disclosed herein may be used for imaging on substrates such as silicon wafers, it should be understood that the disclosed concepts may be applicable with any type of lithography imaging system, for example, a lithography imaging system for imaging on substrates other than silicon wafers.

[0191] The description herein is intended to be illustrative rather than limiting. Accordingly, those skilled in the art will appreciate that modifications may be made to the described invention without departing from the scope of the claims set forth below.

[0192] Fig.21 is a diagram of an original image and an average image of a printed pattern according to an embodiment.

[0193] In addition, the present disclosure describes acquiring and analyzing a large number of images of a printed pattern to perform high-quality metrology as part of a lithography process. Fig.21 As shown, the average image 2110 (shown above left) can show more clearly defined structures than the original image 2120 (shown above right). An example of how this difference can be quantified is illustrated by two graphs (2130, 2140) of signal-to-noise ratio as shown in the following images. In both graphs, the peaks in the signal-to-noise ratio can be seen as generally corresponding to the edges of the features appearing in the above images (2110, 2120). However, comparing the two graphs, it is shown that the graph 2140 corresponding to the original image 2120 contains increased noise, while the graph 2130 corresponding to the average image 2110 has a reduced noise level.

[0194] Fig. 22 is a graph showing "good" and "bad" average images of printed patterns according to an embodiment.

[0195] Characterizing the accuracy of the lithography process may include analyzing the average image. For example, on a printed pattern, some structural features may be repeated at different locations. Structural features may include, for example, channels, ridges, grooves, assist features, sub-resolution assist features, edge configurations, vertical slopes, etc. As previously described in Fig.21 As shown in , the raw images of the printed pattern may be averaged and thereby may have a generally improved image quality across the individual images. However, this may not be sufficient to generate an average image that is useful or desirable for performing accurate measurements. For example, if the raw images are misaligned, the resulting average image may not represent the true structural configuration of the printed pattern. Conversely, if even after averaging, the average image is still assessed as insufficient as a usable average image, this may indicate a defect in the printed pattern, an error in the imaging process, etc.

[0196] exist Fig. 22 Examples of "good" and "bad" average images are shown in Fig. 22As shown, any number of raw images may be acquired using an image capture device, which may include at least one of a scanning electron microscope, an atomic force microscope, and an electron beam inspection system. Once the raw images are acquired, an average image of the printed pattern may be generated by averaging the raw images of the printed pattern. Fig. 22 In the example of , the average image 2220 is viewed as being sharper and exhibiting a higher contrast than the single original image 2210, and thus may provide an improved image for performing metrology. For example, a horizontal line across a feature shown in the average image 2220 may represent a cut line 2230. Thus, a critical dimension measurement may be performed along the cut line 2230.

[0197] In contrast, Fig. 22 An example of a "bad" average image 2250 is shown in the lower half of FIG. Here, the example average image 2250 is generated based on three original images 2240, some of which contain printing defects. Therefore, the bad average image 2250 does not contain the clear and reliable features shown in the good average image 2220 that the printing does not have defects.

[0198] There may be other sources of error that can result in a "bad" average image, such as misalignment or misalignment during image acquisition or analysis. As part of generating the average image, a method may include aligning the raw images and generating the average image from the aligned raw images. The method of alignment may include, for example, those described elsewhere herein, particularly with reference to Figure 4 and Figure 5 And those methods described.

[0199] Fig.23 is a process flow diagram illustrating an exemplary implementation of an image quality grading model according to an embodiment.

[0200] A computer-based model can be used to determine in an objective and quantitative manner whether an average image has sufficient quality for measurement or any other part of a lithography process. As a simplified explanation, the model can receive an average image as an input and can output, for example, "true" or "false" when it is determined that the average image is good or bad. For example, a method for evaluating an image of a printed pattern can be implemented by at least one programmable processor and can include obtaining an average image of the printed pattern, where the average image can be generated by averaging original images of the printed pattern. The method can also include identifying one or more features of the average image and evaluating the average image by executing an image quality grading model (also referred to herein as a "model"). The model evaluation can be based at least on features in the average image, and can determine whether the average image meets a criterion.

