Numerical compensation of SEM-induced charging using a diffusion-based model

Abstract

Systems and methods for image enhancement are disclosed. The method for enhancing an image may include acquiring a scanning electron microscope (SEM) image. The method may also include simulating diffuse charges associated with locations of the SEM image. The method may also include providing an enhanced SEM image based on the SEM image and the diffuse charges.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to EP application 19192198.0 filed on August 19, 2019, which is incorporated herein by reference in its entirety. Technical Field

[0003] The description herein relates to the field of image enhancement, and more particularly to scanning electron microscope (SEM) image enhancement. 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 defect-free. Inspection systems using optical microscopes or charged particle (e.g., electron) beam microscopes (such as scanning electron microscopes (SEMs)) can be used. SEMs can transfer electrons with a certain energy (e.g., 1 keV, 30 keV, etc.) to a surface and use a detector to record secondary or backscattered electrons that leave the surface. By recording such electrons for different excitation positions on the surface, images with nanometer-scale spatial resolution can be produced.

[0005] As the physical size of IC components continues to shrink, the accuracy and yield of defect detection become increasingly important. However, when inspecting electrically insulating materials, the quality of SEM images is often affected by charging artifacts caused by the SEM. In some cases, this charging artifact may result in a charge-induced critical dimension (CD) error of about 2 nanometers. There are several techniques that can be used to reduce charging artifacts in SEM imaging, such as, for example, coating the target sample surface with a conductive material, optimizing the landing energy of the electron beam in the SEM, using faster scanning, such as using a lower dose per SEM frame and using more SEM frames, using opposite scanning directions in the SEM, or using machine learning techniques to numerically remove SEM charging artifacts, etc. However, it should be noted that these techniques have different disadvantages. In particular, it should be noted that experimental techniques for avoiding charging (e.g., pseudo-random scanning) may be slow and result in low production yields. Machine learning techniques may lack insight into their training process. Numerical image processing techniques may lack physics-based interpretability. Further improvements are needed in this area. Summary of the invention

[0006] Embodiments of the present disclosure provide systems and methods for image enhancement. In some embodiments, the method for enhancing an image may include: acquiring a scanning electron microscope (SEM) image. The method may also include: simulating a diffuse charge associated with a position of the SEM image. The method may also include: providing an enhanced SEM image based on the SEM image and the diffuse charge.

[0007] In some embodiments, an inspection system is disclosed. The inspection system may include a memory storing an instruction set and a processor configured to execute the instruction set. The process may execute the instruction set to cause the inspection system to acquire a scanning electron microscope (SEM) image. The process may also execute the instruction set to cause the inspection system to simulate a diffuse charge associated with a location of the SEM image. The process may also execute the instruction set to cause the inspection system to provide an enhanced SEM image based on the SEM image and the diffuse charge.

[0008] In some embodiments, a non-transitory computer readable medium is disclosed. The non-transitory computer readable medium may store an instruction set that can be executed by at least one processor of a device to cause the device to perform a method. The method may include: acquiring a scanning electron microscope (SEM) image. The method may also include: simulating a diffuse charge associated with a location of the SEM image. The method may also include: providing an enhanced SEM image based on the SEM image and the diffuse charge. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system according to some embodiments of the present disclosure.

[0010] Figure 2 is a schematic diagram illustrating an exemplary electron beam tool according to some embodiments of the present disclosure, which may be Figure 1 Portions of an exemplary electron beam inspection system.

[0011] Figure 3 is a diagram of an exemplary process of SEM-induced charging effect according to some embodiments of the present disclosure.

[0012] Figure 4 is a schematic diagram of an exemplary image enhancement system 400 according to some embodiments of the present disclosure.

[0013] Figure 5A is an illustration of a pattern of a first exemplary SEM image overlaid with a SEM signal waveform according to some embodiments of the present disclosure.

[0014] Figure 5B is an illustration of a pattern of a second exemplary SEM image with SEM-induced artifacts according to some embodiments of the present disclosure.

[0015] FIG. 5C to FIG. 5D A diagram showing an example distribution of diffuse charge density generated by a primary electron beam scanned across a linear feature according to some embodiments of the present disclosure is shown.

[0016] Figure 5Eis a diagram of SEM waveforms showing two line-space patterns of SEM-induced artifacts without compensation according to some embodiments of the present disclosure.

[0017] Fig. 5F According to some embodiments of the present disclosure Figure 5E Illustration of the SEM waveform of the two line-space patterns in after charge compensation.

[0018] Fig. 6A is an illustration of a third exemplary SEM image of a pattern having curved features according to some embodiments of the present disclosure.

[0019] Figure 6B The invention describes some embodiments according to the present disclosure. Fig. 6A Graphic illustration of the outline logo of a pattern.

[0020] Figure 6C The invention is a diagram illustrating some embodiments of the present disclosure based on Figure 6B Illustration of the height mask determination of the identified contours.

[0021] Fig.6D The invention is a diagram illustrating some embodiments of the present disclosure based on Figure 6B Illustration of the charge density mask determination of the identified contours.

[0022] FIG. 6E to FIG. 5F The invention describes some embodiments according to the present disclosure. Fig.6D Illustration of the directional blurring of the charge density mask.

[0023] Figure 6G is an illustration of a pattern of a third exemplary SEM image after charging artifact compensation according to some embodiments of the present disclosure.

[0024] Figure 6H is an illustration of an SEM waveform overlaid on top of a pattern of a third exemplary SEM image without compensation, according to some embodiments of the present disclosure.

[0025] Fig.6I is an illustration of an SEM waveform overlaid on top of a third exemplary SEM image of a pattern with compensation according to some embodiments of the present disclosure.

[0026] Figure 7 is a flow chart illustrating an exemplary image enhancement method according to some embodiments of the present disclosure.

[0027] Figure 8 is a flow chart illustrating an exemplary parameter optimization method for image enhancement according to some embodiments of the present disclosure.

[0028] Fig. 9is a flow chart illustrating an exemplary image enhancement method according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0029] 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 specified. The implementations set forth in the following description of the exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of devices and methods according to aspects related to the subject matter described in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the present disclosure is not limited thereto. Other types of charged particle beams may be similarly applied. In addition, other imaging systems such as optical imaging, photoelectric detection, x-ray detection, etc. may be used.

[0030] Electronic devices consist of circuits formed on a silicon wafer called a substrate. Many circuits can be formed together on the same silicon wafer and are called integrated circuits or ICs. The size of these circuits has been significantly reduced so that many of them can be mounted on a substrate. For example, an IC chip in a smartphone can be as small as a thumbnail and can also include more than 2 billion transistors, each of which is less than 1 / 1000 the size of a human hair.

[0031] Manufacturing these extremely small ICs is a complex, time-consuming and expensive process, often involving hundreds of individual steps. An error in even one step can result in a defect in the finished IC, rendering it useless. Therefore, one goal of the manufacturing process is to avoid such defects in order to maximize the number of functional ICs manufactured in the process, i.e., to increase the overall yield of the process.

[0032] One component of improving yield is monitoring the chip manufacturing process to ensure that it produces a sufficient number of functional integrated circuits. One way to monitor the process is to inspect the chip circuit structure at various stages of its formation. Inspection can be performed using a scanning electron microscope (SEM). The SEM can be used to image these extremely small structures, in effect, take "photos" of these structures. The working principle of the SEM is similar to that of a camera. A camera takes photos by receiving and recording the brightness and color of light reflected or emitted from a person or object. The SEM takes "photos" by receiving and recording the energy of electrons reflected or emitted from the structure. Before taking this "photo", an electron beam can be provided to the structure, and when the electrons are reflected or emitted ("leave") from the structure, the detector of the SEM can receive and record the energy of these electrons to generate an image. This image can be used to determine whether the structure is formed correctly and whether it is formed in the correct location. If the structure is defective, the process can be adjusted so that the defect is less likely to occur again.

[0033] However, errors in structures indicated by SEM images may be "real" or may be "false." For example, when the SEM images the structure, distortion may be introduced, making the structure appear deformed or misplaced, when in reality, there were no errors in the formation or placement of the structure. Distortion may be caused by charge accumulation and changes on the structure of the wafer after interaction with the electrons introduced during the scanning phase. Due to the change in energy, the SEM may no longer generate an image that truly represents the structure.

[0034] The present disclosure describes, among other things, methods and systems for reducing SEM-induced charging artifacts. In one example, a SEM image can be adjusted to compensate for the changed energy of electrons received by a detector of the SEM. That is, the energy of the received electrons is not physically corrected, but the characteristics of the generated image (e.g., pixel values) are corrected to reflect the changed energy. Using the compensation techniques disclosed herein, a SEM image can be compensated to provide a more realistic representation of a structure.

[0035] For the sake of 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 denote the same or similar components or entities, and only the differences with respect to the various embodiments are described.

[0036] As used herein, unless otherwise specifically stated, the term "or" encompasses all possible combinations unless not feasible. For example, if a component is described as including 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 a component is described as including 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.

[0037] Figure 1 An exemplary electron beam inspection (EBI) system 100 is illustrated according to some embodiments of the present disclosure. The EBI system 100 may be used for imaging. Figure 1As shown, the EBI system 100 includes a main chamber 101, a load / lock chamber 102, an electron beam tool 104, and an equipment front end module (EFEM) 106. The electron beam tool 104 is located in the main chamber 101. The EFEM 106 includes a first loading port 106a and a second loading port 106b. The EFEM 106 may include additional (multiple) loading ports. The first loading port 106a and the second loading port 106b receive front opening unified pods (FOUPs) containing wafers (semiconductor wafers or wafers made of (multiple) other materials) or samples to be inspected (wafers and samples can be used interchangeably). A "lot" is a plurality of wafers that can be loaded for processing as a batch.

[0038] 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 the 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 electron beam tool 104. The electron beam tool 104 can be a single beam system or a multi-beam system.

[0039] The controller 109 is electrically connected to the electron beam tool 104. The controller 109 may be a computer configured to perform various controls of the EBI system 100. Figure 1 106, but it is understood that the controller 109 may be part of the structure.

[0040] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or special electronic device capable of manipulating or processing information. For example, the processor may include any number of 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 logic 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 comprising one or more processors distributed across multiple machines or devices coupled via a network.

[0041] In some embodiments, the controller 109 may also include one or more memories (not shown). The memory may be a general or special electronic device capable of storing code and data accessible by a processor (e.g., via a bus). For example, the memory may include any 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 may include an operating system (OS) and one or more applications (or "apps") for a specific task. The memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices coupled via a network.

[0042] Figure 2 An exemplary imaging system 200 is illustrated in accordance with some embodiments of the present disclosure. Figure 2 The electron beam tool 104 can be configured for use in the EBI system 100. The electron beam tool 104 can be a single beam device or a multi-beam device. Figure 2As shown, the electron beam tool 104 includes a motorized sample stage 201, and a wafer holder 202 supported by the motorized sample stage 201 to hold a wafer 203 to be inspected. The electron beam tool 104 also includes an objective lens assembly 204, an electron detector 206 (which includes electron sensor surfaces 206a and 206b), an objective lens aperture 208, a focusing lens 210, a beam limiting aperture 212, a gun aperture 214, an anode 216, and a cathode 218. In some embodiments, the objective lens assembly 204 may include a modified swing objective retarding immersion lens (SORIL) including a pole piece 204a, a control electrode 204b, a deflector 204c, and an excitation coil 204d. The electron beam tool 104 may additionally include an energy dispersive X-ray spectrometer (EDS) detector (not shown) to characterize the material on the wafer 203.

