Parameterized inspection image simulation

Abstract

An improved method, apparatus, and system for generating a simulated inspection image are disclosed. According to certain aspects, the method includes acquiring design data including a first pattern, generating a first gray scale distribution corresponding to the design data, and rendering an image using the generated first gray scale distribution.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application No. 63 / 411,040, filed on September 28, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0003] Embodiments provided herein relate to an inspection image simulation technique, and more particularly, to generating a parameterized simulated inspection image from a layout design. Background Art

[0004] In the manufacturing process of integrated circuits (ICs), unfinished or completed circuit components are inspected to ensure that they are manufactured according to the design and are free of defects. Inspection systems using optical microscopes or charged particle (e.g., electron) beam microscopes such as scanning electron microscopes (SEMs) can be used. As the physical size of IC components continues to shrink, the accuracy and yield of defect detection become more important. Various metrology tools are developed and used to verify that the ICs are manufactured correctly. In order to improve defect inspection performance, it is desirable to verify / quantify such metrology tools with a sufficient number of inspection images of various patterns, sizes, and densities. Summary of the invention

[0005] Embodiments provided herein disclose a particle beam inspection apparatus, and more particularly, an inspection apparatus using a plurality of charged particle beams.

[0006] Some embodiments provide a device for generating a simulated inspection image. The device may include a memory storing an instruction set; and at least one processor configured to execute the instruction set to cause the device to perform: acquiring design data including a first pattern; generating a first grayscale distribution corresponding to the design data; and rendering an image using the generated first grayscale distribution.

[0007] Some embodiments provide a non-transitory computer-readable medium storing an instruction set that can be executed by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image. The method includes acquiring design data including a first pattern; generating a first grayscale distribution corresponding to the design data; and rendering an image using the generated first grayscale distribution.

[0008] Other advantages of the embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings which set forth certain embodiments of the present invention by way of illustration and example. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other aspects of the present disclosure will become more apparent through the description of exemplary embodiments with reference to the attached drawings.

[0010] Figure 1 is a schematic diagram illustrating an example charged particle beam inspection system consistent with embodiments of the present disclosure.

[0011] Figure 2 is a schematic diagram showing an example multi-beam tool consistent with an embodiment of the present disclosure, which may be Figure 1 An example of a portion of a charged particle beam inspection system.

[0012] Figure 3 is a block diagram of an example inspection image simulation system consistent with embodiments of the present disclosure.

[0013] FIG. 4A to FIG. 4D An example procedure for inspection image simulation consistent with an embodiment of the present disclosure is shown.

[0014] Figure 5 is a block diagram of an example grayscale distribution generation system consistent with embodiments of the present disclosure.

[0015] FIG. 6A to FIG. 6C An example procedure for grayscale distribution generation consistent with an embodiment of the present disclosure is shown.

[0016] Fig. 7A An example performance evaluation of an inspection image simulation system consistent with embodiments of the present disclosure is shown.

[0017] FIG. 7B to FIG. 7C Example simulated images of various patterns generated using an inspection image simulation system consistent with an embodiment of the present disclosure are shown.

[0018] Figure 8 is a process flow diagram representing an example method for simulating inspection images consistent with embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Reference will now be made in detail to exemplary embodiments, examples of which are shown in the accompanying drawings. The following description refers to the accompanying drawings, in which the same numbers in different drawings represent the same or similar elements, unless otherwise indicated. The implementations set forth in the following description of exemplary embodiments do not represent all implementations. Instead, they are merely examples of apparatus and methods consistent with aspects related to the disclosed embodiments recited 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. Other types of charged particle beams (e.g., including protons, ions, muons, or any other particles carrying a charge) may be similarly applied. In addition, other imaging systems may be used, such as optical imaging, photon detection, x-ray detection, ion detection, and the like.

[0020] Electronic devices consist of circuits formed on a piece of semiconductor material called a substrate. The semiconductor material can include, for example, silicon, gallium arsenide, indium phosphide, or silicon germanium. Many circuits can be formed together on the same piece of silicon and are called integrated circuits, or ICs. The size of these circuits has been dramatically reduced, allowing many more to be packed onto a substrate. For example, the IC chip in a smartphone can be as small as a miniature Figure 1 As small as a chip, it can include more than 2 billion transistors, each of which is less than 1 / 1000 the size of a human hair.

[0021] Manufacturing these ICs from extremely small structures or components is a complex, time-consuming and expensive process that often involves hundreds of individual steps. An error in even one step can cause a defect in the finished IC, rendering it unusable. 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; that is, to improve the overall yield of the process.

[0022] 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. Inspection can be performed using a scanning charged particle 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 "photograph" of the structures of the wafer. The images can be used to determine if the structures are formed correctly in the proper locations. If the structures are defective, the process can be adjusted so the defect is less likely to occur again.

[0023] As the physical size of IC components continues to shrink, the accuracy and yield of defect detection become more important. Metrology tools can be used to determine whether the IC is manufactured correctly by measuring the critical dimensions, curvature, roughness, etc. of the structure on the wafer. Such metrology can be based on the structural contour extracted by the contour extraction tool, which can be part of some of the metrology tools. Accurate verification / quantification of metrology tools is very important for improving defect detection accuracy. In addition, various metrology tools have been developed, and it can be determined based on their performance (e.g., accuracy, throughput, etc.) which metrology tool to use among various metrology tools. Because metrology tool performance may vary by pattern, size, density, etc., it is desirable to test the metrology tool with a sufficient number of inspection images with various patterns, sizes, and densities to accurately verify / quantify the metrology tool. However, obtaining a sufficient number of inspection images with various patterns, sizes, and densities is time-consuming and expensive, or even impossible.

[0024] Although there are several SCPM simulators on the market, such as Hyperlith and eScatter, these simulators are based on physical modeling of beams. Such physical model-based simulators are usually time-inefficient or even impossible to generate a sufficient number of simulated SCPM images with various patterns, sizes, and densities. In addition, the output of these SCPM simulators is not compatible with some metrology tools.

[0025] Embodiments of the present disclosure may provide a parameterized SCPM image simulator. According to some embodiments of the present disclosure, the simulated inspection image combines metrology-related parameters that can be defined and determined by the user. According to some embodiments of the present disclosure, the simulated inspection image may be generated using grayscale distribution data extracted from a real image (i.e., a non-simulated image) or a simulated image based on a physical model, or using user-defined grayscale distribution data. In some embodiments, the grayscale distribution data may come from user-defined grayscale distribution data. According to some embodiments of the present disclosure, parameters related to edge roughness, grayscale distribution, distortion, contrast, etc. may be used to control the simulated inspection image. According to some embodiments of the present disclosure, an inspection image with a complex pattern may be simulated, which may not be possible with existing simulators based on physical models. According to some embodiments of the present disclosure, an inspection image may be simulated much faster than an existing simulator based on a physical model.

[0026] For clarity, the relative sizes of components in the drawings may be exaggerated. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only the differences from the various embodiments are described. As used herein, unless otherwise specifically stated, the term "or" includes all possible combinations unless not feasible. For example, if it is stated that a component may include A or B, then unless otherwise specifically stated or not feasible, the component may include A or B, or A and B. As a second example, if it is stated that a component may include A, B or C, then unless otherwise specifically stated or not feasible, the component may include A or B or C, or A and B, or A and C, or B and C, or A and B and C.

[0027] Figure 1 An example electron beam inspection (EBI) system 100 consistent with embodiments of the present disclosure is shown. The EBI system 100 can be used for imaging. Figure 1As 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 receive a wafer front opening transport box (FOUP), which contains 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 "batch" is a plurality of wafers that can be processed as a batch of loads.

[0028] One or more robots (not shown) in the EFEM 106 can transfer the wafer to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown), which removes gas molecules in the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, one or more robots (not shown) can transfer 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), which 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.

[0029] The controller 109 is electrically connected to the beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. Figure 1 The controller 109 is shown outside the structure including the main chamber 101, the load / lock chamber 102 and the EFEM 106, but it should be understood that the controller 109 can be a part of the structure.

[0030] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or specific electronic device capable of manipulating or processing information. For example, the processor may include any number of central processing units (or "CPUs"), graphics processing units (or "GPUs"), optical processors, programmable logic controllers, microcontrollers, microprocessors, digital signal processors, intellectual property (IP) cores, programmable controller arrays (PLAs), programmable array logic (PALs), general array logic (GALs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), systems on chips (SoCs), application specific integrated circuits (ASICs), and any combination of any type of circuit capable of data processing. The processor may also be a virtual processor, which includes one or more processors distributed across multiple machines or devices coupled via a network.

