Pattern alignment method and device and storage medium

Abstract

The embodiment of the invention relates to a pattern alignment method and device and a storage medium. The method proposed herein includes: acquiring a first image and a second image related to a wafer, where the first image includes a first pattern, the second image includes a second pattern, and the second pattern includes a sub-pattern corresponding to the first pattern; in response to determining that at least one part of the first pattern has periodicity, determining corresponding period types of the first pattern and the second pattern based on the basic period units of the first pattern, a period type in the corresponding period types indicating a distribution range of the basic period units in the corresponding pattern in the pattern; and aligning the first pattern and the second pattern based on an alignment strategy matched with the corresponding period type.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of semiconductor technology, and more specifically, to a pattern alignment method, apparatus, and storage medium. Background Technology

[0002] Defect detection, dimensional measurement, and process monitoring are critical steps in semiconductor manufacturing. Currently, image acquisition devices can be used to image the wafer surface, enabling monitoring and quality assessment of the manufacturing process. For example, a scanning electron microscope (SEM) can be used to image the microstructure on the wafer, obtaining SEM images. With the help of SEM images, not only can the morphology of the circuit structure be observed, but also critical dimensions (CDs) can be precisely measured, and potential defects can be effectively located. Summary of the Invention

[0003] In a first aspect of this disclosure, a pattern alignment method is provided. The method includes: acquiring a first image and a second image relating to a wafer, wherein the first image includes a first pattern, the second image includes a second pattern, and the second pattern includes a sub-pattern corresponding to the first pattern; in response to determining that at least a portion of the first pattern is periodic, determining corresponding periodic types of the first and second patterns based on fundamental periodic units of the first pattern, wherein the periodic type in the corresponding periodic type indicates the distribution range of fundamental periodic units in the corresponding pattern within the pattern; and aligning the first and second patterns based on an alignment strategy matching the corresponding periodic type.

[0004] In a second aspect of this disclosure, an electronic device is provided. The device includes at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. When executed by the at least one processor, the instructions cause the device to perform the method of the first aspect.

[0005] In a third aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions that can be executed by a processor to implement the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer program product is provided. The computer program product includes computer-executable instructions that, when executed by a processor, implement the method according to a first aspect of this disclosure.

[0007] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0008] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A schematic diagram of an example environment according to some embodiments of the present disclosure is shown; Figure 2 A flowchart illustrating an example process of a pattern alignment method according to some embodiments of the present disclosure is shown; Figures 3A to 3B Schematic diagrams are shown of several examples of periodic types according to some embodiments of the present disclosure; Figure 3C Schematic diagrams illustrating examples of patterns according to some embodiments of the present disclosure that do not exhibit periodicity at a microscopic scale; and Figure 4 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0009] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0010] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0011] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0012] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0013] As briefly described above, imaging the wafer surface using image acquisition devices such as SEM enables the monitoring and quality assessment of the manufacturing process. To effectively assess process stability, track defect evolution, perform overlay error analysis, or accurately measure critical dimensions, it is typically necessary to align and compare SEM images of specific locations on the wafer (e.g., a region within the same die) with those of different process steps (e.g., photolithography and / or etching).

[0014] However, in advanced manufacturing processes, wafer surfaces often contain highly regular and repetitive structures, such as memory cell arrays in memory chips and fin field-effect transistor arrays in logic chips. These structures appear as periodic patterns in SEM images, and these periodic patterns pose significant challenges to alignment.

[0015] Traditional alignment methods include feature-point-based alignment, mutual information-based alignment, and template-matching-based alignment. While these methods can address the aforementioned problems to some extent, their alignment capabilities still fall short of practical needs. Specifically, periodic patterns in an image may exhibit multiple periodicity types. For example, a periodic pattern might be distributed in a local region of the image; such a pattern can be considered locally periodic, and its corresponding periodicity type can be called a local periodicity type. Alternatively, a periodic pattern might be distributed throughout the entire image; such a pattern can be considered globally periodic, and its corresponding periodicity type can be called a global periodicity type. Furthermore, a globally periodicity type of periodic pattern might be aperiodic at a microscopic scale, and so on. The aforementioned methods cannot distinguish between these periodicity types. Because periodic patterns of different periodicity types have different structural characteristics, applying the same alignment scheme to these periodic patterns will result in a large number of mismatches.

[0016] Embodiments of this disclosure provide a pattern alignment scheme. Specifically, in this scheme, a first image and a second image related to a wafer are acquired. The first image includes a first pattern, the second image includes a second pattern, and the second pattern includes a sub-pattern corresponding to the first pattern. In response to determining that at least a portion of the first pattern is periodic, corresponding periodic types of the first and second patterns are determined based on the basic periodic units of the first pattern. The periodic type indicates the distribution range of the basic periodic units in the corresponding pattern within that pattern. The first and second patterns are aligned based on an alignment strategy matching the corresponding periodic types of the first and second patterns.

