Region density-based alignment reject ratio for image alignment

Abstract

An improved method and system for inspecting image alignment of an image is disclosed. The improved method and system includes an alignment reject ratio based on applying a density function to both the sample image and the reference image at the region of interest. One or more metrics, such as cross-correlation of a sample and a reference image, may be used to derive a region density-based alignment reject ratio from the density function image. This index may produce a unique alignment solution that is stable to noise and other errors.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application 63 / 397,201, filed on August 11, 2022, and the entire contents of which are incorporated herein by reference. Technical Field

[0003] Embodiments provided herein relate to image alignment techniques, and more particularly, to a misalignment rate for characterizing the amount of misalignment of image features. Background Art

[0004] During the manufacture of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to design and are free of defects. Inspection systems utilizing optical microscopes or charged particle (e.g., electron) beam microscopes such as scanning electron microscopes (SEMs) may be employed. As the physical size of IC components continues to shrink, the accuracy and yield of defect detection become more important. Inspection images such as SEM images may be used to identify or classify defect(s) of the manufactured IC. In order to improve defect detection performance, it is desirable to obtain precise alignment between the SEM image and the corresponding design layout data. Summary of the invention

[0005] Embodiments of the present disclosure provide an image alignment method. The method may include: using a first density function to acquire a first density map of an inspection image corresponding to a region of interest; using a second density function to acquire a second density map of a reference image corresponding to the region of interest; and aligning the inspection image and the reference image based on the acquired first density map and second density map.

[0006] Some embodiments of the present disclosure provide a non-transitory computer-readable medium storing an instruction set, where the instruction set is implemented by at least one processor of a device to cause the device to execute the above method.

[0007] Some embodiments of the present invention provide a charged particle beam device configured to perform the above method. The charged particle beam device may include: a charged particle beam source configured to generate a primary charged particle beam; a charged particle optical system configured to guide the primary charged particle beam at a sample surface so as to inspect the sample surface; a charged particle detector configured to detect charged particles returned from the sample surface, wherein the charged particle detector includes a first sensing element and a second sensing element; and a controller including one or more processors and configured to cause the charged particle beam device to perform the above method.

[0008] Some embodiments of the present disclosure provide another image alignment method. The method may include: acquiring an inspection image corresponding to an area of ​​interest of a sample; acquiring a reference image corresponding to the area of ​​interest of the sample; using a first binary function to acquire an inspection binary contour of the inspection image; using a second binary function to acquire a reference binary contour of the reference image; using a first density function to acquire a first density map of the inspection binary contour; using a second density function to acquire a second density map of the reference binary contour; and determining alignment parameters of the inspection image and the reference image based on the acquired first density map and second density map.

[0009] Some embodiments of the present disclosure provide a non-transitory computer-readable medium storing an instruction set, where the instruction set can be executed by at least one processor of a device to cause the device to perform the above-mentioned another method.

[0010] Some embodiments of the present invention provide a charged particle beam device configured to perform the above method. The charged particle beam device may include: a charged particle beam source configured to generate a primary charged particle beam; a charged particle optical system configured to guide the primary charged particle beam at a sample surface to inspect the sample surface; a charged particle detector configured to detect charged particles returned from the sample surface, wherein the charged particle detector includes a first sensing element and a second sensing element; and a controller including one or more processors and configured to cause the charged particle beam device to perform the above another method. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other aspects of the present disclosure will become more apparent from the description of exemplary embodiments with reference to the accompanying drawings.

[0012] Figure 1 is a schematic diagram illustrating an exemplary charged particle beam inspection system according to an embodiment of the present disclosure.

[0013] Figure 2 It is a diagram illustrating that the embodiment according to the present disclosure may be Figure 1 Schematic diagram of an exemplary multi-beam tool that is a portion of an exemplary charged particle beam inspection system.

[0014] FIG. 3A to FIG. 3D An example technique for generating a misregistration rate is illustrated.

[0015] Figure 4 An example technique for generating a misregistration rate according to an embodiment of the present disclosure is illustrated.

[0016] Figure 5 An example technique for generating a misregistration rate according to an embodiment of the present disclosure is illustrated.

[0017] FIG. 6A to FIG. 6B An example technique for generating a misregistration rate according to an embodiment of the present disclosure is illustrated.

[0018] Figure 7 The misregistration rate performance according to an embodiment of the present disclosure is illustrated.

[0019] Figure 8 is a process flow diagram representing an exemplary method for generating a misregistration rate according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] Reference is now made in detail to example embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, wherein, unless otherwise specified, the same reference numerals in different drawings represent the same or similar elements. The implementations set forth in the following description of the example embodiments do not represent all implementations. Instead, they are merely examples of apparatus and methods that conform to aspects related to the disclosed embodiments as described in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the present disclosure is not limited thereto. Similarly, other types of charged particle beams may be applied. Further, other imaging systems such as optical imaging, photon detection, x-ray detection, etc. may be used.

[0021] Electronic devices consist of circuits formed on a piece of semiconductor material called a substrate. Semiconductor materials may include, for example, silicon, gallium arsenide, indium phosphide, silicon germanium, etc. Many circuits may be formed together on the same silicon wafer and are referred to as integrated circuits or ICs. The size of these circuits has been significantly reduced so that more circuits can be fitted on a substrate. For example, an IC chip in a smartphone may be as small as a thumb cap and may also include more than 2 billion transistors, each of which is less than 1 / 1000 the size of a human hair.

[0022] Manufacturing these ICs with extremely small structures or components is a complex, time-consuming and expensive process, often involving hundreds of individual steps. An error in even one step can result in a defect in the finished IC, rendering it useless. Therefore, one goal of the manufacturing process is to avoid such defects in order to maximize the number of functional ICs manufactured in the process, i.e., to increase the overall yield of the process.

[0023] An integral part of improving yield is monitoring the chip manufacturing process to ensure that it is producing a sufficient number of functional integrated circuits. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. The inspection can be performed using a scanning charged electron microscope (SCPM). For example, the SCPM can be a scanning electron microscope (SEM). The SCPM can be used to image these extremely small structures, in effect taking a "photo" of the structure of the wafer. The image can be used to determine whether the structure was formed correctly and in the correct location. If the structure is defective, the process can be adjusted to make the defect less likely to occur again.

[0024] As the physical size of IC components continues to shrink, the accuracy and yield of defect detection become more important. Inspection images such as SEM images can be used to identify or classify one or more defects of the manufactured IC. In order to improve defect detection performance, it is desirable to obtain accurate alignment between the SEM image and the corresponding reference image. For example, in die-to-database (D2DB) alignment, the SEM image can be compared with the reference image based on the design layout data. In die-to-die (D2D) alignment, a defect-free SEM image can be used as a reference for aligning other SEM images with the same design layout. The difference between the SEM and reference images can be used to calculate the misalignment rate.

[0025] Some conventional alignment methods rely on comparing the positions of pattern edges to determine the degree of misalignment between the SEM and reference images. These methods may not perform well for complex misalignments, such as scaling or deformation of the SEM image relative to the reference image. Further, some mathematical techniques that combine the SEM and reference images, such as cross-correlation, may be sensitive to measurement noise. This may lead to inaccuracies in calculating the misalignment rate, such as generating many possible solutions to a single alignment problem.

[0026] Embodiments of the present invention may provide a misalignment rate based on the regional density of pattern features. The process for calculating the misalignment rate may involve: transforming the SEM image and the reference image using a density function to produce an image of pattern features that are weighted more heavily at its center than at the edge. These transformed regional density-based SEM and reference images may then be evaluated using, for example, the cross-correlation mentioned above to achieve misalignment measurements that are stable to measurement noise and produce a single alignment solution. The transformed regional density-based SEM and reference images may also be used to generate a number of other metrics that may then be combined into a regional density-based misalignment rate.