[0031] In some embodiments, the controller 109 may further include one or more memories (not shown). The memory may be a general or specific electronic device capable of storing processor-accessible code and data (e.g., via a bus). For example, the memory may include any number of random access memories (RAM), read-only memories (ROM), optical disks, magnetic disks, hard drives, solid-state drives, flash drives, secure digital (SD) cards, memory sticks, compact flash (CF) cards, or any combination of any type of storage device. The code and data may include an operating system (OS) and one or more application programs (or "applications") for a specific task. The memory may also be a virtual memory, which includes one or more memories distributed across multiple machines or devices coupled via a network.

[0032] Figure 2 1 shows a schematic diagram of an example multi-beam tool 104 (also referred to herein as apparatus 104) and an image processing system 290 that may be configured for use with the EBI system 100 ( Figure 1 ).

[0033] The beam tool 104 includes a charged particle source 202, a gun aperture 204, a focusing lens 206, a primary charged particle beam 210 emitted from the charged particle source 202, a source conversion unit 212, a plurality of beamlets 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.

[0034] The charged particle source 202, the gun aperture 204, the focusing lens 206, the source conversion unit 212, the beam splitter 222, the deflection scanning unit 226, and the objective lens 228 may be aligned with the primary optical axis 260 of the apparatus 104. The secondary optical system 242 and the charged particle detection device 244 may be aligned with the secondary optical axis 252 of the apparatus 104.

[0035] 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, charged particle source 202 can be an electron source. For example, 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 descriptions in the description of this article. However, it should be noted that any charged particle can be used in any embodiment of the present disclosure, not limited to electrons. Primary charged particle beam 210 can be visualized as being emitted from intersection 208. Gun hole 204 can block the peripheral charged particles of primary charged particle beam 210 to reduce the Coulomb effect. The Coulomb effect may cause an increase in the size of the probe spot.

[0036] The source conversion unit 212 may include an imaging element array and a beam limiting aperture array. The imaging element array may include an array of micro-deflectors or micro-lenses. The imaging element array may form multiple parallel images (virtual or real) of the intersection 208 with the multiple sub-beams 214, 216, and 218 of the primary charged particle beam 210. The beam limiting aperture array may limit the multiple sub-beams 214, 216, and 218. Although Figure 2 , three sub-beams 214, 216, and 218 are shown, but the 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 sub-beams. In some embodiments, the first number of sub-beams may be in the range of 1 to 1000. In some embodiments, the first number of sub-beams may be in the range of 200 to 500. In an exemplary embodiment, the apparatus 104 may generate 400 sub-beams.

[0037] The condenser lens 206 can focus the primary charged particle beam 210. The current of the sub-beams 214, 216 and 218 downstream of the source conversion unit 212 can be changed by adjusting the focusing power of the condenser lens 206 or by changing the radial size of the corresponding beam limiting apertures in the beam limiting aperture array. The objective lens 228 can focus the sub-beams 214, 216 and 218 onto the wafer 230 for imaging, and can form a plurality of probe points 270, 272 and 274 on the surface of the wafer 230.

[0038] 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 they are applied, the force exerted by the electrostatic dipole field on the charged particles (e.g., electrons) of the sub-beams 214, 216, and 218 can be substantially equal in magnitude to the force exerted by the magnetic dipole field on the charged particles, and opposite in direction to the force exerted by the magnetic dipole field on the charged particles. Therefore, the sub-beams 214, 216, and 218 can pass directly through the beam splitter 222 at a zero deflection angle. However, the total dispersion of the sub-beams 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 sub-beams 214, 216, and 218, and guide the secondary charged particle beams 236, 238, and 240 to the secondary optical system 242.

[0039] The deflection scanning unit 226 can deflect the beamlets 214, 216, and 218 to scan the probe points 270, 272, and 274 over the surface area of ​​the wafer 230. In response to the incidence of the beamlets 214, 216, and 218 at the probe points 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 beamlets 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.) for reconstructing an SCPM image of structures on or below the surface area of ​​the wafer 230.

[0040] The generated signals may represent the intensities of the secondary charged particle beams 236, 238, and 240, and the generated signals may be provided to an image processing system 290 in communication 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 points (e.g., scanning probe points 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 result in different signal sensitivities to the movement of the scanning probe points.

[0041] The intensity of the secondary charged particle beams 236, 238, and 240 may vary depending on the external or internal structure of the wafer 230, and thus may indicate whether the wafer 230 includes a defect. In addition, as discussed above, the beamlets 214, 216, and 218 may be projected onto different locations on the top surface of the wafer 230, or onto different sides of a local structure of the wafer 230, to generate secondary charged particle beams 236, 238, and 240 that may have different intensities. Thus, by mapping the intensities of the secondary charged particle beams 236, 238, and 240 to regions of the wafer 230, the image processing system 290 may reconstruct an image that reflects the characteristics of the internal or external structure of the wafer 230.

[0042] 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 host, a terminal, a personal computer, any kind 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 through a medium such as an electrical conductor, a fiber optic cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, a wireless 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. The image acquirer 292 may thus 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 brightness and contrast adjustments 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, 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 scanned raw image data as an initial image, as well as post-processed images. 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.

[0043] 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 initial 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 in 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.

[0044] 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 beamlets 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.

[0045] 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 points 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 elastically interact 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 object (e.g., the electrons of the primary charged particle beam 210), wherein the kinetic energy of the interacting object is not converted into other forms of energy (e.g., heat, 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 inelastically interact with the material of the wafer 230 (e.g., in the form of inelastic scattering or collision). The inelastic interaction does not preserve the total kinetic energy of the interacting object, wherein some or all of the kinetic energy of the interacting object is converted into other forms of energy. For example, through inelastic interactions, the kinetic energy of some of the electrons of the primary charged particle beam 210 may cause electronic excitation and atomic transitions of the material. Such inelastic interactions 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 being inspected and the landing energy of the electrons of the primary charged particle beam 210 that land on the surface of the material, etc. The energy of the electrons 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 greater or less (or even the same) than the number of injected electrons of the primary charged particle beam 210.

[0046] The images generated by the SCPM can be used for defect inspection. For example, a generated image that captures 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 the tolerance level, a potential defect can be identified. For another example, the SCPM can scan multiple areas of a wafer, each area including a test device area that is designed to be the same, and generate multiple images that capture those test device areas during manufacturing. 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.

[0047] Reference now Figure 3 , which is a block diagram of an example inspection image simulation system consistent with embodiments of the present disclosure. Inspection image simulation system 300 (also referred to as "apparatus 300") may include one or more computers, servers, hosts, terminals, personal computers, any type of mobile computing devices, etc., or a combination thereof. It should be understood that in various embodiments, inspection image simulation system 300 may be a charged particle beam inspection system (e.g., Figure 1 It should also be understood that the inspection image simulation system 300 may include one or more components or modules that are separate from and communicatively coupled to the charged particle beam inspection system. In some embodiments, the inspection image simulation system 300 may include one or more components (e.g., software modules) that may be implemented in the controller 109 or the system 290, as discussed herein. Figure 3 As shown, the inspection image simulation system 300 may include a design data acquirer 310, a design data processor 320, a pattern information estimator 330, and an image renderer 340. According to some embodiments, the inspection image simulation system 300 may further include a parameter applicator 360.

[0048] According to some embodiments of the present disclosure, the design data acquirer 310 can acquire design data having a certain pattern. The design data can be a layout file for chip design, which is a golden image, or a graphic database system (GDS) format, a graphic database system II (GDS II) format, an open artwork system exchange standard (OASIS) format, a California Institute of Technology intermediate format (CIF), etc. The chip design may include a pattern or structure for inclusion on the chip. The pattern or structure may be a mask pattern for transferring features from a lithography mask or a reticle to a chip. In some embodiments, a layout in a GDS or OASIS format, etc. may include feature information stored in a binary file format, which represents a plane geometry, text, and other information related to the chip design. Figure 4A Design data 410 is shown. Figure 4A As shown, design data 410 includes pattern 411. In some embodiments, a user may generate design data 410 to include pattern(s) having a specified shape, size, density, etc. In some embodiments, a user may select a certain portion of design data 410 that has pattern(s) having a specified shape, size, density, etc.