[0043] A primary electron beam 220 is emitted from the cathode 218 by applying an accelerating voltage between the anode 216 and the cathode 218. The primary electron beam 220 passes through the gun aperture 214 and the beam limiting aperture 212, which can determine the size of the electron beam entering the focusing lens 210, which is located below the beam limiting aperture 212. The focusing lens 210 focuses the primary electron beam 220 before the electron beam enters the objective lens aperture 208 to set the size of the electron beam before entering the objective lens assembly 204. The deflector 204c deflects the primary electron beam 220 to facilitate beam scanning on the wafer. For example, during the scanning process, the deflector 204c can be controlled to sequentially deflect the primary electron beam 220 to different locations on the top surface of the wafer 203 at different points in time to provide data for image reconstruction of different portions of the wafer 203. In addition, the deflector 204c can also be controlled to deflect the primary electron beam 220 to different sides of the wafer 203 at a specific location and at different time points to provide data for stereoscopic image reconstruction of the wafer structure at that location. In addition, in some embodiments, the anode 216 and the cathode 218 can generate multiple primary electron beams 220, and the electron beam tool 104 can include multiple deflectors 204c to project multiple primary electron beams 220 to different parts / sides of the wafer at the same time to provide data for image reconstruction of different parts of the wafer 203.

[0044] The excitation coil 204d and the pole piece 204a generate a magnetic field that starts at one end of the pole piece 204a and ends at the other end of the pole piece 204a. The portion of the wafer 203 scanned by the primary electron beam 220 can be immersed in the magnetic field and can be charged, which in turn generates an electric field. The electric field reduces the impact energy of the primary electron beam 220 near the surface of the wafer 203 before it collides with the wafer 203. The control electrode 204b, which is electrically isolated from the pole piece 204a, controls the electric field on the wafer 203 to prevent micro-arching of the wafer 203 and ensure proper beam focusing.

[0045] Once the primary electron beam 220 is received, a secondary electron beam 222 may be emitted from the portion of the wafer 203. The secondary electron beam 222 may form a beam spot on the sensor surfaces 206a and 206b of the electron detector 206. The electron detector 206 may generate a signal (e.g., voltage, current, etc.) representing the intensity of the beam spot and provide the signal to the image processing system 250. The intensity of the secondary electron beam 222 and the resulting beam spot may vary depending on the external or internal structure of the wafer 203. In addition, as described above, the primary electron beam 220 may be projected onto different locations of the top surface of the wafer or onto different sides of the wafer at a specific location to generate secondary electron beams 222 (and resulting beam spots) of different intensities. Therefore, by mapping the intensity of the beam spot with the location of the wafer 203, the processing system may reconstruct an image reflecting the internal or surface structure of the wafer 203.

[0046] As described above, the imaging system 200 can be used to inspect the wafer 203 on the sample stage 201 and includes the electron beam tool 104. The imaging system 200 can also include an image processing system 250, which includes an image acquisition device 260, a storage device 270, and a controller 109. The image acquisition device 260 can include one or more processors. For example, the image acquisition device 260 can include a computer, a server, a mainframe, a terminal, a personal computer, any kind of mobile computing device, etc., or a combination thereof. The image acquisition device 260 can be connected to the detector 206 of the electron 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, radio, or a combination thereof. The image acquisition device 260 can receive a signal from the detector 206 and can construct an image. Therefore, the image acquisition device 260 can acquire an image of the wafer 203. The image acquisition device 260 can also perform various post-processing functions, such as generating a contour, superimposing an indicator on the acquired image, etc. The image acquisition device 260 can perform adjustments to the brightness and contrast of the acquired image, etc. The storage device 270 may be a storage medium such as a hard disk, cloud storage, random access memory (RAM), other types of computer readable memory, etc. The storage device 270 may be coupled to the image acquirer 260 and may be used to save the scanned raw image data as an initial image and save a post-processed image. The image acquirer 260 and the storage device 270 may be connected to the controller 109. In some embodiments, the image acquirer 260, the storage device 270, and the controller 109 may be integrated together as a control unit.

[0047] In some embodiments, the image acquirer 260 can acquire one or more images of the sample based on the imaging signal received from the detector 206. The imaging signal can correspond to a scanning operation for performing charged particle imaging. The acquired image can be a single image including multiple imaging areas. The single image can be stored in the storage device 270. The single image can be an initial image that can be divided into multiple areas. Each of the areas can include an imaging area containing a feature of the wafer 203.

[0048] In some embodiments, the SEM image may be a single SEM image generated by a single scan of the primary electron beam 220 on the wafer 203 along a single scan direction. In some embodiments, the SEM image may be a first average SEM image generated by averaging a plurality of SEM images, each SEM image being generated by a single scan of the primary electron beam 220 on the wafer 203 along the same scan direction. In some embodiments, the SEM image may be a second average SEM image generated by averaging a plurality of SEM images, each SEM image being generated by a single scan of the primary electron beam 220 on the wafer 203 along a different scan direction. The embodiments of the present disclosure are not limited to any particular SEM image generated by any particular method, and the disclosed methods and systems may enhance SEM images including but not limited to the examples herein.

[0049] A challenge in defect detection is artifacts introduced by inspection tools (e.g., SEM). Artifacts do not originate from actual defects in the final product. They may distort or deteriorate the quality of the image to be inspected and cause difficulty or inaccuracy in defect detection. For example, when using SEM to inspect electrical insulating materials, the quality of the SEM image is often affected by charging artifacts induced by the SEM.

[0050] Figure 3 is a diagram of an exemplary process of SEM-induced charging effects according to some embodiments of the present disclosure. The SEM generates a primary electron beam (e.g., Figure 2 The primary electron beam 220 in Figure 3 , electrons of the primary electron beam 302 are projected onto the surface of an insulator sample 304. The insulator sample 304 may be composed of an insulating material (such as a non-conductive resist, a silicon dioxide layer, etc.). The electrons of the primary electron beam 302 may penetrate a certain depth of the surface of the insulator sample 304 and interact with particles of the insulator sample 304 in an interaction volume 306. Some electrons in the primary electron beam 302 may elastically interact with particles in the interaction volume 306 (e.g., in the form of elastic scattering or collision) and may be reflected or recoiled away from the surface of the insulator sample 304. The elastic interaction preserves the total kinetic energy of the interacting bodies (e.g., the electrons of the primary electron beam 302 and the particles of the insulator sample 304), wherein the kinetic energy of the interacting bodies is not converted into other forms of energy (e.g., heat, electromagnetic energy, etc.). Such reflected electrons generated from the elastic interaction may be referred to as backscattered electrons (BSE), such as Figure 3BSE 308 in the primary electron beam 302. Some electrons in the primary electron beam 302 may interact inelastically with particles in the interaction volume 306 (e.g., in the form of inelastic scattering or collisions). Inelastic interactions may not preserve the total kinetic energy of the interacting bodies, where some or all of the kinetic energy of the interacting bodies is converted into other forms of energy. For example, through inelastic interactions, the kinetic energy of some electrons in the primary electron beam 302 may cause electronic excitations and transitions of atoms of the particles. Such inelastic interactions may also generate electrons that leave the surface of the insulator sample 304, which may be referred to as secondary electrons (SEs), such as Figure 3 The yield or emission rate of BSE and SE depends on, for example, the electron energy of the primary electron beam 302 and the material being inspected. The electron energy of the primary electron beam 302 can be determined in part by its acceleration voltage (e.g., Figure 2 The number of BSEs and SEs may be more or less (or even the same) than the injected electrons of the primary electron beam 302. The imbalance of incoming and outgoing electrons may cause charge (e.g., positive or negative charge) to accumulate on the surface of the insulator sample 304. Because the insulator sample 304 is non-conductive and cannot be grounded, the extra charge may accumulate locally on or near the surface of the insulator sample 304, which may be referred to as a SEM-induced charging effect.

[0051] Typically, insulating materials (e.g., many types of resists) can be positively charged because the outgoing electrons (e.g., BSE or SE) typically exceed the incoming electrons of the SEM's primary electron beam and accumulate additional positive charge on or near the surface of the insulator material. Figure 3 The SEM-induced charging effect is shown to occur and cause positive charges to accumulate on the surface of the insulator sample 304. The positive charges can be physically modeled as holes 312. Figure 3 306, electrons of the primary electron beam 302 injected into the interaction volume 306 may diffuse to neighboring volumes of the interaction volume 306, which may be referred to as diffuse electrons, such as diffuse charges 314. The diffuse electrons may recombine with positive charges (e.g., holes) in the insulator sample 304 (such as recombination pairs 316). The diffusion and recombination of charges may affect the distribution of holes 312. Holes 312 may cause problems by, for example, attracting BSEs and SEs back to the surface of the insulator sample 304, increasing the landing energy of the electrons of the primary electron beam 302, causing the electrons of the primary electron beam 302 to deviate from their intended landing point, or interfering with the surface of the insulator sample 304 and electron detectors of the BSEs and SEs (such as Figure 2 The electric field between the electron detector 206).

[0052] The SEM-induced charging effect may attenuate and distort the SEM signal received by the electron detector, which may further distort the generated SEM image. Moreover, because the insulator sample 304 is non-conductive, positive charge may accumulate along the path of the primary electron beam 302 as the primary electron beam 302 scans across its surface. This accumulation of positive charge may increase or complicate the distortion of the generated SEM image. Such distortion caused by the SEM-induced charging effect may be referred to as SEM-induced charging artifacts. SEM-induced charging artifacts may introduce errors in estimating the geometric dimensions of the fabricated structure or cause misidentification of defects during inspection.

[0053] In one aspect of some embodiments of the present disclosure, a modeling technique for simulating SEM-induced charging effects is disclosed. The model is based on the physical theory of charge diffusion and can analytically or phenomenologically describe the charge diffusion (e.g., positive charge diffusion) in a sample generated by electrons injected from a primary electron beam. In addition, it is assumed that the SEM image and the compensated SEM image sought are related by a compensation relationship. The compensation relationship is developed based on physical theories such as electron-hole recombination or Coulomb interaction. On the basis of the diffusion-based model and the compensation relationship, the distorted SEM image can be numerically compensated. In order to reduce computational costs, the diffusion-based model and the compensation relationship use a parameterization method to introduce parameters for simulating the main physical effects of charging without tedious calculations of minor details or time-consuming simulations (e.g., Monte Carlo simulations). In order to optimize the compensation results, the parameters and compensation relationships used in the diffusion-based model are tunable and can be calibrated to theoretical calculations, experimental benchmarks, or practical key performance indicators (KPIs). Because this method uses an analytical description based on physical theory, it can significantly reduce computational costs. It can also provide a simple and intuitive explanation of the image processing process, offer high throughput for compensating SEM images with charging artifacts, and enhance the confidence of the compensation results.

[0054] The surface of the insulating material sample may include various features, such as lines, grooves, corners, edges, holes, etc. These features may be at different heights. When the primary electron beam scans across a feature with a height change (especially a sudden height change), SEs may be generated and collected from the surface and additionally from the edge of the feature or even a hidden surface (e.g., the sidewall of the edge). These additional SEs may cause brighter edges or contours in the SEM image. This effect may be referred to as "edge enhancement" or "edge bloom". The SEM-induced charging effect may also be exacerbated due to the escape of additional SEs (i.e., leaving additional positive charges on the sample surface). The exacerbated charging effect may cause different charging artifacts in the edge bloom region of the SEM image, depending on whether the height of the surface scanned by the primary electron beam is increased or decreased. Therefore, in some implementations, determining the edge (or edge bloom) in the SEM image may be performed as a step in the compensation process.