[0017] According to the scheme of this disclosure embodiment, based on the basic periodic units of the first pattern, the periodicity type of the pattern to be aligned (e.g., the first pattern and the second pattern, the second pattern being larger and containing sub-patterns corresponding to the first pattern) can be accurately identified. Based on this, the most suitable alignment strategy can be selected according to the specific characteristics of the periodicity type, thereby avoiding the mismatch problem caused by the failure to distinguish different periodicity types in traditional methods. For example, for patterns with local periodicity types, an alignment algorithm that focuses more on detailed features can be adopted. For example, for patterns with global periodicity types, consistency at a larger scale can be considered, and so on. In this way, not only is the alignment problem of various periodic patterns improved, but the accuracy and success rate of alignment are also significantly enhanced. In particular, this method can be applied to complex structures in semiconductor manufacturing (e.g., memory cell arrays and fin field-effect transistor arrays), thereby providing a better data foundation for corresponding process monitoring, defect detection, and critical dimension measurement.

[0018] The following description, in conjunction with the accompanying drawings, will illustrate an example implementation of the pattern alignment scheme of the present disclosure. Figure 1A schematic diagram of an example environment 100 according to some embodiments of the present disclosure is shown. (Refer to...) Figure 1 Environment 100 includes electronic device 110, first image 121, second image 122, and alignment result 130. It should be understood that the structure and function of the various elements in environment 100 are described below for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0019] The first image 121 may refer to an image on a wafer corresponding to a certain chip cell (also referred to herein as a first chip cell) and a certain process step (also referred to herein as a first process step, such as photolithography). The pattern in the first image 121 can serve as a template in the pattern alignment process, and this pattern is also referred to herein as a template pattern. The second image 122 may include a pattern to be aligned to the template pattern, and this pattern is also referred to herein as a target pattern. In some embodiments, the second image 122 and the first image 121 may correspond to the same chip cell. For example, the second image 122 may be an image of the first chip cell corresponding to a process step (also referred to herein as a second process step, such as etching) that is different from the first process step. Alternatively, in some embodiments, the second image 122 and the first image 121 may correspond to different chip cells. For example, the second image 122 may be an image of the second chip cell corresponding to the first process step, and so on.

[0020] The first image 121 and the second image 122 can be images of the same type or different types. For example, one of the first image 121 and the second image 122 can be a layout, and the other can be a detection image. Alternatively, both the first image 121 and the second image 122 can be detection images, and so on. The layout can be a design layout for a wafer (e.g., graphic data stored in Graphic Design System (GDS) format or any other suitable format, also referred to herein as a GDS layout). The layout can describe the ideal arrangement of the geometry of each layer in the chip to be fabricated. The detection image can be a wafer surface image obtained by physical inspection. For example, a detection image can be an image obtained by SEM acquisition of the wafer surface topography (also referred to herein as an SEM image) or a wafer surface image acquired by any other suitable device.

[0021] In environment 100, if at least a portion of the template pattern is periodic, electronic device 110 determines the corresponding periodicity type of the template pattern and the target pattern based on the basic periodic units of the template pattern (e.g., the template pattern is a global periodicity type, the target pattern is a local periodicity type, etc.). Electronic device 110 aligns the two patterns based on an alignment strategy that matches the corresponding periodicity types of the two patterns (e.g., after cropping the non-periodic portion of the target pattern, electronic device 110 aligns the template pattern and the target pattern based on structural similarity, etc.), thereby obtaining alignment result 130.

[0022] In environment 100, electronic device 110 can be any type of device with computing capabilities. For example, electronic device 110 may include terminal device or server device.

[0023] In some embodiments, the terminal device may be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio broadcast receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof.

[0024] In some embodiments, the server-side device may be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. The server-side device may include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in a cloud environment, and so on.

[0025] Figure 2 A flowchart illustrating an example process 200 of a pattern alignment method according to some embodiments of the present disclosure is shown below. Figure 1 Process 200 will be described. Process 200 may be implemented, for example, at electronic device 110.

[0026] Reference Figure 2In frame 210, electronic device 110 acquires a first image 121 and a second image 122 related to the wafer. The first image 121 includes a first pattern, and the second image 122 includes a second pattern. The first pattern may be a partial or complete cropped from the first image 121. The first pattern can serve as a reference in the pattern alignment process; herein, the first pattern is also referred to as a template pattern. The second pattern may be a partial or complete cropped from the second image 122. The second pattern can be a pattern to be aligned to the first pattern during the pattern alignment process; herein, the second pattern is also referred to as a target pattern. The second pattern includes a sub-pattern corresponding to the first pattern. This sub-pattern may represent the same or similar structures in the wafer as the first pattern (e.g., the first pattern and the sub-pattern respectively represent transistors with the same function in different chip cells, etc.).

[0027] In some embodiments, the first image 121 and the second image 122 may both be detection images (e.g., SEM images) acquired by a detection device. The electronic device 110 can ensure that the pattern in the second image 122 (i.e., the second pattern) includes a sub-pattern corresponding to the pattern in the first image 121 (i.e., the first pattern) by changing the field of view (FOV) during the image acquisition process. For example, the electronic device 110 may acquire the first image 121 based on a first field of view and acquire the second image 122 based on a second field of view, where the range of the second field of view is larger than the range of the first field of view.