[0049] Return to reference Figure 3 , the design data processor 320 may perform image processing operations on the design data 410 acquired by the design data acquirer 310. In some embodiments, the design data processor 320 may transform the design data 410 into a binary image. In some embodiments, the design data processor 320 may further perform corner rounding on the binary image. Figure 4A A binary image 420 is shown, which is obtained after corner rounding is performed on the binary image transformed from the design data 410. In some embodiments, the corner rounding operation can be performed to simulate a pattern formed on a wafer. Figure 4A , the binary image 420 includes a pattern 421 corresponding to the pattern 411 of the design data 410. Figure 4A As shown, the corners of pattern 421 on binary image 420 are rounded compared to the corners of pattern 411 on design data 410. Although corner rounding is shown as an image processing operation, it should be understood that any image processing operation that mimics the pattern formed on the wafer may be performed on design data 410. For example, pattern merging or pattern cropping may be performed on binary image 420.

[0050] According to some embodiments of the present disclosure, the parameter applicator 360 may apply one or more parameters to incorporate properties that a real SCPM image would have. According to some embodiments of the present disclosure, the inspection image simulation system 300 may consider parameters to simulate the SCPM image, including certain metrology-related properties, such as roughness, charging effect, distortion, grayscale distribution, voltage contrast, etc. In some embodiments, the parameter applicator 360 may apply the charging effect to the binary image 420. When the structure of the wafer includes insulating materials, the charging effect may cause image distortion. The parameter applicator 360 may apply an image distortion model 360-1 representing the charging effect on the binary image 420 to the binary image 420. In some embodiments, the charging effect may be applied by adjusting the distortion parameters of the image distortion model 360-1 corresponding to the charging effect. In some embodiments, the image distortion model 360-1 representing the distortion map may be adjusted by changing parameters related to rotation, scale, drift, etc. At this stage, the charging effect may be applied according to the field of view (FOV) of the processed binary image 425. In some embodiments, the distortion model 360-1 may be established based on observing real SCPM images, structures on the wafer, materials constituting the structures, inspection conditions, etc. In some embodiments, the image distortion model 360-1 may represent a distortion map caused by any reason other than charging effects. Figure 4A 4 shows a processed binary image 425 obtained after applying the image distortion model 360-1 to the binary image 420. Figure 4A , the processed binary image 425 includes a pattern 426 corresponding to the pattern 421 of the binary image 420. Figure 4A As shown, due to the introduction of distortion representing the charging effect, the shape or position of the pattern 426 on the processed binary image 425 may be different from the shape or position of the pattern 421. Although the subsequent process to be performed by the inspection image simulation system 300 will be illustrated with the processed binary image 425, it should be understood that when the distortion model 360-1 is not applied to the binary image 420, the subsequent process can be performed on the binary image 220.

[0051] In some embodiments, one or more image processes including application of distortion model 360-1 may be applied to binary image 425 to incorporate one or more parameters into the simulated inspection image. Figure 4AThe processed binary image 425 shows the resulting processed binary image obtained by applying roughness to the contour and applying the image distortion model 360-1 to the binary image 420. In some embodiments, the roughness of the contour can be modeled to be applied by the parameter applicator 360. In some embodiments, the roughness can be modeled using a power spectral density (PSD) function. In some embodiments, the roughness can be applied by adjusting the parameters of the roughness model according to the desired roughness level. For example, the roughness model can be adjusted by changing the parameters of the PSD function, such as standard deviation, longitudinal correlation coefficient, slope coefficient, etc. It should be noted that any model representing the roughness of the contour can be utilized in the present disclosure. In the present disclosure, the processed binary image 425 can refer to the resulting image after one or more image processes are performed on the binary image 420.

[0052] Return to reference Figure 3 , the pattern information estimator 330 can estimate the pattern information from the processed binary image 425. In some embodiments, the pattern information estimator 330 can estimate the distance information of the pattern 426. In some embodiments, the distance information of the pattern 426 can be estimated by performing a distance transform operation on the processed binary image 425. The distance transform converts the processed binary image 425 composed of feature pixels and non-feature pixels into an image in which all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. In some embodiments, the pixels constituting the outline of the pattern 426 can be identified as feature pixels. Figure 4B A distance image 430-1 estimated from the processed binary image 425 is shown. Figure 4B In the example, the distance image 430-1 includes a segment 431 corresponding to Figure 4A A segment 427 including a pattern 426 in the processed binary image 425 of FIG. Figure 4B As the distance to the nearest feature pixel (i.e., the outline of pattern 426) becomes shorter, distance image 430-1 becomes brighter. As the distance to the nearest feature pixel (i.e., the outline of pattern 426) becomes longer, distance image 430-1 becomes darker. Figure 4BAs shown, the distance image 430-1 is brighter along the circular outline of the pattern 426 and becomes darker as the distance from the outline increases. According to some embodiments, the distance image 430-1 can be used to determine the distance of a certain pixel in the segment 431 from the outline of the pattern 426. For example, the position of all pixels in the segment 431 can be defined by the distance from the outline of the pattern 426. In some embodiments, the distance image 430-1 can show whether a certain pixel in the segment 431 is located inside the outline of the pattern 426 or outside the pattern 426. For example, the distance image 430-1 can use a different color for the pixel located inside the outline of the pattern 426 than for the pixel located outside the outline. In some embodiments, although the brightness represents the distance magnitude of a certain pixel, the color can show whether the pixel is located inside or outside the outline of the pattern. In some embodiments, when a certain pixel is located inside the outline of the pattern 426, a negative sign (-) can be used, and when a certain pixel is located outside the outline of the pattern 426, a positive sign (+) can be used. Although obtaining distance information is described with respect to one pattern (eg, 426), it should be understood that distance information may be obtained in a similar manner for any or all patterns on the processed binary image 425.

[0053] In some embodiments, the pattern information estimator 330 may be configured from Figure 4B The degree information of the pattern 426 is estimated by using the distance image 430-1 of FIG. In some embodiments, the degree information of the pattern 426 can be estimated by performing a gradient operation on the distance image 430-1. In some embodiments, the direction of the maximum change on the distance image 430-1 can be obtained by performing a gradient operation on the distance image 430-1. Figure 4B 4 shows a gradient image 430-2 obtained by performing a gradient operation on the distance image 430-1. Figure 4B As shown, the gradient image 430-2 includes a segment 433 corresponding to the segment 431 of the distance image 430-1. Figure 4BAs shown, the gradient image 430-2 shows the direction of the maximum change on the distance image 430-1. Since the distance image 430-1 has the distance to the outline of the pattern 426 as the pixel value, the direction of the maximum change of the distance image 430-1 can be perpendicular to the outline of the pattern 426. As indicated by the direction lines 432 and 434 in the gradient image 430-2, in this example, the direction of the maximum change of the distance image 430-1 can be a radial direction. Although the gradient image 430-2 shows two direction lines 432 and 434, it should be understood that the gradient image 430-2 can have any number of direction lines indicating the direction of the maximum change of the distance image 430-1. In some embodiments, the rotation center of the direction lines 432 and 434 can be determined based on the gradient image 430-2. In this example, the rotation center of the direction lines 432 and 434 is the center of the segment 433. In some embodiments, a reference line extending from the rotation center can be set based on the gradient image 430-2 to determine the degree information of each pixel in the segment 433. In this example, direction line 434 can be used as a reference line to define 0°. According to some embodiments, the degree information of a certain pixel in segment 433 can be determined by the degree of the pixel and a reference line (e.g., reference line 434). For example, the degree of a certain pixel can be determined by the angle between a line from the center to the corresponding pixel and the reference line. Figure 4B The gradient image 430-2 shows that the direction lines range from 0° to 360° (ie, a degree range of 360°), but it should be understood that the degree range may be different depending on the pattern shape, the gradient image 430-2, etc. For example, the degree range of a certain pattern may be less than 360°.

[0054] According to some embodiments of the present disclosure, the position of each pixel in segment 433 can be determined based on the distance information and degree information of segment 433. For example, the position of a pixel can be specified as the distance from the outline of pattern 426 and the degree from a reference line. Although some embodiments of the present disclosure are shown using circular patterns (e.g., pattern 426), it should be understood that the present disclosure can be applied to patterns of any shape having a closed-loop pattern. For example, pixels in a segment having any closed-loop pattern can be specified by defining the position of a pixel in a segment using the distance from the pattern outline and the degree from a reference line. In the present disclosure, a closed-loop pattern may include any polygonal type of pattern, such as a rectangular pattern, a star pattern, etc. In some embodiments, a closed-loop pattern may also include a line pattern, because a line pattern also has a width and a length.