[0027] For the sake of clarity, the relative size of the parts in the drawings may be enlarged. In the following description of the drawings, the same or similar reference numerals refer to the same or similar parts or entities, and only describe the difference about individual embodiments. As used herein, unless otherwise specifically stated, the term "or" covers all possible combinations, otherwise it is not feasible. For example, if it is stated that a part can include A or B, then unless otherwise specifically stated or not feasible, the part can include A or B, or A and B. As a second example, if it is stated that a part can include A, B or C, then unless otherwise specifically stated or not feasible, the part can include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0028] Figure 1 An example electron beam inspection (EBI) system 100 is illustrated according to an embodiment of the present disclosure. The EBI system 100 can be used for imaging. Figure 1 As shown, the EBI system 100 includes a main chamber 101, a load / lock chamber 102, a beam tool 104, and an equipment front end module (EFEM) 106. The beam tool 104 is located in the main chamber 101. The EFEM 106 includes a first load port 106a and a second load port 106b. The EFEM 106 may include (multiple) additional load ports. The first load port 106a and the second load port 106b can receive a front opening wafer transport box (FOUP), which accommodates wafers (e.g., semiconductor wafers or wafers made of (multiple) other materials) or samples to be inspected (wafers and samples can be used interchangeably). A "lot" is a plurality of wafers that can be loaded for processing as a batch.

[0029] One or more robots (not shown) in the EFEM 106 can transport the wafer to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) that removes gas molecules in the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, the one or more robots (not shown) can transport the wafer from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pump system (not shown) that removes gas molecules in the main chamber 101 to reach a second pressure lower than the first pressure. After reaching the second pressure, the wafer is inspected by the beam tool 104. The beam tool 104 can be a single beam system or a multi-beam system.

[0030] The controller 109 is electrically connected to the beam tool 104. The controller 109 may be a computer configured to implement various controls of the EBI system 100. Figure 1106, but it should be appreciated that the controller 109 may be part of the structure.

[0031] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or special electronic device capable of manipulating or processing information. For example, the processor may include any number of any combination of the following: a central processing unit (or "CPU"), a graphics processing unit (or "GPU"), an optical processor, a programmable logic controller, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a programmable logic array (PLA), a programmable array logic (PAL), a general array logic (GAL), a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a system on a chip (SoC), an application-specific integrated circuit (ASIC), and any type of circuit capable of data processing. The processor may also be a virtual processor comprising one or more processors distributed across multiple machines or devices coupled via a network.

[0032] In some embodiments, the controller 109 may also include one or more memories (not shown). The memory may be a general or special electronic device capable of storing code and data accessible by a processor (e.g., via a bus). For example, the memory may include any combination of any number of the following: random access memory (RAM), read-only memory (ROM), optical disk, disk, hard drive, solid-state drive, flash drive, secure digital (SD) card, memory stick, compact flash (CF) card, or any type of storage device. The code and data may include an operating system (OS) and one or more applications (or "apps") for a specific task. The memory may also be a virtual memory comprising one or more memories distributed across multiple machines or devices coupled via a network.

[0033] Figure 2 An example multi-beam tool 104 (also referred to herein as an apparatus 104) and a plurality of beam tools 104 that may be configured for use with an EBI system 100 ( Figure 1 ) is a schematic diagram of an image processing system 290.

[0034] The beam tool 104 may include a charged particle source 202, a gun aperture 204, a converging lens 206, a primary charged particle beam 210 emitted from the charged particle source 202, a source conversion unit 212, a plurality of beams 214, 216, and 218 of the primary charged particle beam 210, a primary projection optical system 220, a motorized wafer stage 280, a wafer holder 282, a plurality of secondary charged particle beams 236, 238, and 240, a secondary optical system 242, and a charged particle detection device 244. The primary projection optical system 220 may include a beam splitter 222, a deflection scanning unit 226, and an objective lens 228. The charged particle detection device 244 may include detection sub-areas 246, 248, and 250.

[0035] Charged particle source 202, gun aperture 204, focusing lens 206, source conversion unit 212, beam splitter 222, deflection scanning unit 226, and objective lens 228 may be aligned with a primary optical axis 260 of apparatus 104. Secondary optical system 242 and charged particle detection device 244 may be aligned with a secondary optical axis 252 of apparatus 104.

[0036] The charged particle source 202 can emit one or more charged particles, such as electrons, protons, ions, muons, or any other particles carrying an electric charge. In some embodiments, the charged particle source 202 can be an electron source. For example, the charged particle source 202 can include a cathode, an extractor, or an anode, wherein primary electrons can be emitted from the cathode and extracted or accelerated to form a primary charged particle beam 210 (in this case, a primary electron beam) having an intersection (virtual or real) 208. For ease of explanation and without ambiguity, electrons are used as examples in some of the descriptions in this article. However, it should be noted that any charged particle can be used in any embodiment of the present invention, without being limited to electrons. The primary charged particle beam 210 can be visualized as being emitted from the intersection 208. The gun aperture 204 can block the peripheral charged particles of the primary charged particle beam 210 to reduce the Coulomb effect. The Coulomb effect may cause the probe spot size to increase.

[0037] The source conversion unit 212 may include an imaging element array and a beam limiting aperture array. The imaging element array may include a micro deflector or a micro lens array. The imaging element array may form multiple parallel images (virtual images or real images) of the intersection 208 together with the multiple beams 214, 216, and 218 of the primary charged particle beam 210. The beam limiting aperture array may limit the multiple beams 214, 216, and 218. Although Figure 2Three beams 214, 216, and 218 are shown, but embodiments of the present disclosure are not limited thereto. For example, in some embodiments, the apparatus 104 may be configured to generate a first number of beams. In some embodiments, the first number of beams may be in the range of 1 to 1000. In some embodiments, the first number of beams may be in the range of 200 to 500. In an exemplary embodiment, the apparatus 104 may generate 400 beams.

[0038] The converging lens 206 can focus the primary charged particle beam 210. By adjusting the focal length of the converging lens 206 or by changing the radial size of the corresponding beam-limiting apertures in the beam-limiting aperture array, the current of the beams 214, 216, and 218 downstream of the source conversion unit 212 can be changed. The objective lens 228 can focus the beams 214, 216, and 218 onto the wafer 230 for imaging, and can form a plurality of probe spots 270, 272, and 274 on the surface of the wafer 230.

[0039] The beam splitter 222 can be a Wien filter type beam splitter that generates an electrostatic dipole field and a magnetic dipole field. In some embodiments, if the electrostatic dipole field and the magnetic dipole field are applied, the force exerted by the electrostatic dipole field on the charged particles of the beam waves 214, 216, and 218 can be equal in magnitude and opposite in direction to the force exerted by the magnetic dipole field on the charged particles. Therefore, the beam waves 214, 216, and 218 can pass directly through the beam splitter 222 at a zero deflection angle. However, the total dispersion of the beam waves 214, 216, and 218 generated by the beam splitter 222 can also be non-zero. The beam splitter 222 can separate the secondary charged particle beams 236, 238, and 240 from the beam waves 214, 216, and 218, and guide the secondary electron beams 236, 238, and 240 toward the secondary optical system 242.

[0040] The deflection scanning unit 226 can deflect the beams 214, 216, and 218 to scan the probe spots 270, 272, and 274 over the surface area of ​​the wafer 230. In response to the beams 214, 216, and 218 being incident at the probe spots 270, 272, and 274, secondary charged particle beams 236, 238, and 240 can be emitted from the wafer 230. The secondary charged particle beams 236, 238, and 240 can include charged particles (e.g., electrons) having an energy distribution. For example, the secondary charged particle beams 236, 238, and 240 can be secondary electron beams including secondary electrons (energy ≤ 50 eV) and backscattered electrons (energy between 50 eV and the landing energy of the beams 214, 216, and 218). The secondary optical system 242 can focus the secondary charged particle beams 236, 238, and 240 onto detection sub-regions 246, 248, and 250 of the charged particle detection device 244. The detection sub-regions 246, 248, and 250 can be configured to detect the corresponding secondary charged particle beams 236, 238, and 240 and generate corresponding signals (e.g., voltages, currents, etc.) that are used to reconstruct the SCPM image of the structures on or below the surface area of ​​the wafer 230.