[0201] The features of the first average image evaluated by the image quality grading model may include at least one of edges, auxiliary features, and sub-resolution auxiliary features. In addition, the indicator may be based on at least one or more of signal-to-noise ratio, contrast, cost (e.g., cost as calculated by a cost function), and alignment quality, wherein the indicator corresponds to the original images averaged to generate the average image. For example, based on the signal-to-noise ratio associated with the edge (feature) (as an indicator), the model may determine whether the average image is good (e.g., true or false).

[0202] Fig.23 The process flow diagram shown in provides one example of a sequence of operations that may evaluate an average image, generate an improved average image, or modify the operation of another component of a lithography or metrology system (eg, a SEM).

[0203] At 2310, an original image (eg, as illustrated by 2120, 2210, 2240) may be acquired from a printed pattern.

[0204] At 2320, a selection of original images can be used to generate an average image. The selection can be made from some or all of the original images.

[0205] At 2330, features of the average image (eg, edges, size, shape, etc. of auxiliary features) may be identified and input into a model for use in evaluating the average image.

[0206] At 2340, the model may be applied using the original image (and associated features) as input to assess whether the average image is good or bad.

[0207] At 2350, the method may also include generating a tag for at least one original image. The tag may indicate whether the first average image meets the criterion. In addition, the method may include associating the tag with at least one original image in a computer memory. As used herein, a "tag" may include any numeric or Boolean value, metadata, database entry, annotation, etc., which may be applied directly to an original image to tag it, or may be generally associated with a specific image (e.g., by using a relational database, pointers, etc.). For example, when an average image is generated and determined by the model to be good, then all original images used to generate the average image may be labeled "good." Return to Fig. 22 For example, three original images that produced a good average image will then be labeled as "good". Conversely, three original images that produced a bad average image will then be labeled as "bad". In this way, a large number of original images can be graded by the model and labeled as being useful for generating reliable average images.

[0208] Thus, in some embodiments, the label may be based on the output of a machine learning algorithm (e.g., as further described below), and may also be at least one of a score or a Boolean value indicating whether the average image meets the criterion. An example of a score that may be generated by the model may be a numeric value, where a positive value indicates that the average image is good, a negative value indicates that the average image is bad, and zero indicates that the model cannot determine whether the average image is good or bad.

[0209] At 2360, the above-mentioned marking can further enable another embodiment, which can include generating a second average image that meets the criterion, thereby being a "good" image. Here, the generating can include averaging the second group of original images of the printed pattern marked as meeting the criterion. Because the second group of original images were previously marked as "good", the average of these original images will also be good.

[0210] In some embodiments, the image quality grading model may include, or be part of, a machine learning algorithm. Thus, it may be determined whether the average image is good or bad based on the output of the machine learning algorithm. Examples of machine learning algorithms that may be implemented may include, but are not limited to, supervised learning algorithms, such as neural network algorithms, nearest neighbor algorithms, naive Bayes algorithms, decision tree algorithms, linear regression algorithms, and support vector machine algorithms. In some embodiments, the model may be trained using a data set that identifies at least one example of an average image that does not meet the criteria.

[0211] In other embodiments, the machine learning algorithms may include unsupervised learning algorithms, such as k-means clustering and association rule algorithms, alone or in combination.

[0212] In some embodiments, the method may include generating a residual image corresponding to one of the original images by subtracting at least one of the first original images from the average image, for example at 2370. In this way, the determination may be further made based on the average image and the residual image.