[0028] In some embodiments, the electronic device 110 can acquire multiple pairs of candidate images to be aligned, each pair including a template image with a template pattern and a target image with a target pattern. The electronic device 110 can preprocess the acquired multiple pairs of candidate images to remove at least one pair of candidate images that do not meet the alignment conditions. For example, if the target pattern is a periodic pattern with the corresponding template pattern as the basic periodic unit, the electronic device 110 can remove the corresponding pair of candidate images from the multiple pairs of candidate images. This is because such target patterns and template pattern pairs lack a unique correspondence, thus failing to support meaningful alignment.

[0029] Continue to refer to Figure 2In block 220, in response to determining that at least a portion of the first pattern is periodic, electronic device 110 determines the corresponding periodicity type of the first and second patterns based on the basic periodic units of the first pattern. The periodicity type indicates the distribution range of the basic periodic units in the corresponding pattern within that pattern. For example, the periodicity type may indicate a local distribution of the basic periodic units in the pattern. Alternatively, the periodicity type may indicate a global distribution of the basic periodic units in the pattern, and so on. As an example, electronic device 110 may compare the basic periodic units of the first pattern with the first pattern itself. This allows determination of the distribution range of the basic periodic units in the first pattern, thereby determining the periodicity type of the first pattern. Similarly, electronic device 110 compares the basic periodic units of the first pattern with the second pattern (since the second pattern includes sub-patterns corresponding to the first pattern, the second pattern also includes the basic periodic units of the first pattern). This allows determination of the distribution range of the basic periodic units of the second pattern within the second pattern, thereby determining the periodicity type of the second pattern.

[0030] Periodicity can refer to the characteristic of a pattern repeating in space according to certain rules. That is, the pattern contains one or more repeating units (also referred to herein as basic periodic units), which are arranged repeatedly at fixed intervals along one or more directions, thereby forming a periodic pattern, either overall or locally. In some embodiments, the electronic device 110 can determine an estimate of whether the first pattern is periodic based on various methods (e.g., frequency domain analysis and / or autocorrelation analysis, etc.). Based on this, the electronic device 110 can comprehensively determine whether the first pattern is periodic based on these estimates.

[0031] For example, electronic device 110 can determine a first estimate of whether the first pattern is periodic based on the frequency domain feature representation of the first pattern. The frequency domain feature representation can refer to the spectrum obtained after transforming the first pattern from the spatial domain to the frequency domain, which reflects the energy intensity and directional characteristics of different spatial frequency components in the image. In some embodiments, the frequency domain feature representation of the first pattern can be determined using an algorithm based on Fourier transform or any other suitable algorithm.

[0032] As an example, suppose the first image 121 is a SEM image. Electronic device 110 can perform a discrete Fourier transform on the SEM image (e.g., a grayscale image) to obtain the frequency domain feature representation of the first pattern. This process can be represented by formula (1): (1) Where F(u,v) represents the frequency domain feature representation, used to indicate the spatial frequency at position (u,v) in the frequency domain (also known as the frequency domain), u and v represent the horizontal and vertical coordinates in the frequency domain, M and N represent the width and height of the first image 121, j is the imaginary unit, and I(x,y) represents the brightness of the first image 121 at position (x,y) in the spatial domain (also known as the spatial domain), where x and y represent the horizontal and vertical coordinates in the spatial domain, respectively.

[0033] Electronic device 110 can cyclically shift the four quadrants of F(u,v) until the zero-frequency component moves from the four corners of the spectrum to the center. Then, electronic device 110 can calculate the amplitude spectrum. Electronic device 110 can analyze the distribution of peaks and bright spots in the off-center region of the amplitude spectrum. If there are no significant off-center peaks in the amplitude spectrum except at the center (zero frequency), it indicates that the corresponding first pattern lacks a significant repeating structure in the spatial domain. In this case, electronic device 110 can determine that the first estimate indicates the first pattern is not periodic. If there are one or more pairs of significant off-center peaks that are centrally symmetric in the horizontal, vertical, or diagonal directions, it indicates that the first pattern has a periodic structure in the corresponding direction. In this case, electronic device 110 can determine that the first estimate indicates the first pattern is periodic in that direction.

[0034] In some embodiments, the electronic device 110 may determine whether the first estimate is valid based on whether the magnitude of the deviation from the center peak exceeds a certain proportion of the center peak magnitude. For example, if the magnitude of the deviation from the center peak exceeds a certain proportion of the center peak magnitude, the electronic device 110 may determine that the periodicity of the first pattern is significant. In this case, the electronic device 110 may determine that the first estimate is valid.

[0035] In some embodiments, after determining that the first estimate indicates that the first pattern is periodic, the fundamental periodic unit in the first pattern (e.g., the smallest fundamental periodic unit in the first pattern) can be further determined. For example, for a direction determined to have significant periodicity, the electronic device 110 can determine the specific coordinates of the distance from the center peak in the corresponding region using a peak detection algorithm or other suitable algorithm. The electronic device 110 can determine the period length of the fundamental periodic unit based on the reciprocal of the spatial frequency corresponding to the coordinates. As an example, suppose the specific coordinates of the distance from the center peak are... The corresponding spatial frequency can be expressed by formula (2): , (2) in Represents spatial frequency in the horizontal direction. This represents the spatial frequency in the vertical direction.