[0041] The generated signals may represent the intensities of the secondary charged particle beams 236, 238, and 240, and may be provided to an image processing system 290 that communicates with the charged particle detection device 244, the primary projection optical system 220, and the motorized wafer stage 280. The movement speed of the motorized wafer stage 280 may be synchronized and coordinated with the beam deflection controlled by the deflection scanning unit 226, so that the movement of the scanning probe spots (e.g., scanning probe spots 270, 272, and 274) may orderly cover the region of interest on the wafer 230. The parameters of such synchronization and coordination may be adjusted to accommodate different materials of the wafer 230. For example, different materials of the wafer 230 may have different resistance-capacitance characteristics, which may cause different signal sensitivities to the movement of the scanning probe spots.

[0042] The intensity of the secondary charged particle beams 236, 238, and 240 may vary depending on the external structure or internal structure of the wafer 230, and thus may indicate whether the wafer 230 includes a defect. Moreover, as discussed above, the beams 214, 216, and 218 may be projected onto different locations on the top surface of the wafer 230 or onto different sides of the local structure of the wafer 230 to generate secondary charged particle beams 236, 238, and 240 with different intensities. Therefore, by mapping the intensities of the secondary charged particle beams 236, 238, and 240 using the region of the wafer 230, the image processing system 290 may reconstruct an image reflecting the characteristics of the internal structure or external structure of the wafer 230.

[0043] In some embodiments, the image processing system 290 may include an image acquirer 292, a storage device 294, and a controller 296. The image acquirer 292 may include one or more processors. For example, the image acquirer 292 may include a computer, a server, a mainframe, a terminal, a personal computer, any type of mobile computing device, etc., or a combination thereof. The image acquirer 292 may be communicatively coupled to the charged particle detection device 244 of the beam tool 104 via a medium such as an electrical conductor, an optical fiber cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, radio, or a combination thereof. In some embodiments, the image acquirer 292 may receive a signal from the charged particle detection device 244 and may construct an image. Thus, the image acquirer 292 may acquire an SCPM image of the wafer 230. The image acquirer 292 may also perform various post-processing functions, such as generating contours, superimposing indicators on the acquired image, etc. The image acquirer 292 may be configured to perform adjustments to the brightness and contrast of the acquired image. In some embodiments, the storage device 294 may be a storage medium such as a hard disk, a flash drive, a cloud storage device, a random access memory (RAM), other types of computer readable memory, etc. The storage device 294 may be coupled to the image acquirer 292 and may be used to save the scanned raw image data as a raw image, as well as a post-processed image. The image acquirer 292 and the storage device 294 may be connected to a controller 296. In some embodiments, the image acquirer 292, the storage device 294, and the controller 296 may be integrated together as a control unit.

[0044] In some embodiments, the image acquirer 292 may acquire one or more SCPM images of the wafer based on an imaging signal received from the charged particle detection device 244. The imaging signal may correspond to a scanning operation for performing charged particle imaging. The acquired image may be a single image including a plurality of imaging regions. The single image may be stored in the storage device 294. The single image may be an original image that may be divided into a plurality of regions. Each of the regions may include an imaging region containing features of the wafer 230. The acquired image may include a plurality of images of a single imaging region of the wafer 230 sampled multiple times within a time series. The plurality of images may be stored in the storage device 294. In some embodiments, the image processing system 290 may be configured to perform an image processing step on a plurality of images of the same position of the wafer 230.

[0045] In some embodiments, the image processing system 290 may include measurement circuitry (e.g., an analog-to-digital converter) to obtain a distribution of detected secondary charged particles (e.g., secondary electrons). The charged particle distribution data collected during the detection time window, combined with the corresponding scan path data of the beams 214, 216, and 218 incident on the wafer surface, can be used to reconstruct an image of the inspected wafer structure. The reconstructed image can be used to reveal various features of the internal or external structure of the wafer 230, and thus can be used to reveal any defects that may be present in the wafer.

[0046] In some embodiments, the charged particles may be electrons. When the electrons of the primary charged particle beam 210 are projected onto the surface of the wafer 230 (e.g., probe spots 270, 272, and 274), the electrons of the primary charged particle beam 210 may penetrate the surface of the wafer 230 to a certain depth, thereby interacting with the particles of the wafer 230. Some electrons of the primary charged particle beam 210 may interact elastically with the material of the wafer 230 (e.g., in the form of elastic scattering or collision), and may be reflected or recoiled out of the surface of the wafer 230. The elastic interaction preserves the total kinetic energy of the interacting body (e.g., the electrons of the primary charged particle beam 210), wherein the kinetic energy of the interacting body is not converted into other forms of energy (e.g., thermal energy, electromagnetic energy, etc.). Such reflected electrons generated by the elastic interaction may be referred to as backscattered electrons (BSE). Some electrons of the primary charged particle beam 210 may interact inelastically with the material of the wafer 230 (e.g., in the form of inelastic scattering or collision). Inelastic interactions do not preserve the total kinetic energy of the interacting bodies, where some or all of the kinetic energy of the interacting bodies is converted into other forms of energy. For example, through inelastic interactions, the kinetic energy of some electrons of the primary charged particle beam 210 may cause electronic excitation and transitions of atoms of the material. This inelastic interaction may also generate electrons that leave the surface of the wafer 230, which may be referred to as secondary electrons (SE). The yield or emission rate of BSE and SE depends on, for example, the material under test and the landing energy of the electrons of the primary charged particle beam 210 that land on the surface of the material. The electron energy of the primary charged particle beam 210 may be determined in part by its accelerating voltage (e.g., Figure 2 The number of BSEs and SEs may be more or less (or even the same) than the injected electrons of the primary charged particle beam 210.

[0047] In some embodiments, images can be generated by the SEM and used, for example, for defect inspection. For example, an image generated to capture a test device area of ​​a wafer can be compared to a reference image that captures the same test device area. The reference image can be predetermined (e.g., by simulation) and does not include known defects. If the difference between the generated image and the reference image exceeds a tolerance level, a potential defect can be identified. For another example, the SEM can scan multiple areas of a wafer, each area including a test device area designed to be the same, and generate multiple images that capture those test device areas manufactured. Multiple images can be compared to each other. If the difference between the multiple images exceeds the tolerance level, a potential defect can be identified.

[0048] For ease of explanation and without ambiguity, electrons are used as examples in some of the descriptions in this article. However, it should be noted that any charged particle can be used in any embodiment of the present invention, and is not limited to electrons. For example, the source in a charged particle beam tool can emit one or more charged particles, such as electrons, protons, ions, muons, or any other particles carrying an electric charge. The system and method for detection can be used in other imaging systems, such as optical imaging, photon detection, proton detection, x-ray detection, ion detection, etc. Photon detection can include infrared, visible light, UV, DUV, EUV, x-rays, or light within any other wavelength range. Therefore, although the detector in the present disclosure can be disclosed with respect to electronic detection, some embodiments of the present disclosure may involve the use of other detection systems to detect other charged particles or photons.

[0049] FIG. 3A to FIG. 3D A misalignment rate 300 according to a comparative embodiment is illustrated. FIG. 3A to FIG. 3B The misalignment rate is based on grayscale subtraction, while FIG. 3C to FIG. 3D The index of is based on the amount of shift in the pattern edge features.