[0213] In other embodiments, at 2380, some methods disclosed herein may include providing active feedback during operation of the image capture device to improve at least one of the focus of the image capture device and the contrast of the raw images acquired by the image capture device. For example, when the model analyzes the raw images and determines that many or all of the raw images result in a poor average image, then this information may be used to modify the operation of, for example, the SEM so that the acquired raw images may be improved in some aspect (e.g., automatically improving the operation of the image capture device). In this manner, the disclosed subject matter provides methods that may be used to iteratively refine the acquisition of raw images to provide the best possible set of raw images for analysis using the model.

[0214] Fig.24 is a process flow diagram illustrating an exemplary image quality grading model for iteratively improving an average image in accordance with an embodiment.

[0215] In some embodiments, instead of or in addition to evaluating an average image and labeling original images, some methods may provide the best possible average image based on the available original images. Fig.24 Such an exemplary implementation is shown in , where, at 2410, a raw image can be acquired and input into a computational process (eg, similar to the acquisition of raw images described elsewhere herein).

[0216] At 2420, a check may be performed to determine if a maximum number of iterations has been reached. The maximum number of iterations may be specified based on user generated input or may be automatically determined by a computer program.

[0217] When the maximum number of iterations has not been reached, the model 2430 may then (at 2432) generate a configuration that includes one possible combination of raw images obtained from the printed pattern. For example, the configuration may include a list of locations in the printed pattern where the raw images were obtained. In another embodiment, the configuration may include a subset of available raw images that may be used to generate the candidate average image (e.g., as a specific combination of raw images).

[0218] Contrast (e.g., a value or expression that quantifies the contrast in the candidate average image) can be calculated. In this way, the candidate average images can be compared to each other and to the model, or in other embodiments, the model can use contrast as an input to affect the model output.

[0219] Similarly, in other embodiments, an alignment quality may be calculated for the configuration. In such embodiments, the alignment quality may represent the degree to which the raw images that make up the candidate image are aligned (such as by any alignment process described herein). The alignment quality may include calculating coordinate differences, standard deviations, etc. between corresponding points or aspects (e.g., corresponding EP gauges, CDs, etc.) of the raw images. In other embodiments, the alignment quality may include calculating a correlation coefficient between the raw images, for example indicating that a highly correlated set of raw images has a high alignment quality.

[0220] At 2434, the model 2430 may determine whether the average image is good or bad, for example based at least in part on one or more of contrast or alignment quality. When the average image is bad, the process may continue with another iteration (determining a new configuration and candidate average image) if the maximum number of iterations has not been reached.

[0221] At 2440, when the candidate average image has been evaluated as "good" by the model 2430, then the process can determine whether the candidate average image is better than the current average image (which is understood to be the current best possible average image). In some embodiments, when the output of the model is a score, then the score (e.g., a higher positive score as described above) can be used to determine that the candidate average image is better. In other embodiments, the determination can be based on calculating the signal-to-noise ratio of the candidate image (which can be from the entire candidate image, or from a specific portion of the candidate image) and comparing it to the current average image. When the candidate average image is not better than the current average image, the candidate average image will not be used, and another iteration may occur.

[0222] At 2450, when the candidate average image is better than the current average image, the current average image may be replaced by the candidate average image.

[0223] At 2460, once the maximum number of iterations has been reached, a check may be performed to determine if there is currently an established best average image.

[0224] At 2470, when there is no established best average image, a list can be stored in computer memory that identifies the original images in the current configuration and associates the configuration with the average image (which can also be stored). For example, the list can be an array, a set of pointers, a database entry, a data file, etc.

[0225] At 2480, when there is an established best average image, but the updated best average image (e.g., as determined at 2420 to 2450) is better than the best average image, then the updated best average image is saved to the computer memory and the corresponding list may also be saved.

[0226] At 2490, the original image associated with the updated best average image may be labeled as described herein.

[0227] The embodiments may be further described using the following aspects:

[0228] 1. A method for improving a process model for a patterning process, the method comprising:

[0229] obtaining a) a measured profile from an image capture device, and b) a simulated profile resulting from a simulation of the process model;

[0230] aligning the measured profile with the simulated profile by determining an offset between the measured profile and the simulated profile; and

[0231] The process model is calibrated to reduce a difference between the simulated profile and the measured profile calculated based on the determined offset.