[0036] In some embodiments, the electronic device 110 may determine a second estimate of whether the first pattern is periodic based on a self-similarity map of the first pattern. The self-similarity map may at least indicate the similarity between multiple locations in the first pattern. In some embodiments, the self-similarity map of the first pattern may be determined using a self-similarity-based algorithm or any other suitable algorithm. Self-similarity-based algorithms aim to measure the repetitiveness of an image's internal structure by matching it to itself at different spatial offsets. Specifically, the electronic device 110 may generate a self-similarity map by calculating the local similarity between each location in the first pattern and other locations using a sliding window or block matching method. In some embodiments, in the vertical direction, the self-similarity algorithm may be combined with various image preprocessing techniques, such as histogram equalization, Local Contrast Normalization (LCN), etc., to enhance texture features or suppress illumination / noise interference. In the horizontal direction, self-similarity algorithms can employ various criteria for similarity measurement, including but not limited to the sum of absolute differences (SAD) and zero-mean normalized cross-correlation (ZNCC), etc. The embodiments disclosed herein do not impose any limitations on these criteria.

[0037] After obtaining the self-similarity map, the electronic device 110 can detect whether there are significant high similarity peaks at non-zero offset positions by referring to frequency domain analysis. If there are no significant non-zero offset peaks in the self-similarity map except for the origin (zero offset), it indicates that the first pattern lacks repeating structures. In this case, the electronic device 110 can determine that the second estimate indicates the first pattern does not have periodicity. If one or more pairs of significant non-zero offset peaks symmetrical about the origin appear in the horizontal, vertical, or diagonal directions, it indicates that there is periodic repetition in the corresponding directions. In this case, the electronic device 110 can determine that the second estimate indicates the first pattern has periodicity.

[0038] In some embodiments, the electronic device 110 may determine whether the second estimate is valid based on whether the similarity score of the non-zero offset position exceeds a certain proportion of the origin (i.e., the maximum self-similarity value). For example, if the similarity score of the non-zero offset position exceeds a certain proportion of the origin, the electronic device 110 may determine that the periodicity of the first pattern is significant. In this case, the electronic device 110 may determine that the second estimate is valid. In some embodiments, after determining that the second estimate indicates that the first pattern is periodic, the fundamental periodic unit (e.g., the smallest fundamental periodic unit) in the first pattern may be further determined. The specific algorithm may be determined according to actual needs, and the embodiments of this disclosure will not be described in detail here.

[0039] After determining the first estimate and the second estimate, if at least one of the first estimate and the second estimate indicates that the first pattern is periodic, the electronic device 110 can determine that the first pattern is periodic. In this case, the electronic device 110 extracts the basic periodic unit of the first pattern and determines the corresponding periodicity type of the first pattern and the second pattern based on the basic periodic unit. If each of the first estimate and the second estimate indicates that the first pattern is not periodic, the electronic device 110 can determine that the first pattern is not periodic. In this case, the electronic device 110 can use alignment algorithms based on feature points, alignment algorithms based on mutual information, and alignment algorithms based on template matching, etc., to align the first pattern and the second pattern. By employing multiple methods in parallel to determine whether the first pattern is periodic, the ability to identify weak periodicity or local periodicity can be improved.

[0040] In some embodiments, when the first pattern is not periodic, the electronic device 110 can use a template-matching-based alignment algorithm to align the first pattern and the second pattern. In some embodiments, the template-matching-based alignment algorithm can be used in conjunction with image preprocessing methods (e.g., histogram, LCN). Furthermore, the calculation of matching similarity can be implemented based on SAD, ZNCC, etc.

[0041] If the first pattern is determined to be periodic, the electronic device 110 extracts the basic periodic units of the first pattern and determines the periodicity type of the first and second patterns based on these basic periodic units. In some embodiments, if the first pattern is periodic only in the horizontal direction, the electronic device 110 can divide the first pattern into multiple regions along the horizontal direction based on the minimum period length. If the first pattern is periodic only in the vertical direction, the electronic device 110 can divide the first pattern into multiple regions along the vertical direction based on the minimum period length. If the first pattern is periodic in both the horizontal and vertical directions, the electronic device 110 can divide the first pattern into multiple regions along both the horizontal and vertical directions based on the minimum period length. Based on this, the electronic device 110 can use the region near the center of these regions as the basic periodic unit of the first pattern.

[0042] Figures 3A to 3B Schematic diagrams of example 300A and 300B of periodic types according to some embodiments of the present disclosure are shown. (See reference...) Figures 3A to 3B In some embodiments, the cycle type may include a global cycle type and a local cycle type. For example... Figure 3A As shown, the global periodicity type can indicate that the basic periodic units 301 in pattern 302 are distributed throughout the entire area of ​​pattern 302. For example... Figure 3BAs shown, the local periodicity type can indicate that the basic periodic units 301 in pattern 302 are distributed locally within pattern 302. By distinguishing between global periodicity types and local periodicity types, electronic device 110 can adopt differentiated alignment strategies for different periodic characteristics. This adaptive processing mechanism based on periodicity type significantly improves the accuracy of image alignment, and is particularly suitable for the high-precision comparison requirements of complex hybrid patterns in advanced processes. For ease of discussion, the following description uses the example of periodicity types including global periodicity types and local periodicity types to illustrate the exemplary implementation of embodiments of this disclosure.