[0050] exist Figure 3A In the SEM image 360, a reference image 350 is overlapped with a SEM image 360 ​​in a region of interest ROI on the sample surface. The sample may be, for example, a semiconductor wafer. The reference image may be a template (such as a D2DB reference), for example, a pattern design layout (such as a graphic database system (GDS) or other file). FIG. 3A to FIG. 3D The method may not be suitable for D2D methods that use defect-free SEM images as reference images. Figure 3A As shown, the reference image 350 is represented by a set of empty polygons corresponding to the pattern features 365 of the SEM image 360 ​​offset by a displacement d. Figure 3A To eliminate the misalignment in the method of FIG. 3 , the area of ​​the reference image 350 is subtracted from the grayscale value of the SEM image 360, as shown in FIG. Figure 3BAs shown. Patterns in the reference image that are not aligned with patterns in the SEM image will leave dark areas in the grayscale after subtraction. The subtraction can be used to calculate a misalignment rate that takes a normalized range of values ​​from 0 to 1, where 0 represents the best possible alignment value and 1 represents the worst possible alignment value. The SEM and reference images can be aligned based on the misalignment rate.

[0051] exist Figure 3C In , the misalignment rate is calculated by determining the displacement d of the edge feature in the reference image 350 and its corresponding edge feature in the SEM image 360. Figure 3D As illustrated by the arrows in , multiple displacement measurements can be averaged, weighted averaged, or otherwise combined. As described above, the displacement can be used to calculate a misalignment rate using a normalized range of values ​​from 0 to 1, where 0 represents perfect alignment of pattern features and 1 represents complete misalignment of pattern features.

[0052] Both the subtraction and displacement methods discussed above have several disadvantages. Compared with the central area of ​​the pattern feature, the subtraction and displacement method relies more on the characteristics of the edge of the pattern feature to determine the misalignment, which makes the subtraction and displacement method difficult to use with low-quality images and has poor stability to noise. The subtraction and displacement method is not well suited to scaling and deformation errors. The subtraction and displacement method is also relatively insensitive to fine misalignments, which become increasingly non-negligible as device features continue to shrink. Additionally, the subtraction and displacement method may be incompatible with D2D reference images.

[0053] Figure 4 A system 400 for area density based misalignment measurement according to an embodiment of the present disclosure is illustrated. Figure 1 Calculations and other operations associated with deriving misalignment measurements are performed in the controller 109 in the image processing unit. The left, middle, and right columns each show cross-sectional intensity profiles of a reference image 450, an inspection image 460 (generated by SEM or other imaging), and a cross-correlation 470 (such as a normalized cross-correlation) of the reference image and the inspection image in three different states. In the cross-correlation algorithm, the alignment that maximizes the cross-correlation between the inspection image and the reference image can be output as the alignment result. The three states can be, for example, intensity profiles, binary transform profiles, and density transform profiles.

[0054] The reference image may be, for example, a template such as a D2DB reference image. For example, in some embodiments, the reference image may be a layout file of a wafer design corresponding to the inspection image. The layout file may be a golden image or GDS format, a graphic database system II (GDS II) format, an open artwork system interchange standard (OASIS) format, a Caltech intermediate format (CIF), etc. The wafer design may include a pattern or structure for inclusion on the wafer. The pattern or structure may be a mask pattern used to transfer features from a lithography mask or mask to a wafer. In some embodiments, a layout in a GDS or OASIS format may include feature information stored in a binary file format that represents a planar geometry, text, and other information related to the wafer design. In some embodiments, the reference image may be an image reproduced from a layout file. In some embodiments, before applying an alignment algorithm, a GDS image may be reproduced to generate an image similar to the inspection image, such as an intensity profile reference image 450. Alternatively, the intensity profile reference image 450 may be a substantially defect-free inspection image selected as a reference image in the D2D method. In the illustrated example, the intensity profile of the reference image 450 may be of the D2D type because the illustrated intensity profile of the reference image 450 includes fluctuations and other irregularities that may not be present in a binary profile. Due to the fluctuations of the intensity profiles of the reference image 450 and the inspection image 460, the cross correlation 470 may have several distinct peaks, which may give an erroneous measure of the ideal alignment position between the reference image and the inspection image. Three such peaks are represented by Figure 4 The vertical dashed line in the left column of .

[0055] To overcome these fluctuations, the intensity profiles of the reference image 450 and the inspection image 460 may be binarized, such as by applying a binary transformation, to produce Figure 44 and 5. The binary reference image 451 and the binary inspection image 461 shown in the center column of FIG. Here, a threshold grayscale value can be selected. Points on the profile below the threshold can be marked as not containing pattern features, while points at or above the threshold can be marked as containing pattern features. It should be understood that if the intensity profile reference image 450 is derived from a GDS file or other design data, it may already have a binary form. Further, the threshold applied to the inspection image and the reference image can be the same threshold or different thresholds. Similarly, the binary transformation applied to the inspection image and the reference image can be the same transformation or different transformations. For example, a binary function can be applied to the inspection image 460, and a second binary function can be applied to the reference image 450. In some embodiments, the first binary function and the second binary function can be the same binary function. Although the binarization operation can eliminate intensity fluctuations, the cross-correlation 471 of the two binary images can still produce multiple alignment solutions. In fact, a continuous range of solutions between the vertical dashed lines is seen throughout the stationary segment of the binarized cross-correlation 471.

[0056] Therefore, other transformations may be performed on the reference image 451 and the inspection image 461 to produce Figure 4 452 and 462 in the right column of . For example, the density function can be a transformation applied to the binary image profile 451 / 461 to compress its spatial information toward the central portion of each pattern feature in the ROI. When the information in the binary profiles 451 and 461 of the reference image 450 and the inspection image 460 is compressed toward the central peak in each pattern feature as seen in the density profiles 452 and 462, the cross-correlated density profile 472 can produce a single unique alignment solution as shown by a single vertical dashed line. For example, each density profile in the density profiles 452 and 462 can include a range of density values ​​that generally increase toward the center of each pattern feature. A single unique alignment solution can be based in part on a peak density value in the density profile 452 of the reference image 450 and a peak density value in the density profile 462 of the inspection image 460. In some embodiments of the present disclosure, the alignment solution can be based on multiple density values ​​in the density profile, wherein a larger weight is given to a higher density value. Therefore, the inspection and reference images can be aligned based on a misalignment rate based on regional density.

[0057] Similar to the binary transform described above, the density transforms applied to the inspection image and the reference image can be the same transform or different transforms. For example, a first density function can be applied to the inspection image 460 (such as by applying the first density function to the inspection binary contour 461), and a second density function can be applied to the reference image 450 (such as by applying the second density function to the reference binary contour 451). In some embodiments, the first density function and the second density function can be the same density function.

[0058] Figure 4 Area density based alignment may have several advantages over, for example, edge based alignment processes. For example, flat areas of pattern features (e.g., dark holes or bright metal areas) may be more reliable misalignment indicators than edge patterns. Figure 4 The density profiles in the image may allow for stable automatic center-to-center alignment using compressed information from these flat regions. The density profiles 452 / 462 may be less sensitive to noise than their intensity profile counterparts 450 / 460 and their binary counterparts 451 / 461. However, they may be more sensitive to small amounts of misalignment and to scaling and deformation errors between the reference image and the inspection image. And as discussed above, alignment based on regional density may provide a single solution per aligned feature.

[0059] The cross-correlated density profile 472 of the reference image 452 and the inspection image 462 can be used to generate a misalignment rate based on the area density. For example, the misalignment rate can take the following form:

[0060] MA index = 1-[x·y / (|x|·|y|)] (Equation 1)

[0061] Where x represents the inspection density profile 462 and y represents the reference density profile 452. The cross-correlated density profile 472 may provide a misalignment rate ranging from 0 to 1, where a misalignment rate of 0 is the best possible alignment value and a misalignment rate of 1 is the worst possible alignment value.

[0062] The misalignment rate based on area density can be a result of a charged particle beam inspection process (such as using Figure 1 The misalignment rate based on area density can be applied to various alignment processes, automatic parameter tuning, data filtering, etc.