[0232] 2. The method according to clause 1, wherein the offset is further determined based on measurement coordinates that substantially define a portion of the measurement profile.

[0233] 3. The method according to aspect 2, wherein the offset is further determined based on a distance between the measured coordinate and the simulated contour, the distance being a distance at the measured coordinate along a direction perpendicular to the measured contour.

[0234] 4. The method according to clause 3, the aligning further comprising reducing a cost function calculated based on the distance.

[0235] 5. The method according to any preceding aspect, further comprising generating edge placement (EP) coordinates on the measured profile, and wherein the offset is further determined based on the EP coordinates.

[0236] 6. The method of clause 5, wherein the EP coordinates are generated by interpolating between two or more measured coordinates.

[0237] 7. The method of clause 6, wherein the EP coordinates are generated by extrapolation from two or more measured coordinates.

[0238] 8. The method of any preceding aspect, the calibrating further comprising modifying a feature of the process model to reduce the difference, the modification causing a change in the shape of the simulated profile.

[0239] 9. A method according to any preceding clause, wherein the measurement profile is identified based on changes in intensity of pixels in a measurement image.

[0240] 10. The method of clause 9, wherein the identifying is performed based on the change exceeding a grayscale threshold.

[0241] 11. The method according to any preceding aspect, further comprising:

[0242] Obtaining the simulation contour from a Graphics Database System (GDS) polygon; and

[0243] The edge placement coordinates or the measurement coordinates including the measurement profile are converted into GDS coordinates.

[0244] 12. The method of clause 11, wherein the GDS polygons are capable of being in one or more formats selected from the group consisting of GDS Streaming Format (GDSII) and Open Artifact Systems Interchange Standard (OASIS).

[0245] 13. A method for improving an optical proximity correction (OPC) model for a patterning process, the method comprising:

[0246] obtaining a) a measured profile from an image capture device, and b) a simulated profile resulting from a simulation of the OPC model;

[0247] aligning the measured profile with the simulated profile by determining an offset between the measured profile and the simulated profile; and

[0248] Features of the OPC model are modified to reduce a difference between the simulated profile and the measured profile calculated based on the determined offset.

[0249] 14. The method of aspect 13, wherein the characteristic comprises one or more of diffusion rate, diffusion range, deprotection ratio, and acid / base concentration.

[0250] 15. The method according to any one of aspects 13 to 14, further comprising:

[0251] The simulated profile is obtained based on the simulation of the OPC model, wherein the OPC model is a preliminary model including an optical model and excluding a resist model.

[0252] 16. The method according to any one of aspects 13 to 15, further comprising:

[0253] obtaining an initial simulation profile using a preliminary model including an optical model and a resist model;

[0254] Features of the resist model are modified to reduce a difference between the initial simulated profile and the measured profile.

[0255] 17. A method for improving a process model for a patterning process, the method comprising:

[0256] obtaining a) a plurality of measurement images from an image capture device, and b) a simulated profile generated from a simulation of the process model;

[0257] aligning the plurality of measurement images;

[0258] generating a combined measurement image from the aligned plurality of measurement images;

[0259] extracting a measurement profile from the combined measurement image by an image analysis method;

[0260] aligning the measured profile with the simulated profile by determining an offset between the measured profile and the simulated profile; and

[0261] The process model is calibrated to reduce a difference between the simulated profile and the measured profile calculated based on the determined offset.

[0262] 18. A method according to clause 17, wherein the combined image is generated by averaging the aligned plurality of measurement images.

[0263] 19. A method according to any one of clauses 17 to 18, wherein the plurality of measurement images are obtained from printed patterns from at least two different dies made from a target pattern.

[0264] 20. A method according to any of clauses 17 to 19, wherein each of the plurality of measurement images producing the combined image is acquired by scanning a different die.