[0043] In some embodiments, the electronic device 110 can determine the corresponding periodicity type of the first pattern and the second pattern by comparing the basic periodic unit 301 of the first pattern with the first pattern and the second pattern, respectively. For example, for any pattern in the first pattern and the second pattern (also referred to herein as a given pattern, e.g.) Figure 3A and Figure 3B (As shown in pattern 302), the electronic device 110 can transmit the basic periodic unit of the first pattern (e.g., Figure 3A and Figure 3B Using pattern 302 as a reference, sliding matching is performed across the entire range of the given pattern to calculate similarity and identify high-similarity regions. If the similarity between the basic periodic unit of the first pattern and the given pattern meets the similarity requirement (e.g., basic periodic unit 301 shows high similarity to most areas of pattern 302), the electronic device 110 can determine that the basic periodic unit is widely and consistently repeated in the given pattern. In this case, the electronic device 110 can determine that the periodicity type of the given pattern is a global periodicity type. If the similarity between the basic periodic unit of the first pattern and the given pattern does not meet the similarity requirement (e.g., basic periodic unit 301 shows high similarity to a small portion of pattern 302), the electronic device 110 can determine that the basic periodic unit is centrally distributed in the given pattern. In this case, the electronic device 110 can determine that the periodicity type of the given pattern is a local periodicity type. In this way, the electronic device 110 can not only identify whether a pattern is periodic, but also further distinguish the spatial coverage of its periodicity, thereby providing effective data support for the selection of subsequent alignment strategies.

[0044] In some embodiments, a given pattern can be divided into multiple sub-patterns. For example, the given pattern can be divided into multiple sub-patterns based on a regular grid (such as equally spaced rectangular blocks) or based on the basic periodic units of the given pattern. Each sub-pattern can be independently compared with the basic periodic units of the first pattern. In some embodiments, the electronic device 110 can divide the given pattern into multiple sub-patterns based on the arrangement direction (such as horizontal, vertical, or diagonal) and size (such as the width and / or length of the basic periodic units) of the basic periodic units in the given pattern. In this way, the size of each sub-pattern can approximately correspond to a basic periodic unit. This division strategy helps to limit the comparison operation to areas with consistent structural semantics during the comparison process, avoiding the basic periodic units from being incorrectly matched to irrelevant patterns due to reasons such as crossing periodic boundaries, thereby improving the accuracy and reliability of periodic type identification.

[0045] In some embodiments, if the similarity between the basic periodic unit of the first pattern and each of the multiple sub-patterns is greater than a threshold similarity, the electronic device 110 can determine that the basic periodic unit is widely and consistently present throughout the given pattern. In this case, the electronic device 110 can determine that the similarity between the basic periodic unit and the given pattern meets the similarity requirement, and thus can determine that the periodicity type of the given pattern is a global periodicity type. If the similarity between the basic periodic unit of the first pattern and at least some of the multiple sub-patterns is less than a threshold similarity, the electronic device 110 can determine that the basic periodic unit is centrally distributed in the given pattern. In this case, the electronic device 110 can determine that the similarity between the basic periodic unit and the given pattern does not meet the similarity requirement, and thus can determine that the periodicity type of the given pattern is a local periodicity type. By decomposing the given pattern into sub-patterns and performing fine-grained similarity verification, the electronic device 110 can more accurately identify the spatial distribution characteristics of the periodic structure in the given pattern, thereby effectively distinguishing between global periodicity types and local periodicity types.

[0046] In some embodiments, the electronic device 110 may determine the similarity between the basic periodic unit of the first pattern and each of the plurality of sub-patterns based on ZNCC or any other suitable algorithm. The threshold similarity may be determined based on the algorithm employed. For example, for the ZNCC algorithm, the threshold similarity may be set to 0.6 or any other suitable value.

[0047] Continue to refer to Figure 2 In box 230, electronic device 110 aligns the first pattern and the second pattern based on an alignment strategy that matches the corresponding periodic type. As mentioned above, periodic types include, but are not limited to, global periodic types and local periodic types, etc.

[0048] In some embodiments, if the corresponding periodic types of the first pattern and the second pattern are both global periodic types, the electronic device 110 can acquire multiple candidate alignment positions (also referred to herein as first candidate alignment positions) for the first pattern and the second pattern. The electronic device 110 can select a target alignment position from the multiple first candidate alignment positions based on multiple similarity maps (also referred to herein as first similarity maps) of the first pattern and the second pattern corresponding to the multiple first candidate alignment positions. The electronic device 110 can align the first pattern and the second pattern based on the target alignment position. In this way, the electronic device 110 can not only identify multiple potential alignment positions, but also determine the most suitable alignment method through similarity map analysis, thereby ensuring the accuracy of pattern alignment.