[0063] In some embodiments, the area density based misalignment rate can be based on a single metric such as Equation 1 above. However, other metrics may be needed in order to evaluate the score derived using the first metric. Further, in some embodiments, density profiles 452 and 462 can be used to create multiple metrics that are combined in the area density based misalignment rate. In some embodiments, classification techniques can be applied to the alignment of the inspection image and the reference image, allowing classification metrics such as the maximum F1 score to be applied to the alignment problem.

[0064] Figure 5 Another metric 500 for misalignment measurement based on area density according to an embodiment of the present invention is illustrated. Figure 5In , a fixed binarization threshold may be applied to the reference density profile 552. The reference density profile 552 may be, for example Figure 4 The reference density profile 452 is obtained by using a fixed threshold. A new reference binary profile 551 can be generated from the density profile 552. Then, a new binary profile 561 can be generated from the inspection density profile 562 by selecting a binarization threshold that produces the best fit of the reference binary profile 551.

[0065] For example, a dilation-erosion operation may be applied to the inspection image density profile 562. For example, the inspection image density profile 562 may be, for example Figure 4 The threshold value can be ranged from the maximum erosion (minimum dilation) value a, through the intermediate value b to the maximum dilation (minimum erosion) value c. This produces a set of candidate binary inspection contours, such as binary inspection contours 561a to 561c. Although Figure 5 Only three representative values ​​are depicted in , but it should be understood that any number of intermediate thresholds may be employed.

[0066] These values ​​can be plotted in a receiver operating characteristic (ROC) curve 572. The ROC curve 572 can be used to analyze the performance of the classification model. The ROC curve can plot the true positive rate (e.g., the ratio of the sum of true positives and true positives and false negatives) (also referred to as recall) along the y-axis, also known as the recall rate, for a classification threshold range relative to the false positive rate (e.g., the ratio of the sum of false positives and false negatives) along the x-axis. True positives can correspond to the point where candidate inspection binary contours 561 correctly predict that pattern features exist in reference binary contours 551. In other words, true positives occur at any point where both inspection contours and reference contours depict pattern features. False positives can correspond to the point where candidate inspection binary contours 561 incorrectly predict that pattern features exist in reference binary contours 551. In other words, false positives occur at any place where candidate inspection contours describe pattern features that do not exist in reference contours. Similarly, true negatives can correspond to the point where candidate inspection binary contours 561 correctly predict that pattern features do not exist in reference binary contours 551. A false negative may correspond to a point where the candidate inspection binary contour 561 erroneously predicts a pattern feature that is not present in the reference binary contour 551 .

[0067] For illustration purposes, consider examining profile 561a. Figure 5In the ROC curve 572 of FIG. 5, low values ​​on the x-axis correspond to high erosion thresholds (such as threshold 563a), which produce narrow binary inspection profile 561a. Because each pattern feature point in inspection profile 561a is aligned with a pattern feature point in reference profile 551, each positive is a true positive. However, because candidate inspection profile 561a also cannot identify many actual positives in reference profile 551, the number of false negatives is also high. Therefore, the true positive rate of profile 561a is low. Further, because there are few or no false positives, the numerator in the false positive rate is low.

[0068] The optimal value may correspond to a point that optimizes the true positive rate and the false positive rate. For example, the maximum F1 score may be used to determine the best inspection binary profile 561 for matching with the reference profile 551. The F1 score may be the harmonic mean of recall (true positive rate) and precision (i.e., the ratio of true positives to the sum of true positives and false positives). The F1 score may take a value in the range of 0 to 1, where 0 represents the worst classification score and 1 represents the best classification score. Therefore, the maximum F1 score measures the maximum similarity between the inspection image and its reference under all possible dilation / erosion scales.

[0069] exist Figure 5 , three F1 scores A, B, and C are plotted along the ROC curve 572. The F1 scores A, B, and C correspond to the binarization thresholds 563a, 563b, and 563c, respectively, and to the binary inspection profiles 561a, 561b, and 561c. The maximum F1 score may correspond to, for example, point B and profile 561b. Therefore, the F1 score B of profile 561b represents another measure of the fit between the inspection image and the reference image, and it can be used as another measure in the misalignment rate based on regional density. Although the maximum F1 score is discussed as one possible classification metric described above, some embodiments of the present disclosure may include other classification metrics as an alternative or in addition to the maximum F1 score. For example, Figure 5 The area under the curve (AUC) of the ROC 572 in can be used as another metric when deriving the misalignment rate based on regional density.

[0070] FIG. 6A to FIG. 6B An area density based misregistration rate 600 including multiple metrics according to an embodiment of the present disclosure is illustrated. Fig. 6A A plurality of inspection image maps and reference image maps of an ROI are shown. The reference intensity map 650 and the inspection intensity map 660 can be binarized into a reference binary map 651 and an inspection binary map 661, respectively. For example, a first binary function can be applied to the inspection intensity map 660, and a second binary function can be applied to the reference intensity map 650. The reference intensity map 650, the inspection intensity map 660, the reference binary map 651, and the inspection binary map 661 can be, for example, Figure 4 The reference intensity profile 450, the inspection intensity profile 460, the reference binary profile 451 and the inspection binary profile 461. Figure 4 Similar to the process discussed above, the binary map 651 / 661 can be converted into a regional density map 652 / 662, such as by applying the first density function and the second density function as discussed above. Figure 4 As discussed, the first binary function and the second binary function may be the same or different from each other. Likewise, the first density function and the second density function may be the same or different from each other.

[0071] These area density maps can then be used to derive one or more metrics that can be combined into an area density-based misalignment rate. Fig. 6A As can be seen, the reference area density map 652 and the inspection area density map 662 can be used to Figure 4 The normalized cross-correlation NC discussed is combined. The normalized cross-correlation NC can take the form of the bracketed term in Equation 1, [x·y / (|x|·|y|)], where x represents the inspection density map 662 and y represents the reference density map 652. This binarized score will take a value between 0 and 1, where 0 represents no correlation and 1 represents perfect correlation.

[0072] In a parallel process, the same mapping 652 / 662 can be used to derive the above Figure 5 The maximum F1 score discussed above. For example, the maximum F1 score can be determined by comparing a reference binary map (such as reference binary map 651) under a fixed threshold with a plurality of new inspection binary maps. The new inspection binary maps can be generated by applying a dilation-erosion operation to the inspection density map 662 to generate a plurality of binarization thresholds. Figure 5 The classification technique discussed is used to analyze multiple new bigrams, for example to determine the maximum F1 score of multiple test bigrams. The maximum F1 score can also take a value between 0 and 1, where 0 represents the worst classification score and 1 represents the best classification score.

[0073] The results of the two scores can be combined, for example, as a weighted average, to produce a misregistration rate based on area density. Fig. 6A As can be seen, the weights are equal and the combination is a simple average, giving the following misalignment rates:

[0074] MA index = 1-[(NC+MAXF1) / 2] (Equation 2)

[0075] Where NC is the normalized cross-correlation value and Max F1 is the maximum F1 score as discussed above. Here, these terms are averaged and subtracted from 1 so that 0 corresponds to the best alignment and 1 corresponds to the worst alignment. However, the misalignment rate need not be presented in this form. Further, the two components of the misalignment rate need not be weighted equally. In general, in some embodiments of the present invention, two metrics may be combined, with the first metric being assigned the coefficient C 1 , and the second metric is assigned the coefficient C 2 , where (C 1 +C 2 )=1. It should also be understood that the misregistration rate is not limited to the range of 0 to 1, nor is it limited to the combination of only two metrics.

[0076] For example, Figure 6B Picture shows Fig. 6A Here, the reference area density map 652 and the inspection area density map 662 can be combined into a plurality of metrics M1 to MN. The metrics can be combined into misalignment rates and averaged as shown. Alternatively, the metrics can be combined in another way. For example, the metrics can be assigned weights C 1 To C N , where (C 1 +C 2 +C 1 ......+C N )=1. In general, metrics can be combined in any suitable manner. The selection of metrics and weight distribution can be determined using machine learning or other data-driven methods. In some embodiments of the present disclosure, a large number of training data sets can be used to train a misalignment rate model for use in a specific application.