[0055] Return to reference Figure 3 , the image renderer 340 may render a grayscale image corresponding to the processed binary image 425. According to some embodiments of the present disclosure, the image renderer 340 may render an image using grayscale distribution data corresponding to the processed binary image 425. Figure 4C A grayscale image 440 rendered using the grayscale distribution data 340 - 1 is shown. Figure 4C The gray level distribution data 340-1 shown is an example gray level distribution along line 442 in segment 441. Figure 4C In FIG. 4 , line 442 is at 45° to reference line 443, and grayscale distribution data 340-1 represents the grayscale of pixels located along line 442. Figure 4C In the grayscale distribution data 340-1, the x-axis represents the distance from the outline of the pattern 426, wherein the distance 0 represents the outline of the pattern 426, and the distance with a negative sign (-d) represents the distance d from the outline of the pattern inside the pattern 426, and the distance with a positive sign (+d) represents the distance d from the outline of the pattern outside the pattern 426. Figure 4C Grayscale distribution data 340-1 along a line 442 at 45° is shown, but it should be understood that grayscale distribution data along multiple lines at different degrees are used to generate the grayscale image of segment 441. It should also be understood that other portions of grayscale image 440 can be rendered in a similar manner to generating segment 441.

[0056] According to some embodiments of the present disclosure, the grayscale distribution data 340-1 may be developed based on a real SCPM image, a simulated image from a simulator based on a physical model, or user-defined grayscale distribution data. Figure 5 How to develop grayscale distribution data is explained in the present disclosure. In some embodiments, the grayscale distribution data 340-1 can be modified according to grayscale distribution data extracted from a real SCPM image or a simulated image from a physical model-based simulator, or according to user-defined grayscale distribution data. When modifying existing grayscale distribution data, the user can change the grayscale distribution to reflect the properties that the user intends to observe from the inspection image. In some embodiments, existing grayscale distribution data developed from a SCPM image having a pattern, size, or density different from the design data 410 can be used to simulate an inspection image corresponding to the design data 410. In this case, when rendering an image corresponding to the design data 410, the existing grayscale distribution data can be modified according to the difference between the design data 410 and the SCPM image from which the existing grayscale distribution data was extracted. According to some embodiments of the present disclosure, the grayscale distribution data 340-1 can be obtained by modifying the existing grayscale distribution data of a non-simulated image or a simulated image having a pattern type, size, or density similar to the design data 410. Therefore, according to some embodiments of the present disclosure, inspection images with various patterns, sizes, densities, etc. can be simulated.

[0057] According to some embodiments of the present disclosure, a user may determine which grayscale distribution data to apply when rendering a grayscale image. Figure 4D Shows how the grayscale distribution data affects the rendered grayscale image. Figure 4D The binary image format shows the Figure 4A The design data 410 corresponds to the design data 460 having different patterns. Figure 4D , three grayscale images 440-2, 440-3, and 440-4 rendered by applying three different grayscale distribution data 340-2, 340-3, and 340-4 to design data 460, respectively, are shown. For example, grayscale image 440-2 is obtained by applying grayscale distribution data 340-2 to design data 460, and so on. Figure 4C Similar to the gray level distribution data 340-1, it should be noted that Figure 4D The three grayscale distribution data 340-2, 340-3, and 340-4 also show grayscale distribution data only along a line at a certain angle for a pattern in the design data 460. Figure 4D As shown, the three resulting grayscale images 440-2, 440-3, and 440-4 are different from each other. According to some embodiments of the present disclosure, the user can obtain a desired grayscale image by adjusting the grayscale distribution data to be applied to the design data. Although not shown, it should be noted that after performing one or more processes on the design data 460, the rendered grayscale images 440-2, 440-3, and 440-4 are obtained by applying the grayscale distribution data 340-2, 340-3, and 340-4 to the design data 460.

[0058] Return to reference Figure 3 In some embodiments, the parameter applicator 360 may apply the parameters that the user intends to consider in the simulated inspection image. In some embodiments, the charging effect may be applied to each segment 441 on the grayscale image 440. According to some embodiments, a model representing the charging effect may be applied to the grayscale image 440. In some embodiments, the charging effect of the insulating or poorly conductive material irradiated by the electron beam may affect the resulting SCPM image. The charging effect on the SCPM image may result in certain voltage contrast patterns on the SCPM image. In some embodiments, the charging effect may cause the voltage contrast on the SCPM image to darken or lighten. In some embodiments, a model representing the charging effect may be generated according to the material of the structure formed on the wafer, the pattern shape, the intensity of the irradiation beam, the scanning direction, etc. In some embodiments, the parameter applicator 360 may apply a model representing the charging effect of each segment 441 on the grayscale image 440. In some embodiments, the model representing the charging effect may be adjusted by adjusting parameters related to the charging direction, tail length, contrast value, grayscale value, pattern profile, etc. Figure 4CThe resulting grayscale image 450 after the charging effect is applied is shown. Figure 4C As shown, the resulting grayscale image 450 is different from the grayscale image 440 according to the charging effect applied to the grayscale image 440. For example, the resulting grayscale image 450 is different from the grayscale image 440 in various aspects, such as contrast, pattern outline, grayscale, etc.

[0059] In some embodiments, the resulting grayscale image 450 can be output as the output data 350 of the system 300. In some embodiments, one or more parameters can be applied to the resulting grayscale image 450, and its output data can be output as the output data 350 of the system 300. In some embodiments, the output data 350 can be packaged in some image format including identification information of pattern shape, size, density, etc. In some embodiments, the output data 350 can be in any other format that can be used in a later process, such as by a metrology tool.

[0060] Figure 5 is a block diagram of an example grayscale distribution extraction system 500 consistent with embodiments of the present disclosure. The grayscale distribution extraction system 500 (also referred to as "apparatus 500") may include one or more computers, servers, mainframes, terminals, personal computers, any kind of mobile computing devices, etc., or a combination thereof. It should be understood that in various embodiments, the grayscale distribution extraction system 500 may be a charged particle beam inspection system (e.g., Figure 1 It should also be understood that the grayscale distribution extraction system 500 may include one or more components or modules that are separate from the charged particle beam inspection system and communicatively coupled to the system. In some embodiments, the grayscale distribution extraction system 500 may include one or more components (e.g., software modules) that may be implemented in the controller 109 or the system 290, as discussed herein. It should be understood that in various embodiments, the grayscale distribution extraction system 500 may be Figure 3 The inspection image simulation system 300 may be a part of the inspection image simulation system 300, or may be separated therefrom. Figure 5 As shown, the grayscale distribution extraction system 500 may include an image acquirer 510 , a contour extractor 520 , a pattern information estimator 530 , and a grayscale distribution generator 540 .

[0061] According to some embodiments of the present disclosure, the image acquirer 510 may acquire an inspection image as an input image. In some embodiments, the inspection image is an SCPM image of a sample or a wafer. In some embodiments, the inspection image may be a SCPM image of a sample or a wafer. Figure 1 EBI System 100 or Figure 2The inspection image generated by the electron beam tool 104. In some embodiments, the image acquirer 510 can obtain the inspection image from a storage device or system storing the inspection image. Fig. 6A An example inspection image 610 including a pattern 611 is shown. Fig. 6A As shown, inspection image 610 may include pattern 611 having a certain shape, size, and density.

[0062] Return to reference Figure 5 , the contour extractor 520 may extract contour information of the pattern(s) on the inspection image 610. In some embodiments, the contour information of the pattern 611 may include information of the boundary line(s) of the pattern 611. In some embodiments, the boundary line of the pattern may be a line for determining an outer shape of the pattern, a line for determining an inner shape of the pattern, a boundary line between different textures in the pattern, or other types of lines that may be used to identify the pattern. Fig. 6A An example contour extraction image 620 of the inspection image 610 is shown. Fig. 6A As shown, a contour 621 of the pattern 611 is indicated in the contour extraction image 620 .