[0055] Additionally, in some embodiments, when the inspection tool performs a one-dimensional SEM scan, the inspection tool can compensate for images having line-space features such as Figure 5A and Figure 5B ) such that the SEM signal amplitude (e.g., represented by a waveform) can be compared to line-space features (such as grooves or lines shown in FIG. Fig. 5F In some embodiments, when the inspection tool performs a two-dimensional SEM scan (e.g., in a raster manner), the inspection tool can compensate for the presence of two-dimensional curved features (such as Fig. 6A The SEM image of the bending shown in FIG. 1 ) allows correction of charging-induced asymmetries in image brightness (such as Figure 6G ), and the SEM signal amplitudes of different parts of the bend can become more uniform or symmetrical (such as Fig.6I ).

[0056] Figure 4 is a schematic diagram of an exemplary image enhancement system 400 according to some embodiments of the present disclosure. In some embodiments, the image enhancement system 400 may be Figure 2408. In some embodiments, the image processing system 250 may be part of the image enhancement system 400. The image enhancement system 400 may include an image generator 402, a pattern extractor 404, an image enhancer 406, and a storage module 408. In some embodiments, the image enhancement system 400 may also include an output device 410. Each of the image generator 402, the pattern extractor 404, and the image enhancer 406 may include a hardware module, a software module, or a combination thereof. For example, a hardware module may include an input / output (I / O) interface for receiving and sending data. For another example, a software module may include a computer-readable program code or instruction for implementing an algorithm.

[0057] In some embodiments, the image generator 402 may be Figure 2 200. The image generator 402 can acquire an inspection image of a surface of a sample, such as a wafer 203. In some embodiments, the wafer 203 can be a semiconductor wafer substrate, a semiconductor wafer substrate with one or more epitaxial layers or processing films, or a semiconductor wafer substrate with a coated resist layer. The inspection image can include features of the sample, an area of ​​interest on the sample, or the entire sample. In some embodiments, the inspection image can be, for example, an SEM image acquired using a SEM.

[0058] In some embodiments, the pattern extractor 404 may be implemented as a signal processing algorithm, an image processing algorithm, etc. The pattern extractor 404 may be integrated into the image acquirer 260. In some embodiments, the pattern extractor 404 may operate as a separate independent unit configured to process the inspection image. In some embodiments, the pattern extractor 404 may include an image processing unit (not shown) configured to adjust the brightness, contrast, saturation, flatness, noise filtering, etc. of the inspection image before storage. In some embodiments, the pattern extractor 404 may extract pattern information from a pre-stored inspection image.

[0059] In some embodiments, the pattern extractor 404 may include a feature extraction algorithm to extract relevant pattern information from the inspection image. The extracted relevant pattern information may include global information (e.g., global structural features, global patterns, reference datums, etc.) or local information (e.g., local structural features or local patterns, etc.). The extracted relevant pattern information may be stored in the storage module 408, which may be configured to be accessed by other components of the image enhancement system 400.

[0060] In some embodiments, the image enhancement system 400 may include an image enhancer 406. The image enhancer 406 may generate an enhanced inspection image based on the inspection image generated by the image generator 402 or the pattern or feature information extracted by the pattern extractor 404. By applying a diffusion-based model and compensation relationship to the inspection image and the pattern or feature information, the image enhancer 406 may output an enhanced inspection image. In some embodiments, the image enhancer 406 may be implemented as a software module, an algorithm, etc.

[0061] In some embodiments, the image enhancement system 400 may include a storage module 408. In some embodiments, the storage module 408 may be Figure 2 408 may be part of the storage device 270 of the imaging system 200 in the embodiment of the present invention. In some embodiments, the storage module 408 may be an integrated storage medium of the image enhancement system 400, configured to be connected to each of its components. The storage module 408 may be a remote storage module accessible via wired or wireless communication over the Internet, a cloud platform, or a suitable Wi-Fi communication path, etc.

[0062] The storage module 408 may store inspection images from the image generator 402, extracted patterns or feature information from the pattern extractor 404, enhanced inspection images from the image enhancer 406, etc. The storage module 408 may also share the stored information with other components (not shown) of the image enhancement system 400.

[0063] In some embodiments, the image enhancement system 400 may optionally further include an output device 410. In some embodiments, the output device 410 may be integrally connected to the EBI system 100, integrated within the electron beam tool 104, or as a separate output device located at a remote location. For example, the output device 410 may be remotely located and operated via a wired or wireless communication network (e.g., Wi-Fi, the Internet, or a cloud network) or other suitable communication network or platform. In some embodiments, the output device 410 may be a handheld output device, a wearable output device, a multi-screen display, an interactive output device, or any other suitable output device.

[0064] The output device 410 can display the inspection image from the image generator 402, the pattern or feature information from the pattern extractor 404, the enhanced image from the image intensifier 406, etc. In some embodiments, the output device 410 can also be connected to the storage module 408 to display the stored information of the sample or the region of interest on the wafer. In some embodiments, the output device 410 can display the pre-processed and post-processed images of the wafer, the region of interest, or the features on the wafer. The output device 410 can also be used to display the real-time pre-processed SEM image.

[0065] Figure 5A is an illustration of a pattern of a first exemplary 2D SEM image superimposed with a 1D SEM signal waveform according to some embodiments of the present disclosure. For example, Figure 5A The pattern in may be a slit, from which a 1D waveform can be obtained by scanning a line from left to right in the middle of the slit. Figure 5A , the primary electron beam scans from left to right. The SEM signal waveform may represent the SEM signal amplitude or intensity generated when the primary electron beam scans across two linear features (e.g., contours) of the pattern, where the height of the overlapping waveforms represents the amplitude. The two linear features in the SEM image may represent a first edge 502 and a second edge 504 of the pattern. Due to their symmetrical structure, the first edge 502 and the second edge 504 should generate a substantially symmetrical SEM signal. However, as Figure 5A As shown, the SEM waveform amplitudes at the first edge 502 and the second edge 504 are different (i.e., "asymmetric"). When the primary electron beam scans from outside the slit to inside the slit (i.e., the height of the sample surface decreases), the SEM signal amplitude generated is smaller than the SEM signal amplitude generated when the primary electron beam scans from inside the slit to outside the slit (i.e., the height of the sample surface increases). This SEM signal asymmetry can be attributed to the SEM-induced charging effect, and Figure 5A Example one-dimensional charging artifacts are shown (eg, along the scan direction of the primary electron beam).

[0066] Figure 5B is an illustration of a second exemplary SEM image with SEM-induced artifacts according to some embodiments of the present disclosure. Figure 5B , the first edge 506 and the second edge 508 of the slit also show asymmetric brightness characteristics due to the SEM-induced charging effect: at the first edge 506, the outside of the slit is brighter than the inside of the slit, however, at the second edge 508, the inside of the slit is brighter than the outside of the slit. For the tip-to-line CD measurement in the second exemplary SEM image, the charging artifacts may cause measurement uncertainties on the order of 2 nanometers in some cases.

[0067] FIG. 5C to FIG. 5D A diagram showing an example distribution of diffuse charge density generated by a primary electron beam scanned across a linear feature according to some embodiments of the present disclosure. In some embodiments, the diffuse charge can be a positive charge. Figure 5C , a primary electron beam 510 (shown as a beam spot in the top view) is scanned from left to right across a linear feature 512 (eg, a line-space pattern). Figure 5DThe corresponding charge density distribution at a particular moment in time within the linear feature 512 is shown. For example, if the primary electron beam 510 causes a charging effect that accumulates positive charges, the charge density distribution may be a hole density distribution on the surface of the linear feature 512. In FIG5d , white represents the instantaneous density of the charge. The lighter the color, the higher the density. Figure 5D In FIG. 5 , the location below the beam spot has the highest charge density, and as the primary electron beam 510 scans across the linear feature 512, the charge diffuses and expands from the scan path of the primary electron beam 510. Figure 5D As shown, the primary electron beam 510 may induce a charging effect in front of the current irradiation position. However, the portion of the linear feature 512 behind the moving beam spot has a greater charge spread than the portion of the linear feature 512 before the beam spot.

[0068] Figure 5E is an illustration of an SEM waveform of two line-space patterns showing SEM-induced artifacts without compensation according to some embodiments of the present disclosure. For example, a line-space pattern similar to Figure 5A and Figure 5B The linear features of generate two line-space patterns (shown as a solid curve and a dashed curve, respectively). The two line-space patterns have different CDs (e.g., line widths), which are represented by the distance between the two peaks of each curve. Figure 5E and Fig. 5F In , the two CDs are 26 nm and 68 nm, respectively. Without compensation, the SEM signals are asymmetric (ie, their peaks have different heights), showing charging artifacts.

[0069] Fig. 5F According to some embodiments of the present disclosure Figure 5E 4. Illustration of SEM waveforms of two line-space patterns in FIG. 4 after charge compensation. After charge compensation (or "discharging") performed by image intensifier 406, the SEM waveforms of the two line-space patterns show more substantial symmetry, showing a significant reduction in charging artifacts.

[0070] Fig. 6A is an illustration of a third exemplary SEM image of a pattern having curved features according to some embodiments of the present disclosure. For example, the pattern may be a hole. Fig. 6A In FIG. 6 , the primary electron beam is scanned from left to right. At regions 602 to 608, the SEM should generate symmetrical SEM signals due to its symmetrical structure with respect to the center of the hole. However, regions 602 to 608 show an asymmetry in the brightness inside and outside the hole. This SEM signal asymmetry can be attributed to SEM-induced charging, and Fig. 6A Example two-dimensional charging artifacts are shown.

[0071] Figure 6B The invention describes some embodiments according to the present disclosure. Fig. 6A As previously mentioned, in some embodiments, contour marking can be performed to compensate for changes in charging effects due to height changes at the edge of the sample surface. Figure 6B In FIG. 6 , an inner contour (represented by a square dot 610) may be identified around a dark region. The dark region may have a fuzzy border. The accuracy of the inner contour identification may be allowed to reach a certain level without significantly affecting the uncertainty of the result of the charging artifact compensation of the SEM image. The details of the inner contour identification will be shown in FIG. Figures 7 to 9 Described in.

[0072] The identified contours indicate the location of edge bloom (i.e., areas with extra positive charge). Edge bloom can indicate areas with exacerbated SEM-induced charging artifacts. In some embodiments, an extrapolation method can be used to identify edge bloom areas, where the edge bloom area can be determined as the area enclosed by the inner contour and the outer contour, and the outer contour is extrapolated outward from the inner contour by a predetermined distance. In some embodiments, the predetermined distance can be a few nanometers (e.g., 8, 10, 12 nanometers, or any distance in the order of several nanometers or tens of nanometers). The outer contour is Figure 6B 612. It should be noted that other methods may also be used to determine the edge bloom region, such as automatic methods, and the disclosed techniques are not limited to extrapolation methods whereby both inner and outer contours may be detected by thresholding the intensity of the SEM signal.

[0073] Figure 6C The invention is a diagram illustrating some embodiments of the present disclosure based on Figure 6B The height mask is a map that distinguishes the areas inside and outside the pores in the SEM image at the height of a horizontal slice plane (called a "horizontal cross section") parallel to the sample surface. For example, for curved features such as Fig. 6A As shown, assuming that the bottom (i.e., the lowest point) of the curved feature is at height 0 and the vertex of the curved feature is at the maximum height, one or more (e.g., 1, 2, 3, 4 or any positive integer) horizontal cross sections can be inserted between height 0 and the maximum height to determine the mapping. Because the vertical profile of the curved feature has variations in three-dimensional space (e.g., the inner wall of a hole is not upright but inclined), horizontal cross sections at different heights can result in different mappings that distinguish the "inside" and "outside" of the hole.