[0077] The misalignment rate based on area density can be used to align Figure 4 The inspection image and reference image discussed. For example, each density map in density maps 652 and 662 may include a range of density values ​​that generally increase toward the center of each pattern feature. A unique alignment solution may be based in part on a peak density value in density map 652 of reference image map 650 and a peak density value in density map 662 of inspection image map 660. In some embodiments of the present disclosure, an alignment solution may be based on multiple density values ​​in a density profile, with greater weights given to higher density values. Therefore, the inspection and reference images may be aligned based on a misalignment rate based on regional density.

[0078] Figure 7 A comparison 700 between the misalignment rate of a conventional system and the misalignment rate based on area density according to an embodiment of the present invention is illustrated. Figure 7, three different alignment scenes 700a to 700c are presented. Alignment scene 700a represents a poor alignment between a reference image 750 and an inspection image 760. In 700a, an accurate misalignment rate can produce a high index value. The middle view 700b represents a not-so-bad alignment between the reference image 750 and the inspection image 760. In 700b, an accurate misalignment rate can produce an intermediate index value. Finally, the bottom view 700c represents a good alignment between the reference image 750 and the inspection image 760. In 700c, an accurate misalignment rate can produce a low index value.

[0079] First, consider the above discussion of FIG. 3A to FIG. 3B The comparative misalignment rates based on the subtraction method discussed are Figure 7 , it is seen that the subtraction method tends to go in the wrong direction across all three alignments 700a to 700c, such that it produces low index values ​​for high misalignments and vice versa. For example, in the worst alignment scenario 700a, the subtraction misalignment rate produces a low misalignment value when the actual misalignment is high. Similarly, in the best alignment scenario 700c, the subtraction misalignment rate produces a high misalignment value when the actual misalignment is low. Although the subtraction misalignment rate appears to produce an appropriate measurement for the middle scenario 700b, the subtraction misalignment rate is not consistent across all scenarios 700a to 700c, and therefore cannot be relied upon to produce an accurate index value.

[0080] Next, consider the above FIG. 3C to FIG. 3D The comparative misalignment rates of the discussed shift-based methods. Figure 7 In the example above, the shift method is insensitive to changes in the actual misalignment, thereby producing low index values ​​in each scenario. For example, in the worst alignment scenario 700a, the shifted misalignment rate produces low misalignment values ​​when the actual misalignment is high. In the intermediate alignment scenario 700b, the shifted misalignment rate again produces low misalignment values, where the actual misalignment is an intermediate value. Although the shifted misalignment rate appears to produce an appropriate measurement for the best alignment scenario 700c, the shifted misalignment rate consistently produces low values ​​across all scenarios 700a to 700c. Therefore, the shifted misalignment rate (such as the subtraction misalignment rate) cannot be relied upon to produce an accurate index value.

[0081] Finally, consider the misalignment rate based on regional density according to an embodiment of the present disclosure. Here, the misalignment rate based on regional density is accurately tracked together with the actual misalignment at each misalignment scene in the high misalignment scene, the medium misalignment scene, and the low misalignment scene 700a to 700c. For example, in the worst alignment scene 700a, the misalignment rate based on regional density produces a high misalignment value, and the actual misalignment is also high. In the middle alignment scene 700b, the misalignment rate based on regional density produces an intermediate misalignment value, and the actual misalignment is also an intermediate value. In the best alignment scene 700c, the misalignment rate based on regional density produces a low misalignment value, and the actual misalignment is also low. The misalignment rate based on regional density shows excellent performance in terms of misalignment stability and sensitivity that is superior to the two comparative misalignment rates.

[0082] Figure 8 A method 800 for generating a misalignment rate based on area density according to an embodiment of the present invention is illustrated. The method may be executed by, for example, one or more processors coupled to a memory. The method may be executed by, for example, a controller of an imaging or inspection device (such as, for example, Figure 1 The controller 109 or Figure 2 The inspection image to be used for method 800 may be obtained by an inspection device such as an image processing system 290. Figure 1 EBI System 100 or Figure 1 to Figure 2 Alternatively, the inspection image may be generated by other imaging systems such as optical imaging, photon detection, proton detection, x-ray detection, ion detection, etc. Photon detection may include infrared, visible light, UV, DUV, EUV, x-ray, or light in any other wavelength range.

[0083] At step 810, an inspection image and a reference image are acquired for a region of interest ROI. The inspection image may be generated, for example, by SEM or other imaging. The reference image may be generated, for example, from a design layout file or a representative inspection image that has been selected as a reference. The image may represent, for example, an intensity profile or intensity map, such as a grayscale map of the inspection image and the reference image. The ROI may be, for example, an inspected region on a sample surface such as a semiconductor wafer.

[0084] At step 820, a binarization process is performed on the inspection image and the reference image. The binarization process can transform an image having multiple intensity values ​​or grayscale values ​​into a binary image having two values. The first value can represent an area having a pattern feature, and the second value can represent an area without a pattern feature. For example, a binary image can include Figures 4 to 6B Binary contours or binary maps as seen. In some embodiments, at step 820, a reference image may be acquired in binary form and no further binarization is required.

[0085] At step 830, a density function is applied to the inspection image and the reference image. The density function may be a single density function, or may include a first density function applied to the inspection image (such as by applying the first density function to the binary inspection image derived at step 820), and a second density function applied to the reference image (such as by applying the second density function to the binary reference image derived at step 820). The density function may compress information of the inspection image and the reference image from peripheral or edge regions of the pattern features to more central regions of the pattern features. The density function may be used to generate a density image representing the inspection image and the reference image. For example, the density image may include a density function such as Figures 4 to 6B The density contour or density map seen.

[0086] At step 840, a first metric is calculated based on the inspection density image and the reference density image. The first metric may include, for example, a normalized cross correlation, a maximum F1 value, an AUC, or other classification or alignment evaluation metric. In some embodiments, an optional further step 841 may include calculating a second such metric. In some embodiments, more than two metrics may be calculated.

[0087] At step 850, one or more metrics generated at step 840 or 841 are used to generate a misalignment rate based on regional density. The misalignment rate based on regional density may include a weighted average or other suitable metric combination to generate the misalignment rate. Machine learning or other data-driven methods may be used to determine the selection of metrics and weight distributions. In some embodiments of the present disclosure, a large number of training data sets may be used to train a misalignment rate model for a specific application.

[0088] The method may include the above with respect to, for example Figure 1 , Figure 2 and Figures 4 to 7 The steps shown and the following Figure 8 Any of the steps discussed above. The misalignment rate generated by method 800 can be applied to, for example, Figure 1 EBI System 100 or Figure 1 to Figure 2 The electron beam tool 104 performs the charged particle beam process.

[0089] A non-transitory computer readable medium storing instructions for causing a controller (eg, Figure 1 The controller 109) of the processor performs inspection image, acquisition image, positioning stage, focusing beam, adjusting electric field, bending beam, adjusting converging lens, activating charged particle source, deflecting beam, and method 800 or including Figure 1 , Figure 2 and Figures 4 to 7 Other methods of the process steps discussed. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tapes or any other magnetic data storage medium, compact disk read-only memory (CD-ROM), any other optical data storage medium, any physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM) and erasable programmable read-only memory (EPROM), FLASH-EPROM or any other flash memory, non-volatile random access memory (NVRAM), cache, registers, any other memory chip or cartridge, and networked versions thereof.

[0090] The embodiments may also be described using the following terms.

[0091] 1. An image alignment method, comprising:

[0092] using a first density function to acquire a first density map of the inspection image corresponding to the region of interest;

[0093] using the second density function to obtain a second density map of the reference image corresponding to the region of interest; and

[0094] The inspection image and the reference image are aligned based on the acquired first density map and second density map.

[0095] 2. The method according to clause 1, wherein the first density function and the second density function are the same.