[0063] Return to reference Figure 5 , the pattern information estimator 530 can estimate the pattern information from the contour extraction image 620. In some embodiments, the pattern information estimator 630 can estimate the distance information of the pattern 611. In some embodiments, the distance information of the pattern 611 can be estimated by performing a distance transformation operation on the contour extraction image 620. The distance transformation converts the contour extraction image 620 composed of feature pixels and non-feature pixels into an image in which all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. In some embodiments, the pixels constituting the contour 621 of the pattern 611 can be identified as feature pixels. Fig. 6A A distance image 630-1 estimated from the contour extraction image 620 is shown. Fig. 6A In FIG. 6 , the distance image 630-1 includes a segment 631 corresponding to the segment 622 including the contour 621 in the contour extraction image 620. Fig. 6A In FIG. 6 , the distance image 630-1 becomes brighter as the distance from the contour 621 becomes shorter. The distance image 630-1 becomes darker as the distance from the contour 621 becomes longer. Fig. 6AAs shown, the distance image 630-1 is brighter along the circular contour 621 and becomes darker as the distance from the contour 621 increases. According to some embodiments, the distance image 630-1 can be used to determine the distance of a certain pixel in the segment 631 from the contour 621 of the pattern 611. For example, the positions of all pixels in the segment 631 can be defined by the distance from the contour 621 of the pattern 611. In some embodiments, the distance image 630-1 can show whether a certain pixel in the segment 631 is located inside the contour 621 or outside the contour 621. For example, the distance image 630-1 can use a different color for the pixel located inside the contour 621 than for the pixel located outside the contour 621. In some embodiments, while the brightness represents the distance magnitude of a certain pixel, the color can show whether the pixel is located inside or outside the pattern contour. In some embodiments, when a certain pixel is located inside the contour 621, a negative sign (-) can be used, and when a certain pixel is located outside the contour 621, a positive sign (+) can be used. Although obtaining distance information is described with respect to one pattern (eg, 611 ), it should be understood that distance information may be obtained in a similar manner for any or all patterns on the contour extraction image 620 .

[0064] In some embodiments, the pattern information estimator 530 may be configured from Fig. 6A The degree information of the pattern 611 is estimated by using the distance image 630-1 in FIG. In some embodiments, the degree information of the pattern 611 can be estimated by performing a gradient operation on the distance image 630-1. In some embodiments, the direction of the maximum change on the distance image 630-1 can be obtained by performing a gradient operation on the distance image 630-1. Fig. 6A 6 shows a gradient image 630-2 obtained by performing a gradient operation on the distance image 630-1. Fig. 6A As shown, the gradient image 630-2 includes a segment 633 corresponding to the segment 631 of the distance image 630-1. Fig. 6AAs shown, the gradient image 630-2 shows the direction of the maximum change on the distance image 630-1. Since the distance image 630-1 has the distance to the contour 621 as the pixel value, the direction of the maximum change of the distance image 630-1 can be perpendicular to the contour 621 of the pattern 611. As indicated by the direction line 634 in the gradient image 630-2, in this example, the direction of the maximum change of the distance image 630-1 can be a radial direction. Although the gradient image 630-2 shows one direction line 634, it should be understood that the direction image 630-2 can have any number of direction lines indicating the direction of the maximum change of the distance image 630-1. In some embodiments, the rotation center of the direction line 634 can be determined based on the gradient image 630-2. In this example, the rotation center of the direction line 634 is the center of the segment 633. In some embodiments, a reference line extending from the rotation center can be set based on the gradient image 630-2 to determine the degree information of each pixel in the segment 633. In this example, the direction line 634 can be used as a reference line to define 0°. According to some embodiments, the degree information of a certain pixel in segment 633 can be determined by the degree of the pixel and a reference line (e.g., reference line 634). For example, the degree of a certain pixel can be determined by the angle between a line from the center to the corresponding pixel and the reference line. Fig. 6A The gradient image 630-2 shows that the direction lines range from 0° to 360° (ie, a degree range of 360°), but it should be understood that the degree range may be different depending on the pattern shape, the gradient image 630-2, etc. For example, the degree range of a certain pattern may be less than 360°.

[0065] According to some embodiments of the present disclosure, the position of each pixel in segment 633 can be determined based on the distance information and degree information of segment 633. For example, the position of a pixel can be specified as the distance from the pattern outline 621 and the degree from the reference line. Although some embodiments of the present disclosure are shown using circular patterns (e.g., pattern 611), it should be understood that the present disclosure can be applied to patterns of any shape having a closed-loop pattern. For example, pixels in a segment having any closed-loop pattern can be specified by defining the position of a pixel in a segment using the distance from the pattern outline and the degree from the reference line. In the present disclosure, a closed-loop pattern may include any polygonal type of pattern, such as a rectangular pattern, a star pattern, etc. In some embodiments, a closed-loop pattern may also include a line pattern, because a line pattern also has a width and a length.

[0066] Return to reference Figure 5, the grayscale distribution generator 540 may generate grayscale distribution data corresponding to the inspection image 610. According to some embodiments of the present disclosure, the grayscale distribution generator 540 may extract grayscale distribution data of the inspection image 610 according to the pattern information estimated in the pattern information estimator 530. In some embodiments of the present disclosure, the grayscale distribution generator 540 may extract grayscale distribution data according to the distance information and degree information of each pattern obtained in the pattern information estimator 530. Figure 6B A gray level distribution 640 corresponding to a segment 612 including a pattern 611 in an inspection image 610 is shown. Figure 6B As shown, within the degree range estimated in the pattern information estimator 530 (e.g., 360°), the grayscale distribution data for the segment 612 can be extracted along the direction line 643 that forms a certain angle θ from the rotation center 641 to the reference line 642. According to some embodiments of the present disclosure, the grayscale distribution data for the segment 612 can be extracted along a plurality of direction lines 643 that form different angles θ to the reference line 642. For example, the grayscale distribution data for the segment 612 can be extracted along a plurality of direction lines 643 that rotate at equal angles.

[0067] Figure 6C Grayscale distribution data 645 extracted from the grayscale distribution 640 corresponding to the section 612 including the pattern 611 in the inspection image 610 is shown. Figure 6C In , the x-axis represents the distance from the outline 621 of the pattern 611, wherein the distance 0 represents the outline 621 of the pattern 611, the distance with a negative sign (-) represents the distance from the outline 621 of the pattern 611 inside the pattern, and the distance with a sign (+) represents the distance from the outline 621 of the pattern 611 outside the pattern. Figure 6C In , the y-axis represents the grayscale value. Figure 6C , the grayscale value is sampled along the direction line 643 within each rotation angle of 10°. For example, when the degree θ is equal to 0°, the grayscale value of the direction line 643 is indicated as a grayscale mark next to the number "0", and when the degree θ is equal to 10°, the grayscale value of the direction line 643 is indicated as a grayscale mark next to the number "1", and similarly, when the degree θ is equal to 350°, the grayscale value of the direction line 643 is indicated as a grayscale mark next to the number "35".

[0068] like Figure 6CAs shown, the grayscale value of each direction line 643 can be modeled as the grayscale distribution along the corresponding direction line 643. According to some embodiments of the present disclosure, the grayscale distribution of each direction line 643 can be modeled by the average value and standard deviation of the grayscale values ​​of the pixels located along the direction line 643. In this example, each segment 612 can have 36 grayscale distributions along 36 direction lines 643. According to some embodiments of the present disclosure, the grayscale distribution of the segment 612 can be modeled by the average value and standard deviation of the grayscale values ​​of the pixels located along the 36 direction lines 643. In this example, the grayscale distribution can be generated as two-dimensional data. Although the grayscale distribution data of the segment 612 of the inspection image 610 is extracted along 36 direction lines 643, it should be understood that according to the embodiment, pattern shape, target accuracy, etc., the grayscale distribution data of the inspection image can be extracted along any number of lines in any shape.