[0074] In some embodiments, the height mask can be binarized. For example, pixels defined by the mapping as being inside a hole can be assigned a first weight (e.g., 0), while pixels defined as being outside a hole or on the edge of a hole can be assigned a second weight (e.g., 1), or vice versa. Pixels with the first weight can be shown as black (such as by output device 410), and pixels with the second weight can be shown as white (such as by output device 410), or vice versa. For example, Figure 6C The map at maximum height is shown, as indicated by pixels between the inner and outer contours being white, and pixels outside the outer contour. In some embodiments, the height mask may be ternary, quaternary, or have any number of levels of weight values ​​assigned to the map.

[0075] The reason for determining the height mask is that the charging of the SEM image may appear different when the area inside (or outside) the hole is different. In order to simulate this different charging, the parameters used in the diffusion-based model or the compensation relationship as described above can be assigned different ranges because the area inside (or outside) the hole is different. Because the vertical profile of the hole is not actually ideal, it may be challenging to determine a clear boundary between the "inside" and "outside" of the hole. In other words, such a boundary depends on at which height the horizontal cross section is located (for example, at which height the height mask is determined). As a result, the result of the charging artifact compensation may depend on where the boundary is located. In some embodiments, only one height mask is used in the compensation calculation. In some embodiments, multiple height masks can be used, where the compensation calculation can be repeated for each height mask in the height mask (for example, different parameter ranges are set for each height mask in the height mask). The more mask heights are used, the more likely it is to find an enhanced SEM image that is closer to the optimal. However, using more height masks may also increase the computational cost. The number of height masks to be used may depend on the balance between available computing resources, computational cost, and accuracy requirements for the enhanced SEM images, and is not limited to the present disclosure.

[0076] Fig.6D The invention is a diagram illustrating some embodiments of the present disclosure based on Figure 6B . The charge density mask is a map indicating areas with increased positive charge generation. Areas with increased positive charge generation are areas that are affected by SEM-induced charging effects (e.g., areas where the SEM signal will be attenuated) and can be primary areas for charging artifact compensation.

[0077] The charge density mask can be used to generate charge diffusion that would otherwise be obtained through a model based on two-dimensional diffusion. In some embodiments, the charge density mask can indicate an area with edge bloom. In some embodiments, the charge density mask can have different forms for the inside and outside of the hole. For example, the charge density mask for the area inside the hole can be determined in a non-analytical form (e.g., a uniform form) (e.g., described by a height mask), and the charge density mask for the area outside the hole can be determined in an analytical form. In some embodiments, the charge density mask can be binary. For example, pixels in the edge bloom area can be assigned a first weight (e.g., 1), and pixels outside the edge bloom area and on the edge of the edge bloom area can be assigned a second weight (e.g., 0), or vice versa. Pixels with the first weight can be displayed as white (such as by the output device 410), and pixels with the second weight can be displayed as black (such as by the output device 410), or vice versa. For example, Fig.6D The edge flooding area representing the outside of the hole is shown (e.g., Figure 6B In some embodiments, the charge density mask may be ternary, quaternary, or have any number of levels of weighted values ​​assigned to the pixel.

[0078] Fig. 6E and Fig. 6F The invention describes some embodiments according to the present disclosure. Fig.6D The charge density masks are as follows: Figure 6C Illustration of directional blur at different heights indicated. Because the scanning of the primary electron beam is directional, areas scanned earlier may have residual charging effects that interfere with areas scanned later (e.g., by accumulating charge), and this interference is directional. The charge density mask may be directionally blurred to simulate such interference. In some embodiments, directional blurring may be achieved by convolving the charge density mask with a directional attenuation or reduction function such as a Gaussian function or a Lorentzian function. For example, a directional Gaussian function or a Lorentzian function may be non-zero only for predetermined values ​​of the function's independent variable. Details of the directional Gaussian function or the Lorentzian function will be described in detail in the accompanying drawings. Figures 7 to 9 For example, Fig. 6E Shows the blurred Fig.6D The charge density mask of Figure 6C Charge density mask outside the hole or at higher altitudes as indicated). Fig. 6F Shows the blurred Figure 6C Charge density mask inside the hole or at lower height as indicated. FIG. 6E to FIG. 6F The white areas in show areas with charging effects, and the brightness of the white indicates the intensity of the charging effect.

[0079] Figure 6G is an illustration of a third exemplary SEM image pattern after charging artifact compensation according to some embodiments of the present disclosure. Figures 7 to 9 Compensation is performed using the diffusion-based model and compensation relationships described in . Figure 6G and Fig. 6A In comparison, it can be seen that charging artifacts are significantly reduced or eliminated, such as in regions 622 to 628 .

[0080] Figure 6H is an illustration of an SEM waveform overlaid on top of a pattern of a third exemplary SEM image without compensation, according to some embodiments of the present disclosure. Figure 6H The SEM signal amplitude of the overlapped area of ​​the third exemplary SEM image is shown. Figure 6H As shown, the SEM waveform is asymmetric over regions with symmetric features. For example, SEM signal peaks 630 and 632 are asymmetric (i.e., have different amplitudes), while the regions corresponding to SEM signal peaks 630 and 632 are symmetric relative to the center of the pattern. Also, the baseline between SEM signal peaks 630 and 632 is not flat. Those show SEM-induced charging artifacts.

[0081] Fig.6I is an illustration of an SEM waveform overlaid on top of a pattern of a third exemplary SEM image with compensation according to some embodiments of the present disclosure. Fig.6I 4 shows the SEM signal amplitude of the overlapped region of the third exemplary SEM image. After charge compensation (or "discharging") performed by the image intensifier 406, as shown in FIG. Figure 6H The illustrated SEM waveform becomes substantially symmetrical, showing a significant reduction in charging artifacts. For example, compared to SEM signal peaks 630 and 632, SEM signal peaks 634 and 636 become substantially symmetrical after compensation is performed.

[0082] Figures 7 to 9 Exemplary methods 700 to 900 are shown as embodiments of the present disclosure. Methods 700 to 900 may be performed by an image enhancement module that may be coupled to a charged particle beam device (e.g., EBI system 100). For example, a controller (e.g., Figure 2 The controller 109 of the embodiment of the present invention may include an image enhancement module and may be programmed to implement methods 700 to 900. It should be understood that the charged particle beam device may be controlled to image a wafer or a region of interest on the wafer. Imaging may include scanning the wafer to image a portion of the wafer, which may include scanning the wafer to image the entire wafer. In some embodiments, methods 700 to 900 may be performed by an image intensifier module (e.g., Figure 4 The image enhancement system 400 may be used to perform the image enhancement.

[0083] Figure 7 is a flow chart illustrating an exemplary image enhancement method 700 according to some embodiments of the present disclosure.

[0084] At step 702, the controller acquires a scanning electron microscope (SEM) image. In some embodiments, the controller may acquire the SEM image by scanning the surface of a sample (e.g., insulator sample 304). For example, the controller may receive and process a sample image acquired by an electron detector (e.g., Figure 2 The controller may collect SEM signals from an electronic detector 206 of the controller to generate a SEM image. In some embodiments, the controller may obtain the SEM image by receiving the SEM image from a database. For example, the database may store one or more SEM images generated by the electronic detector in a previous operation. In some embodiments, the database may also store one or more SEM images generated by one or more electronic detectors that are not coupled to the controller (such as an electronic detector coupled to another EBI system).

[0085] In some embodiments, the SEM image may include a pattern of structures of the surface of a sample, such as insulator sample 304. The pattern may include SEM-induced charging artifacts, which may be a pattern distorted due to SEM-induced charging effects.

[0086] In step 704, the controller simulates diffused charges associated with locations of the SEM image. In some embodiments, the controller may simulate diffused charges associated with multiple locations of the SEM image. In some embodiments, the controller may prioritize to simulate diffused charges for areas of the SEM image that include a pattern, because SEM-induced charging artifacts tend to occur at those areas. In some embodiments, the locations of the SEM image associated with the simulated diffused charges may be locations of the pattern. For example, after acquiring the SEM image in step 702, the controller may extract the pattern from the SEM image, such as by using image processing techniques for pattern recognition. In some embodiments, the pattern may include contours, such as contours of features on the surface of the sample. In some embodiments, the pattern may be associated with height changes on the surface of the sample. For example, the pattern may be Figure 5A A pattern including a first edge 502 and a second edge 504, or Figure 5B 5 includes a pattern of first edges 506 and second edges 508. The edges 502 to 508 are associated with height changes on the sample surface, such as the edges of grooves, trenches, or holes.

[0087] In some embodiments, in order to extract a pattern from a SEM image, the controller may determine the pattern as a pixel of the SEM image, and the value of the pixel may satisfy a threshold condition. For example, the threshold condition may be that the brightness value of the pixel exceeds a predetermined value. For another example, the threshold condition may be that the brightness value of the pixel is lower than a predetermined value. For another example, the threshold condition may be that the brightness value of the pixel is within a predetermined range. In some embodiments, the threshold condition may be that the pixel is within an edge flooding region. The controller may determine the edge flooding region using SEM image processing techniques or other information, such as design data (e.g., a GDS or GDSII file) of the characteristics of the sample.

[0088] Refer to Figure 7 At step 706, the controller uses the SEM image and the diffused charge to provide an enhanced SEM image. In some embodiments, at step 704, the controller may use different techniques to simulate the diffused charge depending on whether the SEM scan is one-dimensional or two-dimensional. For example, FIG. 5A to FIG. 5B As shown, a one-dimensional SEM scan can scan a feature once in a certain direction, such as for one-dimensional CD inspection. For another example, a two-dimensional SEM scan can scan a sample in a raster manner (e.g., line by line) or in an individual row scanning manner (e.g., scanning each row independently), such as for two-dimensional CD inspection, such as FIG. 6A to FIG. 6I as shown in .

[0089] When the SEM scan is one-dimensional, in some embodiments, the controller can simulate the diffusion charge associated with the location of the SEM image as follows. In some embodiments, the controller can determine a model for simulating the diffusion charge. The model can be based on the movement of the diffusion charge caused by the charging effect induced by the SEM at the location. For example, if the location is at the scanning path of the electron beam (e.g., the primary electron beam) of the SEM, the controller can determine the diffusion charge based on the diffusion charge distribution and the period of the electron beam irradiating the location. In some embodiments, the diffusion charge distribution can be associated with a diffusion coefficient. In some embodiments, the diffusion charge distribution can be expressed as:

[0090] uD(x, y, t) equation (1)

[0091] In equation (1), D is a diffusion coefficient (e.g., a constant), x and y represent the positions of the scanning path of the electron beam, and t is time. Equation (1) can describe a one-dimensional diffusion process for different positions of the scanning path at different times. For example, Figure 5CAs shown, the scanning path of the primary electron beam 510 is a horizontal line moving from left to right. Assuming the horizontal line as the x-axis, the horizontal position of the scanning path can be represented by the value of x. Assuming the vertical line perpendicular to the x-axis as the y-axis, the vertical position of the scanning path can be represented by the value of y. Assuming that time 0 is when the primary electron beam 510 irradiates the left edge of the linear feature 512, the value of t in equation (1) can be counted from time 0.