[0096] 3. The method according to clause 1, wherein the first density function and the second density function are different from each other.

[0097] 4. A method according to clause 1, wherein

[0098] Acquiring a first density map includes: acquiring a first density value range including a first density value;

[0099] Acquiring the second density map includes: acquiring a second density value range including the second density value; and

[0100] aligning the inspection image and the reference image based on the acquired first density value and second density value; wherein

[0101] The first density value is a first peak density value within a first range of density values, and the second density value is a second peak density value within a second range of density values.

[0102] 5. The method according to clause 1, further comprising:

[0103] obtaining a first binary transformation of the inspection image using the first binary function;

[0104] A second binary transform of the reference image is obtained using the second binary function.

[0105] 6. A method according to clause 5, wherein

[0106] Using the first density function to obtain a first density map includes: applying the first density function to a first binary transform; and

[0107] Using the second density function to obtain a second density map includes applying the second density function to a second binary transform.

[0108] 7. The method according to clause 5, wherein the first binary function and the second binary function are the same.

[0109] 8. The method according to clause 5, wherein the first binary function and the second binary function are different from each other.

[0110] 9. The method according to clause 1, further comprising:

[0111] determining a misalignment rate based on regional density based on the acquired first density map and second density map;

[0112] The aligning the inspection image and the reference image based on the acquired first density map and second density map includes: aligning the inspection image and the reference image based on a misalignment rate based on regional density.

[0113] 10. The method according to clause 9, further comprising:

[0114] determining a first metric based on a first of the acquired first density map and the second density map;

[0115] Wherein an area density based misregistration rate is determined based on a first metric.

[0116] 11. The method according to clause 10, further comprising:

[0117] determining a second metric based on a first one of the acquired first density map and the second density map, the second metric being different from the first metric;

[0118] Wherein the area density based misregistration rate is determined based on a combination of the first metric and the second metric.

[0119] 12. The method of clause 10, wherein the first metric is based on one of: a cross correlation, a normalized cross correlation, an F1 score, and an area under the curve (AUC) of a receiver operating characteristic (ROC) curve.

[0120] 13. The method according to clause 1, further comprising:

[0121] An overlay error is determined based on the acquired first density map and second density map.

[0122] 14. The method according to any of clauses 1 to 13, wherein the inspection image is a charged particle beam image.

[0123] 15. The method according to clause 14, wherein the charged particle beam image is a SEM image.

[0124] 16. The method according to any of clauses 1 to 15, wherein the reference image is based on one of a design layout file and a reference SEM image.

[0125] 17. A non-transitory computer-readable medium storing a set of instructions, the set of instructions being executable by at least one processor of a device to cause the device to perform a method, the method comprising:

[0126] using a first density function to acquire a first density map of the inspection image corresponding to the region of interest;

[0127] using the second density function to obtain a second density map of the reference image corresponding to the region of interest; and

[0128] The inspection image and the reference image are aligned based on the acquired first density map and second density map.

[0129] 18. The non-transitory computer readable medium of clause 17, wherein the first density function and the second density function are the same.

[0130] 19. The non-transitory computer readable medium of clause 17, wherein the first density function and the second density function are different from each other.

[0131] 20. A non-transitory computer readable medium according to clause 17, wherein

[0132] Acquiring a first density map includes: acquiring a first density value range including a first density value;

[0133] Acquiring the second density map includes: acquiring a second density value range including the second density value; and

[0134] The first density value is a first peak density value within a first range of values, and the second density value is a second peak density value within a second range of values.

[0135] 21. The non-transitory computer-readable medium of clause 17, wherein a set of instructions executable by at least one processor of the device causes the device to further perform:

[0136] obtaining a first binary transformation of the inspection image using the first binary function;

[0137] A second binary transform of the reference image is obtained using the second binary function.

[0138] 22. A non-transitory computer readable medium according to clause 21, wherein

[0139] Using the first density function to obtain a first density map includes: applying the first density function to a first binary transform; and

[0140] Using the second density function to obtain a second density map includes applying the second density function to a second binary transform.

[0141] 23. The non-transitory computer readable medium of clause 21, wherein the first binary function and the second binary function are the same.

[0142] 24. The non-transitory computer readable medium of clause 21, wherein the first binary function and the second binary function are different from each other.

[0143] 25. The non-transitory computer-readable medium of clause 17, wherein a set of instructions executable by at least one processor of the device causes the device to further perform:

[0144] determining a misalignment rate based on regional density based on the acquired first density map and second density map;

[0145] The aligning the inspection image and the reference image based on the acquired first density map and second density map includes: aligning the inspection image and the reference image based on a misalignment rate based on regional density.

[0146] 26. The non-transitory computer-readable medium of clause 25, wherein a set of instructions executable by at least one processor of the device causes the device to further perform:

[0147] determining a first metric based on a first of the acquired first density map and the second density map;

[0148] Wherein an area density based misregistration rate is determined based on a first metric.

[0149] 27. The non-transitory computer-readable medium of clause 26, wherein a set of instructions executable by at least one processor of the device causes the device to further perform:

[0150] determining a second metric based on a first one of the acquired first density map and the second density map, the second metric being different from the first metric;

[0151] Wherein the area density based misregistration rate is determined based on a combination of the first metric and the second metric.

[0152] 28. The non-transitory computer-readable medium of clause 26, wherein the first metric is based on one of: a cross correlation, a normalized cross correlation, an F1 score, and an area under the curve (AUC) of a receiver operating characteristic (ROC) curve.

[0153] 29. The non-transitory computer readable medium of any of clauses 17 to 28, wherein the inspection image is a charged particle beam image.

[0154] 30. The non-transitory computer readable medium of clause 29, wherein the charged particle beam image is a SEM image.

[0155] 31. The non-transitory computer readable medium of any one of clauses 17 to 30, wherein the reference image is based on one of a design layout file and a reference SEM image.

[0156] 32. A charged particle beam device comprising:

[0157] a charged particle beam source configured to generate a primary charged particle beam;

[0158] an optical system configured to direct a primary charged particle beam at a sample surface to inspect the sample surface;

[0159] a charged particle detector configured to detect charged particles returning from a surface of the sample;

[0160] A controller comprising one or more processors and configured to cause the charged particle beam apparatus to perform:

[0161] using a first density function to acquire a first density map of the inspection image corresponding to the region of interest;

[0162] using the second density function to obtain a second density map of the reference image corresponding to the region of interest; and

[0163] The inspection image and the reference image are aligned based on the acquired first density map and second density map.

[0164] 33. A charged particle beam apparatus according to clause 32, wherein the first density function and the second density function are the same.

[0165] 34. Charged particle beam apparatus according to clause 32, wherein the first density function and the second density function are different from each other.

[0166] 35. A charged particle beam apparatus according to clause 32, wherein

[0167] Acquiring a first density map includes: acquiring a first density value range including a first density value;

[0168] Acquiring the second density map includes: acquiring a second density value range including the second density value; and

[0169] The first density value is a first peak density value within a first range of values, and the second density value is a second peak density value within a second range of values.

[0170] 36. A charged particle beam apparatus according to clause 32, wherein the controller is further configured to cause the charged particle beam apparatus to perform:

[0171] obtaining a first binary transformation of the inspection image using the first binary function;

[0172] A second binary transform of the reference image is obtained using the second binary function.

[0173] 37. A charged particle beam apparatus according to clause 36, wherein

[0174] Using the first density function to obtain a first density map includes: applying the first density function to a first binary transform; and

[0175] Using the second density function to obtain a second density map includes applying the second density function to a second binary transform.

[0176] 38. A charged particle beam apparatus according to clause 36, wherein the first binary function and the second binary function are the same.

[0177] 39. Charged particle beam apparatus according to clause 36, wherein the first binary function and the second binary function are different from each other.