[0069] According to some embodiments of the present disclosure, the grayscale distribution can be modeled on a pixel-by-pixel basis on the pattern 611. In some embodiments, it can be assumed that the grayscale distribution of the same pattern follows a Gaussian distribution. In some embodiments, the grayscale values ​​of pixels on multiple identical patterns can be extracted from the corresponding grayscale distribution. For example, the inspection image 610 includes multiple repeated patterns 611, such as N patterns 611, and the grayscale values ​​of pixels on the N patterns 611 can be extracted. In some embodiments, it is assumed that the grayscale values ​​of N pixels at corresponding positions on the N patterns 611 follow a Gaussian distribution. As shown in reference Fig. 6A As described above, the position of each pixel on each pattern 611 can be specified by the distance from the pattern outline and the degree from the reference line. Therefore, for each relative pixel position on the pattern 611, N grayscale values ​​can be extracted from the N patterns 611. In some embodiments, the grayscale distribution of each relative pixel position on the pattern 611 can be modeled by fitting a Gaussian distribution model to the N extracted grayscale values. For example, the Gaussian distribution model obtained by fitting to the extracted grayscale values ​​can be expressed by equation (1):

[0070]

[0071] In equation (1), x represents the position of a pixel on pattern 611, μ represents the mean value of a Gaussian distribution model, and σ represents the standard deviation of the Gaussian distribution model. Position x can be represented by a distance from the pattern contour and a degree from a reference line. The mean value μ and the standard deviation σ can be obtained by fitting a Gaussian distribution to N extracted grayscale values ​​at position x. Similarly, a grayscale distribution can be modeled for the remaining pixel positions on pattern 611. According to some embodiments of the present disclosure, each pixel position on pattern 611 can have a corresponding grayscale distribution that follows a Gaussian distribution. In some embodiments, each pixel position on pattern 611 can be modeled by a Gaussian distribution with an associated mean value μ or a standard deviation σ. In some embodiments, the Gaussian distribution representing the grayscale distribution for different pixel positions can have different mean values ​​μ or standard deviations σ. Although it has been described that the grayscale distribution of pixels on pattern 611 is obtained, it should be understood that in some embodiments, the grayscale distribution of pixels on an area (e.g., segment 612) and a surrounding area including pattern 611 can be obtained. When the grayscale distribution of the pattern 611 is modeled based on multiple patterns on one image, it should be understood that the grayscale distribution of the pattern can be modeled based on multiple patterns from multiple images.

[0072] According to some embodiments of the present disclosure, it is assumed that similar or identical patterns have similar or identical grayscale distributions. According to some embodiments of the present disclosure, a grayscale distribution developed for a pattern (e.g., pattern 611) can be used to simulate an inspection image corresponding to design data (e.g., design data 410) having a similar or identical pattern in terms of pattern shape, size, or density. When grayscale distribution data is applied to simulate an inspection image having a similar or identical pattern, the grayscale value of each pixel on pattern 611 can be randomly selected from a corresponding Gaussian distribution model based on probability, system requirements, etc. For example, when simulating an inspection image including 100 pixels, 100 grayscale values ​​can be selected from corresponding 100 Gaussian distribution models for the pattern (e.g., pattern 611).

[0073] According to some embodiments of the present disclosure, grayscale distribution data can also be obtained based on a simulated image from a physical model-based simulator (e.g., Hyperlith, eScatter, etc.). In some embodiments, the grayscale distribution data can be user-defined grayscale distribution data, such as using a Fraser model. In some embodiments, existing grayscale distribution data developed from a SCPM image having a pattern, size, or density different from that from the design data 410 can be used to simulate an inspection image corresponding to the design data 401. In this case, when rendering an image corresponding to the design data 410, the existing grayscale distribution data can be modified based on the difference between the design data 410 and the SCPM image from which the existing grayscale distribution data is extracted. Therefore, according to some embodiments of the present disclosure, inspection images with various patterns, sizes, densities, etc. can be simulated.

[0074] Fig. 7A An example performance evaluation of an inspection image simulation system consistent with an embodiment of the present disclosure is shown. Fig. 7A , the first image is a real SCPM image 710, the second image is a simulated image 720, and the third image is a residual image 730 obtained by subtracting the simulated image 720 from the real SCPM image 710. In this example, the simulated image 720 is obtained by Figure 3 The inspection image simulation system 300 generates an image that combines the parameters of the SCPM image 710 (e.g., distortion, voltage contrast pattern, grayscale distribution, etc.). Fig. 7A As shown, the residual image 730 does not contain pattern-related fingerprint features. It should be understood that according to embodiments of the present disclosure, pattern-related features such as pattern contours, critical dimensions, roughness, etc. can be accurately captured from the simulated image 720 generated by the inspection image simulation system 300.

[0075] FIG. 7B to FIG. 7C Example simulated images of various patterns generated using an inspection image simulation system consistent with an embodiment of the present disclosure are shown. Figure 7B , the images on the left column are design data 741, 743, and 745 having various patterns and densities and in a binary format. Figure 7B The images on the right column are from Figure 3 The inspection image simulation system 300 generates simulation images 742, 744 and 746 based on the corresponding design data 741, 743 and 745 on the left side thereof, respectively. Figure 7C Shown by Figure 3 The inspection image simulation system 300 generates design data 751 and its corresponding simulation image 752 . Figure 7C Further shown is an enlarged image 753 of a portion of the simulated image 752. FIG. 7B to FIG. 7CAs shown, it should be noted that the inspection image simulation technology of the present disclosure can be applied to various patterns and densities, including but not limited to line patterns (such as design pattern 745), complex circuit patterns (such as design pattern 752), etc.

[0076] Figure 8 is a process flow diagram representing an example method for simulating inspection images consistent with embodiments of the present disclosure. For illustrative purposes, reference will be made to Figure 3 The inspection image simulation system 300 describes a method for simulating an inspection image.

[0077] In step S810, design data may be acquired. Step S810 may be performed, for example, by a design data acquirer 310 or the like. In some embodiments, the design data may be a layout file for a chip design in a Golden Image or Graphic Database System (GDS) format, a Graphic Database System II (GDS II) format, an Open Artwork System Interchange Standard (OASIS) format, a California Institute of Technology Intermediate Format (CIF), or the like. The chip design may include a pattern or structure for inclusion on a chip. The pattern or structure may be a mask pattern for transferring features from a photolithography mask or reticle to a chip. In some embodiments, a layout in a GDS or OASIS format, or the like, may include feature information stored in a binary file format that represents plane geometry, text, and other information related to the chip design. Such as Figure 4A As shown, design data 410 includes pattern 411. In some embodiments, a user can generate design data 410 to include a pattern having a specified shape, size, density, etc. In some embodiments, a specific portion of design data 410 can be selected that has a pattern with a specified shape, size, density, etc.

[0078] In step S820, the design data may be processed. Step S820 may be performed by, for example, the design data processor 310. In some embodiments, the design data 410 may be transformed into a binary image. In some embodiments, corner rounding may be performed on the binary image. Figure 4A A binary image 420 is shown, which is obtained after corner rounding is performed on the binary image transformed from the design data 410. In some embodiments, the corner rounding operation can be performed to simulate a pattern formed on a wafer. In some embodiments, pattern merging or pattern cropping can be further performed on the binary image 420.

[0079] According to some embodiments of the present disclosure, one or more parameters may be applied to incorporate properties that a real SCPM image would have. According to some embodiments of the present disclosure, method 800 may consider parameters to simulate a SCPM image, which includes certain metrology-related properties, such as roughness, charging effects, distortion, grayscale distribution, voltage contrast, etc. Method 800 may optionally include step S860-1. In step S860-1, one or more parameters may be applied to the binary image 420. Step S820 may be performed by, for example, parameter applicator 360, etc. In step S860-1, charging effects may be applied to the binary image 420. When the structure of the wafer includes insulating materials, charging effects may cause image distortion. An image distortion model 360-1 representing charging effects on the binary image 420 may be applied to the binary image 420. In some embodiments, the image distortion model 360-1 representing the distortion map may be adjusted by changing parameters related to rotation, scale, drift, etc. Figure 4A A processed binary image 425 obtained after applying the image distortion model 360 - 1 to the binary image 420 is shown.

[0080] In step S830, pattern information may be estimated from the processed binary image 425. Step S830 may be performed by, for example, the pattern information estimator 330. In some embodiments, in step S830, distance information and degree information of the pattern 426 may be estimated. Figure 4B The estimated distance information and degree information are shown, so for simplicity and brevity, a detailed description of the estimated distance information and degree information will be omitted here. According to some embodiments of the present disclosure, the position of each pixel in the segment 433 can be determined based on the distance information and degree information of the segment 433. For example, the position of the pixel can be specified as the distance from the outline of the pattern 426 and the degree from the reference line.