[0092] The area of ​​the cross section (or "profile") of the electron beam may be referred to as the "spot size". When the electron beam is irradiated at a certain location, an area having the same area as the spot size may be irradiated. Because the electron beam is not binary focused on the surface of the sample, the electrons may escape the profile of the electron beam and irradiate surrounding areas outside the spot size. In other words, the profile of the electron beam does not have a binary boundary (e.g., a circle), but rather has an attenuation boundary. In some embodiments, such a profile of the electron beam may be described using a two-dimensional Gaussian distribution:

[0093]

[0094] In equation (2), x and y have the same definitions as in equation 1, and σ represents the spot size. In some embodiments, σ ​​can be determined from measurements. In some embodiments, σ ​​can be used as a calibration parameter (eg, with a specified value).

[0095] In some embodiments, based on equations (1) and (2), when the SEM scan is one-dimensional, a diffusion-based model can be determined for simulating the time-independent diffusion charge p(x) at a horizontal position x of the SEM image:

[0096]

[0097] In equation (3), Δ is the pixel size of the SEM image, which represents the unit or granularity of the position of the SEM image. For example, Δ can be the unit length of the horizontal x-axis. n is the number of pixels, and x=nΔ represents the horizontal position in the SEM image. p(x=nΔ) represents the time-independent diffusion charge at the horizontal position x=nΔ. T is the duration of the electron beam irradiating each pixel. In equation (3), Indicates that the electron beam is irradiating at x=nΔ. When the electron beam scans across the surface of the sample and generates a SEM image, from the perspective of image generation, the electron beam irradiates the surface area corresponding to the pixel size Δ of the SEM image for a duration T before proceeding to irradiate the next area corresponding to the next pixel of the SEM image. In other words, the electron beam can scan across the pixel size Δ from time (n-1)T to nT. During irradiation, charge can be deposited in the area and diffuse. Equation (3) provides an example method for simulating this charge diffusion by performing an integral, which represents an electron beam irradiated at x=nΔ from time (n-1)T to nT, and causes charge diffusion according to equation (1). By integrating all possible y values, the integral of equation (3) makes the diffused charge depend only on the horizontal position in the SEM image. In other words, the diffusion model effectively assumes that the diffusion of charge is one-dimensional (e.g., in the x direction).

[0098] In some embodiments, in order to reduce computational cost, equations (1) to (3) can be simplified to one dimension. For example, they can be simplified as follows:

[0099] u D (x, t) Equation (4)

[0100]

[0101]

[0102] In equations (4) to (6), the x direction may be substantially parallel to the direction of the scanning path of the electron beam. For example, the angle between the x direction and the direction of the scanning path may be within a predetermined value (e.g., any value between 0.01 degrees and 0.1 degrees). The predetermined value may vary depending on the actual application and is not limited thereto.

[0103] In some embodiments, to account for more complex use cases or more complex charging effects, equation (1) can be calibrated for edge flooding regions in SEM images. Alternatively, equations (1) to (3) can be extended to three dimensions. The z direction can be the depth direction perpendicular to the sample surface and can be used to indicate the inside or outside of a concave feature (e.g., a hole, a groove, a trench, etc.). For example, equations (1) to (3) can be extended as follows:

[0104] u D (x, y, z, t) Equation (7)

[0105]

[0106]

[0107] If equations (1) to (3) are used to model charge diffusion for a one-dimensional SEM scan, correspondingly, at step 706, the controller can use the SEM image and the diffused charge to provide an enhanced SEM image according to a compensation relationship. In some embodiments, the compensation relationship can be under the assumption that the generated SEM signal is suppressed by charging effects induced in the vicinity of the electron beam (e.g., by electron-hole recombination, Coulomb interaction, or both).

[0108] In some embodiments, to provide an enhanced SEM image, the controller may determine a corrected pixel value for a pixel of the SEM image by shifting the pixel value of the pixel by a first parameter. The controller may also determine a second parameter as a scaling factor of the product of the corrected pixel value and a diffusion charge associated with the pixel. The controller may also determine an enhanced pixel value by adding the corrected pixel value to the product scaled by the second parameter. Using the enhanced pixel value, the controller may provide an enhanced SEM image. For example, the controller may use the example compensation relationship

[0109]

[0110] In equation (10.1), p(x) is the diffusion charge determined from equation (3). n and m are positive real numbers, such as integers, rational numbers, or irrational numbers. For example, n and m can both be 1. SEM c (x) represents the SEM image value (eg, pixel value) generated at horizontal position x, which is affected by the SEM-induced charging effect. nc (x) is the SEM image value sought that is not affected by the SEM-induced charging effect. The compensation relationship of equation (10.1) can be expressed as follows: if a charging effect (represented by p(x)) occurs at position x, then the actual SEM signal (represented by SEM nc (x) represents the magnitude of). In some embodiments, SEM c (x) can be used to generate SEM images with SEM-induced charging artifacts, and equation (10.1) can be used to derive the SEM nc (x), which can further be used to generate enhanced SEM images that compensate (or "discharge") charging artifacts.

[0111] In equation (10.1), a and c are parameters used for regression or fitting. In some embodiments, a and c can be scalars. For example, parameter a can be introduced to account for the scaling process of mapping the SEM signal to digitized (e.g., 8-bit) pixel values ​​(e.g., grayscale values). Parameter c can be introduced to account for the shift of the minimum value of the SEM signal. For example, the minimum value of the SEM signal is usually adjusted to 0, such as FIG. 5E to FIG. 5FAs shown. However, in practice, the minimum value of the detected SEM signal is usually non-zero. In these cases, the parameter c can adjust the value of such minimum value to be greater than 0. It should be noted that c can have a value of 0. In some embodiments, the parameter c may not be used in equation (10.1). In equation (10.1), n ​​and m may also be parameters used for regression or fitting.

[0112] By using equation (10.1), the controller can c (x) shifting the first parameter c to determine a corrected pixel value for the pixel at position x. The corrected pixel value may be SEM c (x)+c. The controller may also determine a second parameter a. The product of the corrected pixel value and the diffusion charge is p(x)·(SEM c (x)+c). The controller can also set the enhanced pixel value SEM nc (x) is determined as

[0113] SEM nc (x) = a·p(x)·*SEM c (x)+c)+(SEM c (x)+c) Equation (11.1)

[0114] For another example, the controller may use another example compensation relationship to provide an enhanced SEM image.

[0115] SEM c (x) = SEM nc (x)-a·p n (x) Equation (10.2)

[0116] In equation (10.2), p(x), SEM c (x) and SEM nc (x) can have the same meaning as in equation (10.1). n is a positive real number, such as an integer, a rational number, or an irrational number. For example, n can be 1. In equation (10.2), a and n can be parameters for regression or fitting. Using equation (10.2), the controller can determine the corrected pixel value for the pixel at position x as the SEM c (x) itself (i.e., the pixel shift is 0). The controller may also determine a first parameter n and a second parameter a. The controller may also set the enhanced pixel value SEM nc (x) is determined as

[0117] SEM nc (x) = SEM c (x)+a·p n (x) Equation (11.2)

[0118] In some embodiments, to determine the enhanced image at step 706, optimal values ​​for the parameters in equation (10.1) or equation (10.2) may be determined. Figure 8 is a flow chart illustrating an exemplary parameter optimization method 800 for image enhancement according to some embodiments of the present disclosure.

[0119] At step 802, the controller determines a set of values ​​for a first parameter and a set of values ​​for a second parameter. For example, by equation (10.1), the first parameter may be c and the second parameter may be a. For another example, by equation (10.2), the first parameter may be n and the second parameter may be a. In some embodiments, the controller may determine a set of values ​​for more than two parameters. For example, by equation (10.1), the controller may determine a set of values ​​for a, c, n, and m.

[0120] In step 804, the controller determines a set of enhanced SEM images. In some embodiments, the controller may determine each enhanced SEM image using a value in a set of values ​​of a first parameter and a value in a set of values ​​of a second parameter. In some embodiments, by equation (10.1), the controller may determine a candidate value from a predetermined range for each of a and c. For example, the controller may select a candidate value between 0.01 and 10 with a step value of 0.01, and may select a candidate value between 0.5 and 1.5 with a step value of 0.1. It should be noted that the range of candidate values ​​for the first parameter and the second parameter may vary and is not limited to the examples provided. In some embodiments, the controller may determine each enhanced SEM image using a unique combination of each value in a set of values ​​of a parameter. For example, by equation (10.1), the controller may determine a candidate enhanced SEM image using a unique combination of values ​​of a, c, n, and m.

[0121] At step 806, for each of the determined enhanced SEM images, the controller determines a waveform of enhanced pixel values ​​along a direction in each enhanced SEM image. In some embodiments, the direction may be a moving direction of the primary electron beam, such as a direction of a scanning path of the primary electron beam, such as Figure 5C In some embodiments, the waveform may include a first peak corresponding to the first pattern and a second peak corresponding to the second pattern, and the first pattern and the second pattern are spatially symmetrical. For example, the first pattern and the second pattern may be Figure 5A The first and second edges in the waveform can be FIG. 5E to FIG. 5F For example, the controller may use the candidate values ​​determined at step 804 and the SEM image values ​​SEM for all x nc(x) is used to determine each enhanced SEM image according to equation (11.1).

[0122] In some embodiments, the controller may also determine a set of values ​​of a third parameter, such as a diffusion coefficient D in p(x), at step 802. For example, at step 804, the controller may determine candidate values ​​of D between 0.1 and 20 with a step value of 0.1. In some embodiments, for each x of the scan path of the electron beam, the controller may determine a set of diffusion charge values ​​by equation (3). Each of the diffusion charge values ​​may be determined using a different value of the diffusion coefficient D. Based on the set of diffusion charge values, at step 806, the controller may determine a set of enhanced SEM images, each enhanced SEM image being determined using one of the set of values ​​of the first parameter, one of the set of values ​​of the second parameter, and one of the set of diffusion charge values.

[0123] Refer to Figure 8 At step 808, the controller provides the enhanced SEM image as one of the set of enhanced SEM images having the smallest asymmetry between the first peak and the second peak. In some embodiments, the controller may determine whether each of the waveforms determined at step 806 is symmetrical. For example, the waveform may be plotted, such as Figure 5E By equation (10.1), among all combinations of all candidate values ​​of a, c, and D, the controller can determine the optimal combination that shows the minimum asymmetry between the first peak and the second peak (e.g., Fig. 5F ). If such an optimal combination is found, the controller can determine the enhanced SEM image as the SEM image with the smallest asymmetry. In some embodiments, if more than one optimal combination of parameter values ​​is found, the controller can also determine the final optimal combination by considering other KPIs examined, such as the confidence level of the CD estimate based on the SEM image.

[0124] When the SEM scan is two-dimensional, in some embodiments, the controller may simulate the diffused charge associated with the location of the SEM image as follows. In some embodiments, the controller may determine a model for simulating the diffused charge associated with the location of the SEM image. The model may be based on the charge density of the diffused charge caused by the SEM-induced charging effect at the location and the weighting of the charge density. For a set of locations of the SEM image, the controller may determine a charge density value associated with the location and a weight value associated with the location. The controller may also determine the diffused charge at a location in the location by performing a convolution of the charge density value at the location and the weight value. In some embodiments, the controller may determine a charge density value and a weight value for each pixel of the SEM image. In some embodiments, the charge density value may be binarized. For example, the charge density value associated with the location may be a charge density mask, such as Fig.6D and its associated paragraphs. The weight value associated with a position may be referred to as a weight function. The weight function is introduced into the two-dimensional diffusion-based model to take into account the asymmetry introduced by the directional SEM scan, which will be described as follows.