[0265] 21. A method according to any one of aspects 17 to 20, wherein the image capture device is a scanning electron microscope.

[0266] 22. The method of clause 21, wherein obtaining the plurality of measurement images is performed by scanning an electron beam across the printed pattern at a plurality of angles.

[0267] 23. The method of aspect 22, wherein the plurality of angles includes approximately +45 degrees and -45 degrees.

[0268] 24. The method according to clause 22 or 23, wherein half of the plurality of measurement images are scanned at approximately +45 degrees and the other half of the plurality of measurement images are scanned at approximately -45 degrees.

[0269] 25. The method of any one of clauses 21 to 24, wherein the obtaining is performed using the scanning electron microscope operating at a dose lower than a dose required to obtain a scan sufficient to resolve a critical dimension.

[0270] 26. A method according to any one of aspects 17 to 25, wherein the image capture device is an electron beam inspection system.

[0271] 27. The method of clause 26, wherein the electron beam inspection system has a large field of view, and the plurality of measurement images are obtained at least in part from within the large field of view.

[0272] 28. The method of clause 27, wherein the large field of view is approximately 1 to 50 microns on a side.

[0273] 29. A method according to clause 27 or 28, wherein the large field of view is approximately 6 to 12 microns on a side.

[0274] 30. The method of any one of aspects 26 to 29, further comprising using the electron beam inspection system to detect hot spots or weak points in a printed pattern.

[0275] 31. The method according to any one of aspects 17 to 30, further comprising:

[0276] determining a common area in the plurality of measurement images captured from the image capture device; and

[0277] The combined measurement image is generated based on the common area.

[0278] 32. A method for evaluating an image of a printed pattern, the method being implemented by at least one programmable processor and comprising:

[0279] Obtaining a first average image of the printed pattern, wherein the first average image is generated by averaging a plurality of first original images of the printed pattern;

[0280] identifying one or more features of the first average image; and

[0281] Executing the image quality grading model by the at least one programmable processor and evaluating the first average image based at least on the one or more features, the evaluating comprising:

[0282] Whether the first average image meets the indicator is determined by the image quality grading model.

[0283] 33. The method of clause 32, wherein the first average image is generated by at least aligning the plurality of original images and generating the first average image from the aligned plurality of original images.

[0284] 34. The method according to aspect 32, further comprising:

[0285] generating a mark for at least one of the plurality of original images, the mark indicating whether the first average image satisfies the indicator; and

[0286] The tag is associated in a computer memory with at least one original image of the plurality of original images.

[0287] 35. The method according to aspect 34, further comprising:

[0288] Generating a second average image, the generating comprising:

[0289] A plurality of second original images of the printed pattern marked as satisfying the indicator are averaged, wherein a second averaged image satisfies the indicator.

[0290] 36. The method of clause 32, further comprising training the image quality grading model using a data set that identifies at least one example of a first average image that does not meet the metric.

[0291] 37. The method according to aspect 32, further comprising:

[0292] generating a residual image corresponding to one of the plurality of first original images by subtracting at least one of the plurality of first original images from the first average image, and

[0293] Wherein, the determination is also based on the first average image and the residual image.

[0294] 38. The method of clause 32, wherein the one or more features of the first average image evaluated by the image quality grading model include at least one of an edge, an assist feature, and a sub-resolution assist feature.

[0295] 39. The method of clause 32, wherein the indicator is based on at least one or more of a signal-to-noise ratio, a contrast ratio, and an alignment quality, and the indicator corresponds to a plurality of first original images that are averaged to generate an average image.

[0296] 40. The method of clause 34, the image quality grading model further comprising a machine learning algorithm, wherein the determining is based on an output of the machine learning algorithm.

[0297] 41. A method according to clause 34, wherein the label is based on the output of the machine learning algorithm and is at least one of a score or a Boolean value indicating whether the first average image meets the criterion.