[0178] 40. The charged particle beam apparatus according to clause 32, wherein the controller is further configured to cause the charged particle beam apparatus to perform:

[0179] determining a misalignment rate based on regional density based on the acquired first density map and second density map;

[0180] The aligning the inspection image and the reference image based on the acquired first density map and second density map includes: aligning the inspection image and the reference image based on a misalignment rate based on regional density.

[0181] 41. The charged particle beam apparatus according to clause 40, wherein the controller is further configured to cause the charged particle beam apparatus to perform:

[0182] determining a first metric based on a first of the acquired first density map and the second density map;

[0183] Wherein an area density based misregistration rate is determined based on a first metric.

[0184] 42. A charged particle beam apparatus according to clause 41, wherein the controller is further configured to cause the charged particle beam apparatus to perform:

[0185] determining a second metric based on a first one of the acquired first density map and the second density map, the second metric being different from the first metric;

[0186] Wherein the area density based misregistration rate is determined based on a combination of the first metric and the second metric.

[0187] 43. A charged particle beam apparatus according to clause 41, wherein the first metric is based on one of: a cross correlation, a normalized cross correlation, an F1 score, and an area under the curve (AUC) of a receiver operating characteristic (ROC) curve.

[0188] 44. A charged particle beam apparatus according to any of clauses 32 to 43, wherein the inspection image is a charged particle beam image.

[0189] 45. The charged particle beam apparatus according to clause 44, wherein the charged particle beam image is a SEM image.

[0190] 46. ​​A charged particle beam apparatus according to any of clauses 32 to 45, wherein the reference image is based on one of a design layout file and a reference SEM image.

[0191] 47. An image alignment method, comprising:

[0192] acquiring an inspection image corresponding to a region of interest of the sample;

[0193] acquiring a reference image corresponding to a region of interest of the sample;

[0194] using the first binary function to obtain an inspection binary contour of the inspection image;

[0195] obtaining a reference binary contour of the reference image using a second binary function;

[0196] using a first density function to obtain a first density map for examining a binary profile;

[0197] obtaining a second density map of the reference binary profile using the second density function; and

[0198] Alignment parameters of the inspection image and the reference image are determined based on the acquired first density map and second density map.

[0199] 48. A non-transitory computer readable medium storing a set of instructions, the set of instructions being executable by at least one processor of a device to cause the device to perform a method comprising:

[0200] acquiring an inspection image corresponding to a region of interest of the sample;

[0201] acquiring a reference image corresponding to a region of interest of the sample;

[0202] using the first binary function to obtain an inspection binary contour of the inspection image;

[0203] obtaining a reference binary contour of the reference image using a second binary function;

[0204] using a first density function to obtain a first density map for examining a binary profile;

[0205] obtaining a second density map of the reference binary profile using the second density function; and

[0206] Alignment parameters of the inspection image and the reference image are determined based on the acquired first density map and second density map.

[0207] 49. A charged particle beam device comprising:

[0208] a charged particle beam source configured to generate a primary charged particle beam;

[0209] an optical system configured to direct a primary charged particle beam at a sample surface to inspect the sample surface;

[0210] a charged particle detector configured to detect charged particles returning from a surface of the sample;

[0211] A controller comprising one or more processors and configured to cause the charged particle beam apparatus to perform:

[0212] acquiring an inspection image corresponding to a region of interest of the sample;

[0213] acquiring a reference image corresponding to a region of interest of the sample;

[0214] using the first binary function to obtain an inspection binary contour of the inspection image;

[0215] obtaining a reference binary contour of the reference image using a second binary function;

[0216] using a first density function to obtain a first density map for examining a binary profile;

[0217] obtaining a second density map of the reference binary profile using the second density function; and

[0218] Alignment parameters of the inspection image and the reference image are determined based on the acquired first density map and second density map.

[0219] The block diagram in the figure can illustrate the architecture, function and operation of the possible implementation of the system, method and computer hardware or software product according to various exemplary embodiments of the present disclosure. In this regard, each box in the schematic diagram can represent certain arithmetic or logic operations that can be implemented using hardware such as electronic circuits. The box can also represent a module, segment or part of a code including one or more executable instructions for implementing a specified logical function. It should be understood that in some alternative implementations, the functions indicated in the box may not occur in the order indicated in the accompanying drawings. For example, depending on the functions involved, two boxes shown in succession can be implemented or implemented substantially simultaneously, or two boxes can sometimes be implemented in reverse order. Some boxes can also be omitted. It should also be understood that each box of the block diagram and the combination of boxes can be implemented by a system based on dedicated hardware that performs a specified function or action or by a combination of dedicated hardware and computer instructions.

[0220] It should be understood that the embodiments of the present invention are not limited to the exact configurations described above and illustrated in the accompanying drawings, and various modifications and changes may be made without departing from the scope of the present invention. The present disclosure has been described in conjunction with various embodiments, and other embodiments of the present invention will be apparent to those skilled in the art in view of the specification and practice of the present invention disclosed herein. The specification and embodiments are to be considered as exemplary only, and the true scope and spirit of the present invention are indicated by the appended claims.

Claims

1. A non-transitory computer-readable medium storing a set of instructions, the set of instructions being executable by at least one processor of a device to cause the device to perform a method, the method comprising: using a first density function to acquire a first density map of the inspection image corresponding to the region of interest; using a second density function to acquire a second density map of the reference image corresponding to the region of interest; as well as The inspection image and the reference image are aligned based on the acquired first and second density maps. 2 . The non-transitory computer readable medium of claim 1 , wherein the first density function and the second density function are the same. 3 . The non-transitory computer readable medium of claim 1 , wherein the first density function and the second density function are different from each other.

4. The non-transitory computer readable medium of claim 1, wherein: Acquiring the first density map includes acquiring a first density value range including a first density value; Acquiring the second density map includes acquiring a second density value range including a second density value; and The first density value is a first peak density value within the first range of values, and the second density value is a second peak density value within the second range of values.

5. The non-transitory computer readable medium of claim 1 , wherein the set of instructions executable by the at least one processor of the device causes the device to further perform: using a first binary function to obtain a first binary transformation of the inspection image; A second binary transform of the reference image is obtained using a second binary function.

6. The non-transitory computer readable medium of claim 5, wherein: Using the first density function to obtain the first density map comprises applying the first density function to the first binary transform; and Using the second density function to obtain the second density map includes applying the second density function to the second binary transform.

7. The non-transitory computer readable medium of claim 5, wherein the first binary function and the second binary function are the same.

8. The non-transitory computer readable medium of claim 5, wherein the first binary function and the second binary function are different from each other.

9. The non-transitory computer readable medium of claim 1 , wherein the set of instructions executable by the at least one processor of the device causes the device to further perform: determining a misalignment rate based on regional density based on the acquired first density map and the second density map; Wherein aligning the inspection image and the reference image based on the acquired first density map and the second density map includes aligning the inspection image and the reference image based on the misalignment rate based on the regional density.

10. The non-transitory computer readable medium of claim 9, wherein the set of instructions executable by the at least one processor of the device causes the device to further perform: determining a first metric based on a first one of the first density map and the second density map obtained; Wherein the area density based misregistration rate is determined based on the first metric.

11. The non-transitory computer readable medium of claim 10, wherein the set of instructions executable by the at least one processor of the device causes the device to further perform: determining a second metric based on a first one of the first density map and the second density map obtained, the second metric being different from the first metric; Wherein the area density based misregistration rate is determined based on a combination of the first metric and the second metric.

12. The non-transitory computer readable medium of claim 10, wherein the first metric is based on one of: a cross correlation, a normalized cross correlation, an F1 score, and an area under a curve (AUC) of a receiver operating characteristic (ROC) curve.

13. The non-transitory computer readable medium of claim 1, wherein the inspection image is a charged particle beam image. The non-transitory computer readable medium of claim 13 , wherein the charged particle beam image is a SEM image.

15. The non-transitory computer readable medium of claim 1, wherein the reference image is based on one of a design layout file and a reference SEM image.

Images