[0081] In step S840, the grayscale distribution data may be used to render an image. Step S840 may be performed by, for example, the image renderer 340. In some embodiments, the grayscale distribution data corresponding to the processed binary image 420 may be used to render a grayscale image corresponding to the processed binary image 425. Figure 4C A rendered image is shown, so for simplicity and brevity, a detailed description of rendering an image corresponding to the processed binary image 420 will be omitted here. According to some embodiments of the present disclosure, the grayscale distribution data 340-1 can be developed based on a real SCPM image, a simulated image from a physical model-based simulator, or user-defined grayscale distribution data. Figure 5It is explained how the grayscale distribution data is developed, so for the sake of clarity and simplicity, its detailed explanation will be omitted. The grayscale distribution data 340-1 can be modified according to the grayscale distribution data from a real SCPM image or a simulated image from a physical model-based simulator, or according to user-defined grayscale distribution data. When modifying existing grayscale distribution data, the user can change the grayscale distribution to reflect the properties that the user intends to observe from the inspection image. In some embodiments, existing grayscale distribution data developed from a SCPM image having a pattern, size, or density different from the design data 410 can be used to simulate an inspection image corresponding to the design data 410. In this case, when rendering an image corresponding to the design data 410, the existing grayscale distribution data can be modified according to the difference between the design data 410 and the SCPM image from which the existing grayscale distribution data was extracted.

[0082] The method 800 may optionally include step S860-2. In step S860-2, one or more parameters may be applied to the grayscale image 440. Step S860-2 may be performed by, for example, the parameter applicator 360. In step S860-2, a charging effect may be applied to each segment 441 on the grayscale image 440. According to some embodiments, a model representing the charging effect may be applied to the grayscale image 440. In some embodiments, the charging effect may cause the voltage contrast on the SCPIM image to darken or lighten. In some embodiments, a model representing the charging effect may be generated according to the material of the structure formed on the wafer, the pattern shape, the intensity of the irradiation beam, the scanning direction, etc. In some embodiments, the parameter applicator 360 may apply the model representing the charging effect for each segment 441 on the grayscale image 440. In some embodiments, the model representing the charging effect may be adjusted by adjusting parameters related to the charging direction, the tail length, etc. Figure 4C The resulting grayscale image 450 is shown after the charging effect is applied.

[0083] In some embodiments, the resulting grayscale image 450 can be output as the output data 350 of the system 300. In some embodiments, one or more parameters can be applied to the resulting grayscale image 450, and its output data can be output as the output data 350 of the system 300. In some embodiments, the output data 350 can be packaged in some image format including identification information of pattern shape, size, density, etc. In some embodiments, the output data 350 can be in any other format that can be used in a later process, such as by a metrology tool.

[0084] A non-transitory computer readable medium may be provided that stores a controller (eg, Figure 1A processor of the controller 109) executes instructions for image inspection, image acquisition, stage positioning, beam focusing, electric field adjustment, beam bending, focusing lens adjustment, activation of charged particle source, beam deflection, and method 800. 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 media, compact disk read-only memory (CD-ROM), any other optical data storage media, any physical media 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.

[0085] The embodiments may be further described using the following terms:

[0086] 1. A method for generating a simulated inspection image, comprising:

[0087] acquiring design data including a first pattern;

[0088] generating a first grayscale distribution corresponding to the design data; and

[0089] An image is rendered using the generated first grayscale distribution.

[0090] 2. The method of clause 1, wherein the first grayscale distribution is developed based on a non-simulated inspection image, based on a simulated image generated by a physics-based model simulator, or based on a user-defined grayscale distribution.

[0091] 3. A method according to clause 1 or 2, wherein generating the first gray level distribution comprises:

[0092] acquiring a non-simulation inspection image having a second pattern;

[0093] extracting a pattern contour from the non-simulated inspection image;

[0094] estimating pattern information of the extracted pattern contour;

[0095] generating a second grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0096] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0097] 4. The method of clause 3, wherein the first pattern and the second pattern differ in size, shape or density.

[0098] 5. A method according to clause 3 or 4, wherein generating the second gray level distribution comprises:

[0099] Gray level distribution of pixels on the second pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the second pattern.

[0100] 6. A method according to any one of clauses 3 to 5, wherein the pattern information comprises distance information and degree information of the second pattern.

[0101] 7. The method of clause 6, wherein the distance information and the degree information are used to define the positions of pixels on the second pattern.

[0102] 8. The method of clause 1 or 2, wherein generating the first grayscale distribution comprises:

[0103] generating a second grayscale distribution corresponding to the second pattern;

[0104] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0105] 9. The method according to any one of clauses 1 to 8, further comprising:

[0106] User defined parameters are incorporated into the image.

[0107] 10. The method of clause 9, wherein incorporating the user's defined parameters comprises:

[0108] performing corner rounding on the design data including the first pattern;

[0109] applying image distortion to the design data; or

[0110] Applying a charging effect on the rendered image.

[0111] 11. A method for generating a simulated inspection image, comprising:

[0112] acquiring a non-simulation inspection image having a first pattern;

[0113] extracting a pattern contour from the non-simulated inspection image;

[0114] estimating pattern information of the extracted pattern contour;

[0115] generating a first grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0116] The first grayscale distribution is generated by modifying the second grayscale distribution.

[0117] 12. The method of clause 11, wherein generating the first grayscale distribution comprises:

[0118] Gray level distribution of pixels on the first pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the first pattern.

[0119] 13. The method according to clause 11 or 12, wherein the pattern information comprises distance information and degree information of the first pattern.

[0120] 14. The method of clause 13, wherein the distance information and the degree information are used to define the position of pixels on the first pattern.

[0121] 15. The method according to any one of clauses 11 to 13, further comprising:

[0122] acquiring design data including a second pattern; and

[0123] An image is rendered using the generated first grayscale distribution.

[0124] 16. The method of clause 15, wherein the first pattern and the second pattern differ in size, shape or density.

[0125] 17. The method according to any one of clauses 11 to 16, further comprising:

[0126] User defined parameters are incorporated into the image.

[0127] 18. The method of clause 17, wherein incorporating the user's defined parameters comprises:

[0128] performing corner rounding on the design data including the second pattern;

[0129] applying image distortion to the design data; or

[0130] Applying a charging effect on the rendered image.

[0131] 19. A device for generating a simulated inspection image, comprising:

[0132] a memory storing an instruction set; and

[0133] at least one processor configured to execute the set of instructions to cause the apparatus to perform:

[0134] acquiring design data including a first pattern;

[0135] generating a first grayscale distribution corresponding to the design data; and

[0136] An image is rendered using the generated first grayscale distribution.

[0137] 20. The apparatus of clause 19, wherein the first grayscale distribution is developed based on a non-simulated inspection image, based on a simulated image generated by a physics-based model simulator, or based on a user-defined grayscale distribution.

[0138] 21. An apparatus according to clause 19 or 20, wherein when generating the first grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:

[0139] acquiring a non-simulation inspection image having a second pattern;

[0140] extracting a pattern contour from the non-simulated inspection image;

[0141] estimating pattern information of the extracted pattern contour;

[0142] generating a second grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0143] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0144] 22. The device of clause 21, wherein the first pattern and the second pattern differ in size, shape or density.

[0145] 23. An apparatus according to clause 21 or 22, wherein when generating the second grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform:

[0146] Gray level distribution of pixels on the second pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the second pattern.

[0147] 24. An apparatus according to any of clauses 21 to 23, wherein the pattern information comprises distance information and degree information of the second pattern.

[0148] 25. An apparatus as recited in clause 24, wherein the distance information and the degree information are used to define positions of pixels on the second pattern.

[0149] 26. An apparatus according to clause 19 or 20, wherein when generating the first grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:

[0150] generating a second grayscale distribution corresponding to the second pattern;

[0151] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0152] 27. An apparatus according to any of clauses 19 to 26, wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:

[0153] User defined parameters are incorporated into the image.

[0154] 28. The apparatus of clause 27, wherein in conjunction with the user-defined parameters, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform:

[0155] performing corner rounding on the design data including the first pattern;

[0156] applying image distortion to the design data; or

[0157] Applying a charging effect on the rendered image.

[0158] 29. A device for generating a simulated inspection image, comprising:

[0159] a memory storing an instruction set; and

[0160] at least one processor configured to execute the set of instructions to cause the apparatus to perform:

[0161] acquiring a non-simulation inspection image having a first pattern;

[0162] extracting a pattern contour from the non-simulated inspection image;

[0163] estimating pattern information of the extracted pattern contour;

[0164] generating a first grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0165] The first grayscale distribution is generated by modifying the second grayscale distribution.

[0166] 30. The apparatus of clause 29, wherein in generating the first grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform:

[0167] Gray level distribution of pixels on the first pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the first pattern.