[0298] 42. The method of aspect 34, wherein the machine learning algorithm comprises at least one of a neural network algorithm, a nearest neighbor algorithm, a naive Bayes algorithm, a decision tree algorithm, a linear regression algorithm, and a support vector machine algorithm.

[0299] 43. The method of clause 34, wherein the machine learning algorithm comprises at least one of a k-means clustering and an association rule algorithm.

[0300] 44. The method of aspect 32, wherein the plurality of first original images or the plurality of second original images are acquired using an image capture device, the image capture device comprising at least one of a scanning electron microscope, an atomic force microscope, and an electron beam inspection system.

[0301] 45. The method according to aspect 44, further comprising:

[0302] Active feedback is provided during operation of an image capture device to improve at least one of focus of the image capture device and contrast of a raw image acquired by the image capture device.

[0303] 46. ​​A computer program product comprising a non-transitory computer-readable medium having instructions recorded thereon, wherein the instructions implement the method of any one of the preceding aspects when executed by a computer.

Claims

1. A method for evaluating an image of a printed pattern, the method being implemented by at least one programmable processor and comprising: Obtaining a first average image of the printed pattern, wherein the first average image is generated by averaging a plurality of first original images of the printed pattern; identifying one or more features of the first average image; as well as The first average image is evaluated by executing an image quality grading model by the at least one programmable processor, at least the one or more features being input into the image quality grading model, the evaluation comprising: Whether the first average image meets the indicator is determined by the image quality grading model. 2 . The method of claim 1 , wherein the first average image is generated by at least aligning the plurality of original images and generating the first average image from the aligned plurality of original images.

3. The method according to claim 1, further comprising: generating a mark for at least one original image among the plurality of original images, the mark indicating whether the first average image satisfies the indicator; as well as The tag is associated in a computer memory with at least one original image of the plurality of original images.

4. The method according to claim 3, further comprising: Generating a second average image, the generating comprising: A plurality of second original images of the printed pattern marked as satisfying the indicator are averaged, wherein the second average image satisfies the indicator.

5. The method according to claim 1, further comprising: The image quality grading model is trained using a data set that identifies at least one example of the first average image that does not meet the metric.

6. The method according to claim 1, further comprising: generating a residual image corresponding to one of the plurality of first original images by subtracting at least one of the plurality of first original images from the first average image, and Wherein, the determination is also based on the first average image and the residual image.

7. The method of claim 1, wherein the one or more features of the first average image evaluated by the image quality grading model include at least one of edges, assist features, and sub-resolution assist features.

8. The method of claim 1, wherein the indicator is based on at least one or more of a signal-to-noise ratio, a contrast, and an alignment quality, and the indicator corresponds to the plurality of first original images averaged to generate the first average image.

9. The method of claim 3, the image quality grading model further comprising a machine learning algorithm, wherein the determining is based on an output of the machine learning algorithm.

10. The method of claim 9, wherein the label is based on an output of the machine learning algorithm and is at least one of a score or a Boolean value indicating whether the first average image meets the criterion.

11. The method according to claim 9, wherein the machine learning algorithm comprises at least one of a neural network algorithm, a nearest neighbor algorithm, a naive Bayes algorithm, a decision tree algorithm, a linear regression algorithm, and a support vector machine algorithm.

12. The method of claim 9, wherein the machine learning algorithm comprises at least one of a k-means clustering and an association rule algorithm.

13. The method of claim 1, wherein the plurality of first original images or the plurality of second original images are acquired using an image capture device, the image capture device comprising at least one of a scanning electron microscope, an atomic force microscope, and an electron beam inspection system.

14. The method according to claim 13, further comprising: Active feedback is provided during operation of the image capture device to improve at least one of focus of the image capture device and contrast of a raw image acquired by the image capture device.

15. A computer program product comprising a non-transitory computer-readable medium having recorded thereon instructions, the instructions implementing the method according to claim 1 when executed by a computer.

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