[0125] In some embodiments, when the SEM scan is two-dimensional, a two-dimensional diffusion-based model can be determined to simulate the time-independent diffusion charge c(x, y) at the position (x, y) of the SEM image (e.g., as FIG. 6A to FIG. 6I For example, a two-dimensional diffusion-based model can be formulated as the convolution of a charge density mask and a weight function:

[0126]

[0127] In equation (12), c(x, y) represents the time-independent diffusion charge at position (x, y) of the SEM image, which is functionally similar to p(x) in equation (10.1) or (10.2). cdm(x, y) represents the charge density mask, and w(x, y) represents the weighting function. Symbols represents a two-dimensional convolution. For example, a convolution can be

[0128]

[0129] In equation (13), the integration ranges of t and u can be the ranges of x and y in the SEM image, respectively. One physical property of SEM scanning is that the electron beam of the SEM scans across the surface of the sample in a certain direction. Figure 5D As shown, the charge diffusion caused by the electron beam is asymmetric in front of or behind the moving electron beam. To account for this asymmetry in equations (12) and (13), in some embodiments, w(x, y) can be a two-dimensional Gaussian distribution.

[0130]

[0131] In equation (14), x 0 and 0 Can be any coordinate value in the SEM image. In some embodiments, x 0 and 0 Both can be 0. The effect of convolving cdm(x, y) with w(x, y) can be that cdm(x, y) is directionally blurred, such as FIG. 6E to FIG. 6F In some other embodiments, w(x, y) may be a two-dimensional Lorentzian distribution.

[0132]

[0133] In equation (15), n represents a positive integer or a positive fraction. 0 and 0 Can be any coordinate value in the SEM image. In some embodiments, x 0 and 0 Both may be 0. Equations (14) and (15) may achieve similar directional blurring for cdm(x, y). It should be noted that any decay or decreasing function may be used in equations (14) and (15), not limited to the example Gaussian or Lorentzian distributions.

[0134] It should also be noted that in equations (14) and (15), σ x and σ y can be an analog of D in equation (3). In some embodiments, σ x and σ y Can be a parameter for regression or fitting. It should also be noted that equations (14) and (15) describe w(x, y) applicable to horizontal scanning. When the SEM image is obtained from an oblique scan (e.g., the primary electron beam 510 scans across the linear feature 512 at, for example, a certain angle (such as 45°), the xy plane of w(x, y) in equations (14) and (15) can also be rotated accordingly. For example, this rotation can be achieved by performing a Cartesian axis rotation.

[0135] In charge diffusion modeling for two-dimensional SEM scanning, correspondingly, the controller can use the SEM image and the diffused charge to provide an enhanced SEM image through a compensation relationship at step 706. In some embodiments, the compensation relationship can be under the assumption that the generated SEM signal is suppressed by charging effects induced near the electron beam (e.g., by electron-hole recombination, Coulomb interaction, or both).

[0136] In some embodiments, for a set of positions of the SEM image, the controller may further determine a beam profile value of the electron beam at the set of positions when the electron beam is irradiated at the positions. For example, the set of positions may be close to or around the positions irradiated by the electron beam. The controller may also determine the diffuse charge at the position by performing a convolution of the charge density value, the weight value, and the beam profile value at the position. Similar to equation (3), in order to estimate the additional blur introduced by the beam profile, c(x, y) in equation (12) may be further convolved with a beam profile function (such as g(x, y) in equation (2):

[0137]

[0138] In some embodiments, to take into account more complex use cases or more complex charging effects, equations (12) to (16) can be expanded to three dimensions. The z-direction can be the depth direction perpendicular to the sample surface and can be used to indicate the inside or outside of a concave feature (e.g., a hole, a groove, a trench, etc.).

[0139] In some embodiments, to provide an enhanced SEM image, the controller may adjust the pixel value of the SEM image at the position based on one of the aforementioned models to reduce the SEM-induced charging artifacts. In some embodiments, the controller may determine the corrected pixel value of the pixel for the SEM image by shifting the pixel value of the pixel by a first parameter. The controller may also determine the second parameter as a scaling factor of the product of the corrected pixel value and the diffusion charge associated with the pixel. The controller may also determine the enhanced pixel value by adding the corrected pixel value to the product scaled by the second parameter. Using the enhanced pixel value, the controller may provide an enhanced SEM image. For example, the controller may use an example compensation relationship.

[0140]

[0141] In equation (17.1), n ​​and m are positive real numbers, such as integers, rational numbers, or irrational numbers. For example, n and m can both be 1. SE c (x, y) represents the SEM image value (eg, pixel value) generated at position (x, y), which is affected by SEM-induced charging artifacts. nc (x, y) is the SEM image value sought that is not affected by SEM-induced charging artifacts. The compensation relationship of equation (17.1) can be expressed as follows: If a charging effect occurs at position (x, y) (given by ), then the image value of the actual SEM image (expressed by SE nc (x, y) means) can be suppressed. In other words, equation (17.1) can be used to derive SE nc(x, y). In equation (17.1), the parameters a and c function similarly to equation (10.1). In these cases, parameter c can adjust the value of this minimum to be greater than 0. It should be noted that c can have a value of 0. In some embodiments, parameter c may not be used in equation (17.1). In equation (17.1), n ​​and m may also be parameters used for regression or fitting.

[0142] By using equation (17.1), the controller can c (x, y) is shifted by the first parameter c to determine a corrected pixel value for the pixel at position x. The corrected pixel value may be SE c (x, y) + c. The controller may also determine a second parameter a. The product of the corrected pixel value and the diffusion charge is c(x, y)·(SE c (x, y) + c). The controller can also set the enhanced pixel value SE nc (x, y) is determined as

[0143] SE nc (x, y) = a·c(x, y)·(SE c (x,y)+c)+(SE c (x, y) + c) Equation (18.1)

[0144] For another example, the controller may use another example compensation relationship to provide an enhanced SEM image.

[0145] SE c (x) = SE nc (x)-a·c n (x, y) Equation (17.2)

[0146] In equation (17.2), c(x, y), SE c (x) and SE nc (x) may have the same meaning as in equation (17.1). n is a positive real number, such as an integer, a rational number, or an irrational number. For example, n may be 1. In equation (17.2), a and n may be parameters for regression or fitting. Using equation (17.2), the controller may determine the corrected pixel value for the pixel at position x as SE c (x) itself (ie, the pixel shift is 0). The controller may also determine a first parameter n and a second parameter a. The controller may also set the enhanced pixel value SE nc (x) is determined as

[0147] SE nc (x) = SE c (x)+a·c n(x, y) Equation (18.2)

[0148] In some embodiments, to determine the enhanced image at step 706, the controller may determine the optimal values ​​of the parameters in equation (17.1) or (17.2) by method 800. For example, at step 802, the controller may determine a set of values ​​of the first parameter c and a set of values ​​of the second parameter a by equation (17.1). At step 804, the controller determines a set of enhanced SEM images as described above.

[0149] At step 806, for each of the enhanced SEM images determined, the controller determines a waveform of enhanced pixel values ​​along a certain direction in each enhanced SEM image. In some embodiments, the direction may be any moving direction of the primary electron beam. For example, FIG. 6H to FIG. 6I As shown, there are 3 horizontal and 3 vertical white dashed lines. The white dashed lines can represent the direction of movement of the primary electron beam at different times. By scanning along the white dashed lines, the controller can determine a waveform representing a possible SEM-induced charging artifact. For example, by FIG. 6H to FIG. 6I The 1D waveform (shown as a white waveform near the white dashed line) determined by the controller is superimposed on top of the 2D graph. In some embodiments, the waveform may include a first peak corresponding to the first pattern and a second peak corresponding to the second pattern, and the first pattern and the second pattern are spatially symmetric. For example, the first peak and the second peak may be respectively Figure 6H The SEM signal peaks 630 and 632 in FIG. 630 and 632 are close to Figure 6H The first pattern and the second pattern may be the edges of the holes superimposed by the SEM signal peaks 630 and 632, respectively. For example, at step 806, the controller may use the candidate values ​​determined at step 804 and the SEM image values ​​SE for all (x, y) nc (x, y) is used to determine each of the enhanced SEM images according to equation (18.1).

[0150] At step 808, the controller provides the enhanced SEM image as one of the enhanced SEM images in the set of enhanced SEM images that has the smallest asymmetry between the first peak and the second peak. For example, among all combinations of all candidate values ​​of a and c, the controller may determine the optimal combination that shows the smallest asymmetry between the first peak and the second peak. For example, Fig.6I A SEM image is shown having minimal asymmetry between SEM signal peaks 634 and 636. If such an optimal combination is found, the controller may determine the enhanced SEM image to be the SEM image having minimal asymmetry.

[0151] In some embodiments, to account for SEM-induced charging effects at different horizontal cross-sections of the sample surface, the controller can determine a charge density value associated with the height of the location on the sample surface. For example, the controller can determine c(x, y) in equation (12). c(x, y) is related to a height mask (such as Figure 6C and its related paragraphs). The horizontal cross-sections of the height masks can have different heights. In some embodiments, each height mask can have an optimal combination of its corresponding parameters, such as a, c, n, and m in equations (10.1) or (17.1), or a and n in equations (10.2) or (17.2). In other words, the enhanced SEM image for each height mask in the height mask can be different.

[0152] In some embodiments, the controller may determine the final optimal combination of parameters based on one of the optimal combinations of parameters of the height mask. For example, the controller may adjust the SEM signal symmetry (e.g., as FIG. 6H to FIG. 6I The controller may then determine the final enhanced image to be one of the enhanced SEM images having the smallest SEM signal asymmetry.

[0153] Fig. 9 is a flow chart illustrating an exemplary image enhancement method 900 according to some embodiments of the present disclosure.

[0154] In step 902 , the controller acquires a SEM image. Step 902 may be implemented in the same manner as step 702 .

[0155] At step 904, the controller extracts a pattern from the SEM image. The pattern extraction at step 904 can be implemented similarly to the description at step 704. In some embodiments, the controller can use image processing techniques for pattern extraction. In some embodiments, if the pattern is severely distorted due to SEM-induced charging effects, the controller can use an iterative method for pattern extraction and SEM image compensation. Through the iterative method, the controller can extract the pattern at step 904 and enhance the SEM image at subsequent steps 906 to 908. The controller can then extract the pattern again using the enhanced SEM image, which is Fig. 9 The dashed arrow between steps 908 and 904 is shown in FIG. The controller may repeat steps 904 to 908 until the pattern is extracted to a predetermined confidence level.

[0156] At step 906, the controller simulates the diffusion charge associated with the location of the SEM image. Step 906 may be implemented like step 704. For example, the controller may determine the diffusion charge by equation (3), (12), or (16).

[0157] At step 908, the controller provides an enhanced SEM image based on the SEM image and the diffused charge related by the compensation relationship. Step 908 can be implemented like step 706. For example, the compensation relationship can be equation (10.1), (10.2), (17.1), or (17.2). The controller can determine multiple values ​​for parameters in equations (10.1), (10.2), (17.1), and (17.2), such as parameters a, c, n, m, and D in equation (10.1), parameters a, n, and D in equation (10.2), parameters a, c, n, m, σ in equation (17.1), and x and σ y , or the parameters a, n, σ in equation (17.2) x and σ y Using different combinations of values ​​for the parameters, the controller can determine enhanced pixel values ​​for pixels of the SEM image. For example, the enhanced pixel values ​​can be determined by equations (11.1), (11.2), (18.1), or (18.2). Using the enhanced pixel values, the controller can provide an enhanced SEM image (such as by implementing method 800).