[0049] In some embodiments, the electronic device 110 may utilize feature-point-based alignment algorithms, mutual information-based alignment algorithms, and / or template-matching-based alignment algorithms to determine multiple candidate alignment positions. A first similarity map of the first pattern and the second pattern may be represented based on a grayscale image. The pixel value of each pixel in the first similarity map may represent the degree of similarity between the first pattern and the second pattern at that pixel. For example, the higher the pixel value, the higher the degree of similarity between the first pattern and the second pattern at that pixel. In some embodiments, multiple first similarity maps correspond one-to-one with multiple first candidate alignment positions. For example, for each candidate alignment position among the multiple first candidate alignment positions, the electronic device 110 may align the first pattern and the second pattern based on that candidate alignment position. Subsequently, the electronic device 110 may determine a corresponding similarity map based on the aligned first pattern and the second pattern, thereby obtaining a first similarity map corresponding to that candidate alignment position.

[0050] In some embodiments, for a given alignment position among a plurality of first candidate alignment positions, the electronic device 110 can determine a similarity index between the first pattern and the second pattern at the given alignment position based on structural similarity (SSIM). The electronic device 110 can generate a first similarity map of the first pattern and the second pattern corresponding to the given alignment position, at least based on the similarity index.

[0051] In some embodiments, the electronic device 110 can align a first pattern and a second pattern based on a given alignment position. Based on this, the electronic device 110 can determine a similarity index between the first and second patterns at the given alignment position based on the brightness, contrast, and structural information of the first and second patterns at each pixel. The similarity index can indicate the degree of similarity between the first and second patterns at each pixel (or any other suitable granularity). For example, the similarity at each pixel can be based on… or The similarity index is represented by a numerical value (also referred to herein as the SSIM value). A value closer to 1 indicates a higher degree of similarity between the first and second patterns at that pixel, while a lower value indicates a lower degree of similarity. The electronic device 110 can generate a first similarity map of the first and second patterns corresponding to a given alignment position based on this similarity index. As an example, the electronic device 110 can convert the SSIM value at each pixel into the pixel value of the corresponding pixel in the first similarity map, thereby generating the first similarity map. By introducing structural similarity and generating the corresponding similarity map, the electronic device 110 can achieve effective alignment evaluation of periodic or complex patterns while preserving the semantic structure of the image.

[0052] In some embodiments, the electronic device 110 can scale the first pattern and the second pattern based on scaling factors of multiple scales (also referred to herein as scaling scales) to obtain multiple scaled patterns of the first pattern (also referred to herein as first scaled patterns) and multiple scaled patterns of the second pattern (also referred to herein as second scaled patterns). For example, the scaling scale of the scaling factor can be set to 2.0, 3.0, or any other suitable value. For each scaling factor, the electronic device 110 can generate a corresponding scaled pattern. In this case, the first pattern and the second pattern will be magnified by one time and two times, respectively. Thus, the similarity between the first pattern and the second pattern at macroscopic and / or microscopic scales can be considered according to actual needs. The electronic device 110 can determine a similarity index based on at least the first pattern, multiple first scaled patterns, the second pattern, and multiple second scaled patterns, according to structural similarity.

[0053] As an example, the scaled pattern can be represented by formula (3): ; (3) Where s represents the scaling factor. Represents multiple scaling factors The set of n, where n is a positive integer. In some embodiments, for each scaling scale, the electronic device 110 can calculate a similarity index between the first pattern and the second pattern, thereby obtaining multiple similarity indices corresponding one-to-one with multiple scaling scales. The electronic device 110 can fuse these similarity indices to obtain the final target similarity index used to generate a similarity map.

[0054] By using multiple scaling factors, the electronic device 110 can focus on the differences between the first pattern and the second pattern at macroscopic, semi-macroscopic, and microscopic scales. This process is also known as "image pyramiding," which involves comparing at different scales and then combining the comparison results (i.e., similarity indices) and mapping them back to the original scale for integration. In this way, the electronic device 110 can capture differences that are only apparent at specific scales.

[0055] In some embodiments, the electronic device 110 can adjust the similarity index based on an adjustment factor. The adjustment factor can be used to introduce at least one of the following: pattern-related translation invariance, pattern-related scaling invariance, and pattern-related rotation invariance. The electronic device 110 can generate a first similarity map of the first and second patterns corresponding to a given alignment position based on the adjusted similarity index. In some embodiments, the adjustment factor can be implemented based on Hu moments (also called Hu-moments) or any other suitable method. By applying the adjustment factor, the electronic device 110 can focus more on the shape features of the pattern itself, reducing the impact of microscale geometric transformations (such as slight translations, rotations, and scaling). This not only avoids the problem of macroscale structural features being obscured by the accumulation of numerous microscale differences, but also effectively improves the reliability and accuracy of pattern alignment.

[0056] As an example, the similarity index can be calculated using formulas (4)-(6): (4) (5) (6) in This indicates a comparison related to brightness among similarity indices. This refers to comparisons related to contrast in similarity indices. This represents structure-related comparisons, which can be performed with a window centered on the pixel (x,y). , Represents the mean of an image patch. , Indicates standard deviation, Describing covariance, , and These represent stable parameters. These stable parameters are adjustable, and when comparing the same set of images, the larger the stable parameter, the more similar they are considered.