[0168] 31. An apparatus according to clause 29 or 30, wherein the pattern information comprises distance information and degree information of the first pattern.

[0169] 32. An apparatus as described in clause 31, wherein the distance information and the degree information are used to define the position of pixels on the first pattern.

[0170] 33. An apparatus according to any of clauses 29 to 32, wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:

[0171] acquiring design data including a second pattern; and

[0172] An image is rendered using the generated first grayscale distribution.

[0173] 34. The device of clause 33, wherein the first pattern and the second pattern differ in size, shape or density.

[0174] 35. An apparatus according to any of clauses 29 to 34, wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:

[0175] User defined parameters are incorporated into the image.

[0176] 36. The apparatus of clause 35, wherein in conjunction with the user-defined parameter, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform:

[0177] performing corner rounding on the design data including the second pattern;

[0178] applying image distortion to the design data; or

[0179] Applying a charging effect on the rendered image.

[0180] 37. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising:

[0181] acquiring design data including a first pattern;

[0182] generating a first grayscale distribution corresponding to the design data; and

[0183] An image is rendered using the generated first grayscale distribution.

[0184] 38. A computer-readable medium according to clause 37, wherein the first grayscale distribution is developed based on a non-simulated inspection image, based on a simulated image generated by a physics-based model simulator, or based on a user-defined grayscale distribution.

[0185] 39. The computer-readable medium of clause 37 or 38, wherein in generating the first grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform:

[0186] acquiring a non-simulation inspection image having a second pattern;

[0187] extracting a pattern contour from the non-simulated inspection image;

[0188] estimating pattern information of the extracted pattern contour;

[0189] generating a second grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0190] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0191] 40. The computer-readable medium of clause 39, wherein the first pattern and the second pattern differ in size, shape, or density.

[0192] 41. The computer-readable medium of clause 39 or 40, wherein in generating the second grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform:

[0193] Gray level distribution of pixels on the second pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the second pattern.

[0194] 42. The computer-readable medium of any one of clauses 39 to 41, wherein the pattern information comprises distance information and degree information of the second pattern.

[0195] 43. The computer-readable medium of clause 42, wherein the distance information and the degree information are used to define a location of a pixel on the second pattern.

[0196] 44. The computer-readable medium of clause 37 or 38, wherein in generating the first grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform:

[0197] generating a second grayscale distribution corresponding to the second pattern;

[0198] The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

[0199] 45. The computer-readable medium of any of clauses 37 to 44, wherein the set of instructions executable by at least one processor of the computing device causes the computing device to further perform:

[0200] User defined parameters are incorporated into the image.

[0201] 46. ​​The computer-readable medium of clause 45, wherein the set of instructions executable by at least one processor of the computing device, when combined with the user-defined parameters, causes the computing device to perform:

[0202] performing corner rounding on the design data including the first pattern;

[0203] applying image distortion to the design data; or

[0204] Applying a charging effect on the rendered image.

[0205] 47. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising:

[0206] acquiring a non-simulation inspection image having a first pattern;

[0207] extracting a pattern contour from the non-simulated inspection image;

[0208] estimating pattern information of the extracted pattern contour;

[0209] generating a first grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; and

[0210] The first grayscale distribution is generated by modifying the second grayscale distribution.

[0211] 48. The computer-readable medium of clause 47, wherein in generating the first grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform:

[0212] Gray level distribution of pixels on the first pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the first pattern.

[0213] 49. A computer-readable medium according to clause 47 or 48, wherein the pattern information includes distance information and degree information of the first pattern.

[0214] 50. The computer-readable medium of clause 49, wherein the distance information and the degree information are used to define a location of a pixel on the first pattern.

[0215] 51. The computer-readable medium of any of clauses 47 to 50, wherein the set of instructions executable by at least one processor of the computing device causes the computing device to further perform:

[0216] acquiring design data including a second pattern; and

[0217] An image is rendered using the generated first grayscale distribution.

[0218] 52. The computer-readable medium of clause 51, wherein the first pattern and the second pattern differ in size, shape, or density.

[0219] 53. The computer-readable medium of any one of clauses 47 to 52, wherein the set of instructions executable by at least one processor of the computing device causes the computing device to further perform:

[0220] User defined parameters are incorporated into the image.

[0221] 54. The computer-readable medium of clause 53, wherein the set of instructions executable by at least one processor of the computing device, when combined with the user-defined parameters, causes the computing device to perform:

[0222] performing corner rounding on the design data including the second pattern;

[0223] applying image distortion to the design data; or

[0224] Applying a charging effect on the rendered image.

[0225] The block diagrams in the accompanying drawings may illustrate the architecture, functions and operations of possible implementations of systems, methods and computer hardware or software products according to various exemplary embodiments of the present disclosure. In this regard, each box in the schematic diagram may represent certain arithmetic or logic operations that can be implemented using hardware such as electronic circuits. The box may 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 shown in the figure. For example, depending on the functions involved, two boxes shown in succession may be executed or implemented substantially simultaneously, or two boxes may sometimes be executed in reverse order. Some boxes may also be omitted. It should also be understood that each box of the block diagram and the combination of boxes may be implemented by a dedicated hardware-based system that performs a specified function or action, or by a combination of dedicated hardware and computer instructions.

[0226] It should be understood that the embodiments of the present disclosure are not limited to the exact constructions described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. 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 by considering the specification and practice of the invention disclosed herein. It is intended that the description and examples be regarded as exemplary only, with the true scope and spirit of the present invention being indicated by the appended claims.

Claims

1. A device for generating a simulated inspection image, comprising: Memory, storing instruction sets; as well as at least one processor configured to execute the set of instructions to cause the apparatus to perform: acquiring design data including a first pattern; generating a first grayscale distribution corresponding to the design data; and An image is rendered using the generated first grayscale distribution.

2. The apparatus of claim 1, wherein the first grayscale distribution is developed based on a non-simulated inspection image, based on a simulated image generated by a physics-based model simulator, or based on a user-defined grayscale distribution.

3. The apparatus of claim 1 , wherein when generating the first grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform: acquiring a non-simulation inspection image having a second pattern; extracting a pattern contour from the non-simulated inspection image; estimating pattern information of the extracted pattern contour; generating a second grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; as well as The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern. The device of claim 3 , wherein the first pattern and the second pattern differ in size, shape or density.

5. The apparatus of claim 3, wherein when generating the second grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform: Gray level distribution of pixels on the second pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the second pattern. The apparatus according to claim 3 , wherein the pattern information comprises distance information and degree information of the second pattern. 7 . The apparatus of claim 6 , wherein the distance information and the degree information are used to define positions of pixels on the second pattern.

8. The apparatus of claim 1 , wherein when generating the first grayscale distribution, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform: generating a second grayscale distribution corresponding to the second pattern; The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

9. The apparatus of claim 1, wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform: User defined parameters are incorporated into the image.

10. The apparatus of claim 9, wherein in conjunction with the user defined parameters, the at least one processor is configured to execute the set of instructions to cause the apparatus to perform: performing corner rounding on the design data including the first pattern; applying image distortion to the design data; or A charging effect is applied on the rendered image.

11. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a computing device to cause the computing device to perform a method for generating a simulated inspection image, the method comprising: acquiring design data including a first pattern; generating a first grayscale distribution corresponding to the design data; as well as An image is rendered using the generated first grayscale distribution.

12. The computer-readable medium of claim 11, wherein the first grayscale distribution is developed based on a non-simulated inspection image, based on a simulated image generated by a physics-based model simulator, or based on a user-defined grayscale distribution.

13. The computer-readable medium of claim 11, wherein in generating the first grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform: acquiring a non-simulation inspection image having a second pattern; extracting a pattern contour from the non-simulated inspection image; estimating pattern information of the extracted pattern contour; generating a second grayscale distribution corresponding to the non-simulation inspection image based on the estimated pattern information; as well as The first grayscale distribution is generated by modifying the second grayscale distribution based on a difference between the first pattern and the second pattern.

14. The computer readable medium of claim 13, wherein the first pattern and the second pattern differ in size, shape, or density.

15. The computer-readable medium of claim 13, wherein in generating the second grayscale distribution, the set of instructions executable by at least one processor of the computing device causes the computing device to perform: Gray level distribution of pixels on the second pattern is modeled using a Gaussian distribution model based on gray level values ​​of pixels at corresponding positions on a plurality of patterns having the same pattern as the second pattern.

Images