[0158] These embodiments may be further described using the following terms:

[0159] 1. A method for enhancing an image, the method comprising:

[0160] Acquire scanning electron microscope (SEM) images;

[0161] modeling diffuse charges associated with locations of the SEM image; and

[0162] Based on the SEM image and the diffused charges, an enhanced SEM image is provided.

[0163] 2. The method of clause 1, wherein acquiring the SEM image comprises:

[0164] The SEM image is acquired by scanning the surface of the sample.

[0165] 3. The method of clause 1, wherein acquiring the SEM image comprises:

[0166] Receive SEM images from a database.

[0167] 4. A method according to any of the preceding clauses, wherein the SEM image comprises a pattern of structures at the locations of the SEM image, and the pattern comprises SEM-induced charging artifacts.

[0168] 5. A method according to any of the preceding clauses, further comprising:

[0169] After acquiring the SEM image, the pattern is extracted from the SEM image.

[0170] 6. The method of clause 5, wherein the pattern comprises an outline.

[0171] 7. A method according to any of clauses 5 to 6, wherein the pattern is associated with height variations on the surface of the sample.

[0172] 8. The method according to any one of clauses 5 to 7, wherein extracting the pattern from the SEM image comprises:

[0173] The pattern is determined as pixels of the SEM image, wherein the pixel values ​​of the pixels satisfy a threshold condition.

[0174] 9. A method according to any of the preceding clauses, wherein simulating diffuse charges associated with locations of the SEM image comprises:

[0175] A first model is determined, wherein the first model is based on movement of diffused charge caused by a SEM-induced charging effect at the location.

[0176] 10. The method of any of the preceding clauses, wherein simulating diffuse charges associated with locations of the SEM image comprises:

[0177] Based on the diffusion charge distribution and the duration of the electron beam irradiating the location, the diffusion charge is determined, wherein the location is in a scan path of the electron beam of the SEM, and the diffusion charge distribution is correlated to the diffusion coefficient.

[0178] 11. The method of clause 10, wherein the diffuse charge distribution is one of a one-dimensional diffuse charge distribution, a two-dimensional diffuse charge distribution, or a three-dimensional diffuse charge distribution.

[0179] 12. The method of clause 11, wherein a dimension of the one-dimensional diffuse charge distribution is substantially parallel to a direction of the scan path.

[0180] 13. The method of any one of clauses 1 to 8, wherein simulating diffuse charges associated with locations of the SEM image comprises:

[0181] A second model is determined, wherein the second model is based on a charge density of diffused charges caused by a SEM-induced charging effect at the location and a weighting of the charge density.

[0182] 14. The method of any one of clauses 1 to 8, wherein simulating diffuse charges associated with locations of the SEM image comprises:

[0183] For a set of positions of the SEM image, determining a charge density value and a weight value associated with the set of positions; and

[0184] By performing a convolution of the charge density value at the location with the weight value, the diffusion charge at the location is determined.

[0185] 15. The method of clause 14, wherein simulating diffuse charges associated with locations of the SEM image comprises:

[0186] For the set of positions of the SEM image, determining beam profile values ​​of the electron beam at the set of positions when the electron beam is irradiated at the positions; and

[0187] The diffusion charge at the location is determined by performing a convolution of the charge density value, the weight value, and the percentage value at the location.

[0188] 16. A method according to any of clauses 14 to 15, wherein the charge density values ​​are binarized.

[0189] 17. A method according to any of clauses 14 to 16, wherein the charge density value is associated with a height of the set of locations on the surface of the sample.

[0190] 18. A method according to any one of clauses 14 to 17, wherein the weight values ​​are in a two-dimensional Gaussian distribution.

[0191] 19. A method according to any one of clauses 14 to 18, wherein the weight values ​​are in a two-dimensional Lorentzian distribution.

[0192] 20. A method according to any one of clauses 9 to 19, wherein using the SEM image and the diffused charges to provide an enhanced SEM image comprises:

[0193] An enhanced SEM image is provided by adjusting pixel values ​​of the SEM image at the location based on one of the first model or the second model to reduce SEM-induced charging artifacts.

[0194] 21. The method of any preceding clause, wherein using the SEM image and the diffused charges to provide an enhanced SEM image comprises:

[0195] For a pixel of the SEM image, determining a corrected pixel value by shifting the pixel value of the pixel by a first parameter, determining a scaling factor by determining a second parameter as a product of the corrected pixel value and a diffusion charge associated with the pixel, and determining an enhanced pixel value by adding the corrected pixel value and the product scaled by the second parameter; and

[0196] The enhanced pixel values ​​are used to provide an enhanced SEM image.

[0197] 22. The method of clause 21, wherein using the SEM image and the diffused charge to provide an enhanced SEM image comprises:

[0198] determining a set of values ​​for a first parameter and a set of values ​​for a second parameter;

[0199] determining a set of enhanced SEM images, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter and one of the set of values ​​for the second parameter;

[0200] For each enhanced SEM image, determining a waveform of enhanced pixel values ​​along a direction in each enhanced SEM image, wherein the waveform includes a first peak corresponding to a first pattern and a second peak corresponding to a second pattern, and the first pattern and the second pattern are spatially symmetric; and

[0201] The enhanced SEM image is provided as one enhanced SEM image in a set of enhanced SEM images having minimal asymmetry between the first peak and the second peak.

[0202] 23. The method of clause 22, wherein using the SEM image and the diffused charge to provide an enhanced SEM image further comprises:

[0203] determining a set of diffusion charge values ​​based on the diffusion charge distribution and a duration of the electron beam irradiating the location, wherein the location is in a scan path of the electron beam and the diffusion charge distribution is associated with a set of diffusion coefficients; and

[0204] The set of enhanced SEM images is determined, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter, one of the set of values ​​for the second parameter, and one of the set of diffusion charge values.

[0205] 24. An inspection system comprising:

[0206] a memory storing an instruction set; and

[0207] A processor configured to execute a set of instructions to cause the inspection system to:

[0208] Acquire scanning electron microscope (SEM) images;

[0209] modeling diffuse charges associated with locations of the SEM image; and

[0210] Based on the SEM image and the diffused charges, an enhanced SEM image is provided.

[0211] 25. The inspection system of clause 24, wherein the set of instructions for causing the inspection system to acquire the SEM image further causes the inspection system to:

[0212] SEM images are acquired by scanning the surface of the sample.

[0213] 26. The inspection system of clause 24, wherein the set of instructions for causing the inspection system to acquire the SEM image further causes the inspection system to:

[0214] Receive SEM images from a database.

[0215] 27. An inspection system according to any of clauses 24 to 26, wherein the SEM image comprises a pattern of structures of the sample surface, and the pattern comprises SEM-induced charging artifacts.

[0216] 28. An inspection system according to any of clauses 24 to 27, wherein the set of instructions further causes the inspection system to:

[0217] After acquiring the SEM image, the pattern is extracted from the SEM image.

[0218] 29. An inspection system according to clause 28, wherein the pattern includes a contour.

[0219] 30. An inspection system according to any of clauses 28 to 29, wherein the pattern is associated with height variations on the surface of the sample.

[0220] 31. An inspection system according to any of clauses 28 to 30, wherein the set of instructions for causing the inspection system to extract the pattern from the SEM image further causes the inspection system to:

[0221] The pattern is determined as pixels of the SEM image, wherein the pixel values ​​of the pixels satisfy a threshold condition.

[0222] 32. An inspection system according to any of clauses 24 to 31, wherein the set of instructions for causing the inspection system to simulate diffused charges associated with locations of the SEM image further causes the inspection system to:

[0223] A first model is determined, wherein the first model is based on movement of diffused charge caused by a SEM-induced charging effect at the location.

[0224] 33. An inspection system according to any of clauses 24 to 32, wherein the set of instructions for causing the inspection system to simulate diffused charges associated with locations of the SEM image further causes the inspection system to:

[0225] Based on the diffusion charge distribution and the duration of the electron beam irradiating the location, the diffusion charge is determined, wherein the location is in a scan path of the electron beam of the SEM, and the diffusion charge distribution is correlated to the diffusion coefficient.

[0226] 34. The inspection system of clause 33, wherein the diffuse charge distribution is one of a one-dimensional diffuse charge distribution, a two-dimensional diffuse charge distribution, or a three-dimensional diffuse charge distribution.

[0227] 35. The inspection system of clause 34, wherein a dimension of the one-dimensional diffuse charge distribution is substantially parallel to a direction of the scan path.

[0228] 36. An inspection system according to any of clauses 24 to 31, wherein the set of instructions for causing the inspection system to simulate diffused charges associated with locations of the SEM image further causes the inspection system to:

[0229] A second model is determined, wherein the second model is based on a charge density of diffused charges caused by a SEM-induced charging effect at the location and a weighting of the charge density.

[0230] 37. An inspection system according to any of clauses 24 to 31, wherein the set of instructions for causing the inspection system to simulate diffused charges associated with locations of the SEM image further causes the inspection system to:

[0231] For a set of positions of the SEM image, determining a charge density value and a weight value associated with the set of positions; and

[0232] The diffusion charge at the location is determined by performing a convolution of the charge density value at the location with the weight value.

[0233] 38. The inspection system of clause 37, wherein the set of instructions for causing the inspection system to simulate diffused charges associated with locations of the SEM image further causes the inspection system to:

[0234] For the set of positions of the SEM image, determining beam profile values ​​of the electron beam at the set of positions when the electron beam is irradiated at the positions; and

[0235] The diffusion charge at the location is determined by performing a convolution of the charge density value, the weight value, and the percentage value at the location.

[0236] 39. An inspection system according to any of clauses 37 to 38, wherein the charge density values ​​are binarized.

[0237] 40. An inspection system according to any of clauses 37 to 39, wherein the charge density values ​​are associated with heights of the set of locations on the surface of the sample.

[0238] 41. An inspection system according to any one of clauses 37 to 40, wherein the weight values ​​are in a two-dimensional Gaussian distribution.

[0239] 42. An inspection system according to any one of clauses 37 to 41, wherein the weight values ​​are in a two-dimensional Lorentzian distribution.

[0240] 43. An inspection system according to any of clauses 32 to 42, wherein the set of instructions for causing the inspection system to use the SEM image and the diffused charge to provide an enhanced SEM image further causes the inspection system to:

[0241] An enhanced SEM image is provided by adjusting pixel values ​​of the SEM image at the location based on one of the first model or the second model to reduce SEM-induced charging artifacts.

[0242] 44. An inspection system according to any of clauses 24 to 43, wherein the set of instructions for causing the inspection system to use the SEM image and the diffused charge to provide an enhanced SEM image further causes the inspection system to:

[0243] For a pixel of the SEM image, determining a corrected pixel value by shifting the pixel value of the pixel by a first parameter, determining a scaling factor by determining a second parameter as a product of the corrected pixel value and a diffusion charge associated with the pixel, and determining an enhanced pixel value by adding the corrected pixel value and the product scaled by the second parameter; and

[0244] The enhanced pixel values ​​are used to provide an enhanced SEM image.

[0245] 45. The inspection system of clause 44, wherein the set of instructions for causing the inspection system to use the SEM image and the diffused charge to provide an enhanced SEM image further causes the inspection system to:

[0246] determining a set of values ​​for a first parameter and a set of values ​​for a second parameter;

[0247] determining a set of enhanced SEM images, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter and one of the set of values ​​for the second parameter;

[0248] For each enhanced SEM image, determining a waveform of enhanced pixel values ​​along a direction in each enhanced SEM image, wherein the waveform includes a first peak corresponding to a first pattern and a second peak corresponding to a second pattern, and the first pattern and the second pattern are spatially symmetric; and

[0249] The enhanced SEM image is provided as one enhanced SEM image in a set of enhanced SEM images having a minimum asymmetry between the first peak and the second peak.