[0057] Using formulas (4)-(6), the electronic device 110 can calculate the similarity index and similarity value corresponding to each candidate alignment position. In order to reduce the influence of geometric transformations caused by translation, rotation, and scaling at the microscale, so as to prevent the cumulative influence of the microscale from exceeding the influence of the macroscale (this is because, in practice, it is necessary to allow for differences at the microscale, and when aligning different chip units or different period positions, more attention needs to be paid to differences at the macroscale), an adjustment factor can be used in the calculation of the similarity index, and translation, rotation, and scaling invariance related to the pattern can be introduced based on the adjustment factor. In some embodiments, the adjustment factor can be implemented based on Hu moments. Hu moments can be expressed by formulas (7)-(9): I(x,y); (7) (8) (9) in This represents the center distance, used to introduce translation invariance. and are non-negative integers, representing the orders of the central moments in the horizontal and vertical directions, respectively. and It is the brightness centroid (i.e., the first moment). This represents the normalized central moment, used to introduce scaling invariance. The Hu moment can be represented by a nonlinear combination of normalized central moments of higher order (e.g., 2nd-3rd order or any other suitable order) (i.e.) It is constructed to introduce rotational invariance.

[0058] The adjustment factor can be represented by a similarity metric calculated based on Hu moments (e.g., cosine similarity between Hu moment feature vectors of different images). For example, the adjustment factor can be represented by formula (10): ;Formula (10) in , This represents two images (e.g., the first image 121 and the second image 122). , They represent respectively and Extracted Hu moment feature vectors.

[0059] In some embodiments, for multiple similarity indices corresponding to multiple scales, the electronic device 110 may select a subset of them to be adjusted using adjustment factors. For example, the electronic device 110 may select to adjust only the similarity indices at the microscale to obtain adjusted similarity indices. In some embodiments, the electronic device 110 may determine the adjusted similarity indices based on the product of the similarity indices and the adjustment factors or other appropriate methods.

[0060] In some embodiments, after determining multiple first similarity maps corresponding to multiple first candidate alignment positions, the electronic device 110 can determine whether the multiple first similarity maps meet the similarity map requirements, and select the corresponding method to determine the target alignment position based on the determination result. The similarity map requirements may be related to the number of low similarity regions in the similarity map, thereby reflecting whether the first pattern and the second pattern exhibit global periodicity at both macroscopic and microscopic scales.

[0061] For example, if multiple first similarity maps meet the similarity map requirements (e.g., the number of low-similarity regions in each first similarity map is small), electronic device 110 can determine that the first pattern and the second pattern exhibit high similarity at all candidate alignment positions. This means that regardless of which candidate position is selected as the target alignment position, the two patterns can maintain a good matching state. Therefore, electronic device 110 can determine that the first and second patterns not only exhibit global periodicity at a macroscopic scale but also possess this characteristic at a microscopic scale (e.g., ...). Figure 3A (As shown). In this case, since each candidate alignment position provides a reliable matching result, the electronic device 110 can randomly select a target alignment position from multiple first candidate alignment positions. If at least one of the multiple first similarity maps does not meet the similarity map requirements (e.g., there are many low similarity regions in each first similarity map), the electronic device 110 can determine that only at a specific candidate alignment position can the first pattern and the second pattern achieve a satisfactory matching effect. This means that although the two patterns exhibit global periodicity at a macroscopic scale, they may be non-periodic at a microscopic scale or only exhibit periodicity locally. Figure 3C A schematic diagram of example 300C, showing a pattern according to some embodiments of the present disclosure that does not exhibit periodicity at a microscopic scale, is illustrated. (Refer to...) Figure 3CAt a macroscopic scale, the fundamental periodic unit 301 of pattern 302 can be considered to have global periodicity. However, at a microscopic scale, the local units 303 of the fundamental periodic unit 301 do not have periodicity. In this case, to ensure optimal alignment accuracy, the electronic device 110 can introduce additional information to assist the decision-making process. For example, the electronic device 110 can select a target alignment position from multiple first candidate alignment positions based at least on multiple similarity values ​​of the first and second patterns corresponding to multiple first candidate alignment positions. Alternatively, the electronic device 110 can select a target alignment position from multiple first candidate alignment positions based on multiple similarity values, a first similarity map, and / or the matching degree between the first and second patterns determined using a basic template matching algorithm. In this way, the electronic device 110 can further analyze the periodicity of the pattern at the microscopic scale, given that the pattern has global periodicity, and select the target alignment position accordingly. This further improves the accuracy and reliability of pattern alignment.