[0250] 46. ​​The inspection system of clause 45, wherein the set of instructions for causing the inspection system to use the SEM image and the diffused charge to provide an enhanced SEM image further causes the inspection system to:

[0251] determining a set of diffusion charge values ​​based on the diffusion charge distribution and a duration of the electron beam irradiating the location, wherein the location is in a scan path of the electron beam and the diffusion charge distribution is associated with a set of diffusion coefficients; and

[0252] The set of enhanced SEM images is determined, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter, one of the set of values ​​for the second parameter, and one of the set of diffusion charge values.

[0253] 47. A non-transitory computer readable medium storing a set of instructions executable by at least one processor of a device to cause the device to perform a method for enhancing an image, the method comprising:

[0254] Acquire scanning electron microscope (SEM) images;

[0255] modeling diffuse charges associated with locations of the SEM image; and

[0256] Based on the SEM image and the diffused charges, an enhanced SEM image is provided.

[0257] 48. The non-transitory computer readable medium of clause 47, wherein acquiring the SEM image comprises:

[0258] SEM images are acquired by scanning the surface of the sample.

[0259] 49. The non-transitory computer readable medium of clause 47, wherein acquiring the SEM image comprises:

[0260] Receive SEM images from a database.

[0261] 50. The non-transitory computer readable medium of any of clauses 47 to 49, wherein the SEM image comprises a pattern of structures of the sample surface, and the pattern comprises SEM-induced charging artifacts.

[0262] 51. The non-transitory computer-readable medium of any one of clauses 47 to 48, wherein the method further comprises:

[0263] After acquiring the SEM image, the pattern is extracted from the SEM image.

[0264] 52. The non-transitory computer-readable medium of clause 51, wherein the pattern comprises an outline.

[0265] 53. The non-transitory computer readable medium of any of clauses 51 to 52, wherein the pattern is associated with height variations on a surface of the sample.

[0266] 54. The non-transitory computer readable medium of any one of clauses 51 to 53, wherein extracting the pattern from the SEM image comprises:

[0267] The pattern is determined as pixels of the SEM image, wherein the pixel values ​​of the pixels satisfy a threshold condition.

[0268] 55. The non-transitory computer readable medium of any one of clauses 47 to 54, wherein simulating diffused charges associated with locations of the SEM image comprises:

[0269] A first model is determined, wherein the first model is based on movement of diffused charge caused by a SEM-induced charging effect at the location.

[0270] 56. The non-transitory computer readable medium of any one of clauses 47 to 55, wherein simulating diffused charges associated with locations of the SEM image comprises:

[0271] Based on the diffusion charge distribution and the duration of the electron beam irradiating the location, the diffusion charge is determined, wherein the location is in a scan path of the electron beam of the SEM, and the diffusion charge distribution is correlated to the diffusion coefficient.

[0272] 57. The non-transitory computer readable medium of clause 56, wherein the diffuse charge distribution is one of a one-dimensional diffuse charge distribution, a two-dimensional diffuse charge distribution, or a three-dimensional diffuse charge distribution.

[0273] 58. The non-transitory computer readable medium of clause 57, wherein a dimension of the one-dimensional diffuse charge distribution is substantially parallel to a direction of the scan path.

[0274] 59. The non-transitory computer readable medium of any one of clauses 47 to 54, wherein simulating diffused charges associated with locations of the SEM image comprises:

[0275] A second model is determined, wherein the second model is based on a charge density of diffused charges caused by a SEM-induced charging effect at the location and a weighting of the charge density.

[0276] 60. The non-transitory computer readable medium of any one of clauses 47 to 54, wherein simulating diffused charges associated with locations of the SEM image comprises:

[0277] For a set of positions of the SEM image, determining a charge density value and a weight value associated with the set of positions; and

[0278] The diffusion charge at the location is determined by performing a convolution of the charge density value at the location with the weight value.

[0279] 61. The non-transitory computer readable medium of clause 60, wherein simulating diffused charges associated with locations of the SEM image comprises:

[0280] For the set of positions of the SEM image, determining beam profile values ​​of the electron beam at the set of positions when the electron beam is irradiated at the positions; and

[0281] The diffusion charge at the location is determined by performing a convolution of the charge density value, the weight value, and the percentage value at the location.

[0282] 62. The non-transitory computer readable medium of any one of clauses 60 to 61, wherein the charge density values ​​are binarized.

[0283] 63. The non-transitory computer readable medium of any of clauses 60 to 62, wherein the charge density values ​​are associated with heights of the set of locations on the surface of the sample.

[0284] 64. A non-transitory computer-readable medium as described in any of clauses 60 to 63, wherein the weight values ​​are in a two-dimensional Gaussian distribution.

[0285] 65. The non-transitory computer-readable medium of any one of clauses 60 to 64, wherein the weight values ​​are in a two-dimensional Lorentzian distribution.

[0286] 66. The non-transitory computer readable medium of any one of clauses 47 to 65, wherein using the SEM image and the diffused charges to provide an enhanced SEM image comprises:

[0287] An enhanced SEM image is provided by adjusting pixel values ​​of the SEM image at the location based on one of the first model or the second model to reduce SEM-induced charging artifacts.

[0288] 67. The non-transitory computer readable medium of any one of clauses 47 to 66, wherein using the SEM image and the diffused charges to provide an enhanced SEM image comprises:

[0289] For a pixel of the SEM image, determining a corrected pixel value by shifting the pixel value of the pixel by a first parameter, determining a scaling factor by determining a second parameter as a product of the corrected pixel value and a diffusion charge associated with the pixel, and determining an enhanced pixel value by adding the corrected pixel value and the product scaled by the second parameter; and

[0290] The enhanced pixel values ​​are used to provide an enhanced SEM image.

[0291] 68. The non-transitory computer readable medium of clause 67, wherein using the SEM image and the diffused charges to provide an enhanced SEM image comprises:

[0292] determining a set of values ​​for a first parameter and a set of values ​​for a second parameter;

[0293] determining a set of enhanced SEM images, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter and one of the set of values ​​for the second parameter;

[0294] For each enhanced SEM image, determining a waveform of enhanced pixel values ​​along a direction in each enhanced SEM image, wherein the waveform includes a first peak corresponding to a first pattern and a second peak corresponding to a second pattern, and the first pattern and the second pattern are spatially symmetric; and

[0295] The enhanced SEM image is provided as one enhanced SEM image in a set of enhanced SEM images having a minimum asymmetry between the first peak and the second peak.

[0296] 69. The non-transitory computer readable medium of clause 68, wherein using the SEM image and the diffused charges to provide an enhanced SEM image further comprises:

[0297] determining a set of diffusion charge values ​​based on the diffusion charge distribution and a duration of the electron beam irradiating the location, wherein the location is in a scan path of the electron beam and the diffusion charge distribution is associated with a set of diffusion coefficients; and

[0298] The set of enhanced SEM images is determined, wherein each enhanced SEM image is determined using one of the set of values ​​for the first parameter, one of the set of values ​​for the second parameter, and one of the set of diffusion charge values.

[0299] A non-transitory computer-readable medium may be provided that stores a program for use by a processor (eg, Figure 1 The processor executes instructions of the controller 109) that performs image processing, data processing, database management, graphic display, operation of a charged particle beam device or another imaging apparatus, etc. 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, CD-ROMs, any other optical data storage media, any physical media with a pattern of holes, RAM, PROM and EPROM, FLASH-EPROM or any other flash memory, NVRAM, cache, registers, any other memory chip or cartridge, and networked versions thereof.

[0300] The block diagrams in the accompanying drawings illustrate the possible implementation architecture, functionality and operation of the system, method and computer hardware or software product according to various exemplary embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, segment or part of a code, which includes 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 functionality involved, two boxes shown in succession may be executed or implemented substantially simultaneously, or sometimes two boxes may 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.

[0301] It should be understood that the embodiments of the present disclosure are not limited to the exact constructions that have been described above and shown in the accompanying drawings, and that various modifications and changes may be made without departing from the scope of the present disclosure.

Claims

1. A method for enhancing an image, the method comprising: include: Obtain a scanning electron microscope (SEM) image of the sample; simulating SEM-induced charging effects associated with locations of the SEM image, wherein the simulated SEM-induced charging effects include simulated diffuse charge accumulated on or near the surface of the sample induced by scanning an electron beam over the surface of the sample; as well as Based on the SEM image and the simulated SEM-induced charging effect, an enhanced SEM image is provided.

2. The method according to claim 1, wherein the SEM image is obtained include: The SEM image is acquired by scanning the surface of the sample.

3. The method according to claim 1, wherein the SEM image is obtained include: The SEM image is received from a database. 4 . The method of claim 1 , wherein the SEM image comprises a pattern of structures at the locations of the SEM image, and the pattern comprises SEM-induced charging artifacts.

5. The method according to claim 1, further comprising: include: After acquiring the SEM image, a pattern is extracted from the SEM image. The method of claim 5 , wherein the pattern comprises an outline. The method of claim 5 , wherein the pattern is associated with height variations on a surface of the sample.

8. The method according to claim 5, wherein the pattern is extracted from the SEM image include: The pattern is extracted as pixels of the SEM image, wherein pixel values ​​of the pixels satisfy a threshold condition, wherein the threshold condition comprises a pixel brightness value above or below a predetermined value or within a predetermined range, or a pixel position within an edge flooding region.

9. The method of claim 1, wherein the SEM-induced charging effects associated with the locations of the SEM images are simulated include: A first model is determined, wherein the first model is based on movement of the diffused charges caused by the SEM-induced charging effect at the location.

10. The method of claim 1, wherein simulating the SEM-induced charging effects associated with the locations of the SEM images include: A diffusion charge is determined based on a diffusion charge distribution and a duration of irradiating the location with the electron beam, wherein the location is in a scan path of the electron beam of the SEM, and the diffusion charge distribution is associated with a diffusion coefficient. 11 . The method of claim 10 , wherein the diffuse charge distribution is one of a one-dimensional diffuse charge distribution, a two-dimensional diffuse charge distribution, or a three-dimensional diffuse charge distribution.

12. The method of claim 11, wherein a dimension of the one-dimensional diffuse charge distribution is substantially parallel to a direction of the scan path.

13. The method of claim 1, wherein simulating the SEM-induced charging effects associated with the locations of the SEM images include: A second model is determined, wherein the second model is based on a charge density of the diffused charges caused by the SEM-induced charging effect at the location and a weighting of the charge density.

14. The method of claim 1, wherein simulating the SEM-induced charging effects associated with the locations of the SEM images include: For a set of positions of the SEM image, determining a charge density value and a weight value associated with the set of positions; as well as The diffused charge at the location is determined by performing a convolution of the charge density value at the location with the weight value.

15. An inspection system, include: Memory, storing instruction sets; as well as A processor configured to execute the set of instructions to cause the inspection system to: Obtain a scanning electron microscope (SEM) image of the sample; simulating SEM-induced charging effects associated with locations of the SEM image, wherein the simulated SEM-induced charging effects include simulated diffuse charge accumulated on or near the surface of the sample induced by scanning an electron beam over the surface of the sample; as well as Based on the SEM image and the simulated SEM-induced charging effect, an enhanced SEM image is provided.

Images