[0062] In some embodiments, for a given similarity map among a plurality of first similarity maps, electronic device 110 can determine pixels in the given similarity map whose pixel values ​​are lower than a threshold pixel value (e.g., 0.6 or any other suitable value) to obtain at least one target pixel. The at least one target pixel can reflect a region where the first pattern and the second pattern have low similarity, i.e., a low-similarity region. Electronic device 110 can determine a reference pixel ratio for the given similarity map based on the proportion of the at least one target pixel to the total pixels in the given similarity map. If the reference pixel ratios of each of the plurality of first similarity maps are all less than a threshold pixel ratio (e.g., 10% or any other suitable value), electronic device 110 can determine that there are relatively few low-similarity regions in these first similarity maps. In this case, electronic device 110 can determine that the plurality of first similarity maps meet the similarity map requirements (i.e., the first and second patterns exhibit global periodicity not only at a macroscopic scale but also at a microscopic scale), and can then randomly select a target alignment position from a plurality of first candidate alignment positions. If the proportion of reference pixels in at least one of the multiple first similarity maps is greater than a threshold pixel proportion, the electronic device 110 can determine that there are many low-similarity regions in these first similarity maps. In this case, the electronic device 110 can determine that the first similarity map does not meet the similarity map requirements (that is, although the first and second patterns exhibit global periodicity at a macroscopic scale, they may be non-periodic or only locally periodic at a microscopic scale). Furthermore, more information, such as similarity values, can be introduced to select a target alignment position from multiple first candidate alignment positions. In this way, the electronic device 110 can quickly and accurately identify the periodicity of the pattern at the microscopic scale, and thus take appropriate measures to select the target alignment position.

[0063] A similarity value can be a quantitative indicator used to measure the degree of similarity between a first pattern and a second pattern at a specific alignment position. The electronic device 110 can determine the similarity value based on any suitable similarity measurement algorithm. These algorithms include, but are not limited to, SSIM-based algorithms, ZNCC-based algorithms, and mean squared error-based algorithms, etc. In some embodiments, for a given alignment position among a plurality of first candidate alignment positions, the similarity value can be determined based on the average pixel value of each pixel in a first similarity map corresponding to the given alignment position. In some embodiments, the plurality of similarity values ​​can correspond one-to-one with the plurality of first candidate alignment positions. For example, the electronic device 110 can determine the similarity value between the first pattern and the second pattern for each of the plurality of first candidate alignment positions, thereby obtaining the similarity value corresponding to that candidate alignment position. In this way, the electronic device 110 can reuse a first similarity map between the first pattern and the second pattern, and introduce more information such as the similarity value based on the first similarity map to select a target alignment position from the plurality of first candidate alignment positions.

[0064] The overall process of aligning the first and second patterns based on a similarity map is described below. As an example, for each of a plurality of candidate alignment positions, the electronic device 110 can align the first and second patterns based on that candidate alignment position. Based on this, the electronic device 110 can determine a first similarity map and similarity value (e.g., the average pixel value of each pixel in the first similarity map) for the first and second patterns at that candidate alignment position. As an example, the electronic device 110 can calculate a similarity index based on a window of a certain size. The similarity index can indicate the degree of similarity between the first and second patterns at each pixel. The calculation process of the similarity index can use the scaling factor and adjustment factor mentioned above. The electronic device 110 can generate a first similarity map by converting the similarity index into the pixel value of each pixel in the first similarity map. Based on this, the electronic device 110 can statistically determine the proportion of pixels in the first similarity map with a similarity below a threshold (e.g., 0.5 or any other appropriate value) to the total number of pixels, thereby obtaining a reference pixel proportion R corresponding to the first similarity map. Using the above method, multiple first similarity maps corresponding to multiple candidate alignment positions and the reference pixel ratio R corresponding to each first similarity map can be determined.

[0065] If the reference pixel ratio R corresponding to all first similarity maps is less than a threshold ratio (e.g., 0.5 or any other suitable value), the electronic device 110 can determine that there are few low-similarity regions in these first similarity maps, indicating that both the first and second patterns exhibit global periodicity at both the macroscopic and microscopic scales. In this case, the electronic device 110 can randomly select a target alignment position from multiple candidate alignment positions. In some embodiments, the electronic device 110 can select a candidate alignment position that enables the first pattern to achieve center alignment in the second pattern as the target alignment position. If the reference pixel ratio R corresponding to at least some of the first similarity maps is greater than a threshold ratio, the electronic device 110 can determine that there are many low-similarity regions in these first similarity maps, indicating that both the first and second patterns exhibit aperiodicity or local periodicity at the microscopic scale. In this case, the electronic device 110 can select a target alignment position from multiple candidate alignment positions based on similarity indices and similarity values.

[0066] In some embodiments, if the periodicity type of the first pattern is a global periodicity type and the periodicity type of the second pattern is a local periodicity type, the electronic device 110 can determine whether the second pattern includes a non-periodic sub-patterns. A non-periodic sub-pattern can refer to a sub-pattern that is not repeatedly distributed in a regular manner within the second pattern. If the second pattern includes a non-periodic sub-patterns, the electronic device 110 can remove the non-periodic sub-patterns from the second pattern (this can be achieved by masking or cropping the second image, etc.) to obtain a third pattern. The third pattern can be considered a pattern with global periodicity; therefore, the electronic device 110 can align the first and third patterns in a manner similar to the previous method of aligning the first and second patterns based on a similarity map. For example, the electronic device 110 can obtain multiple second candidate alignment positions for the first and third patterns. The electronic device 110 can select a target alignment position from the multiple second candidate alignment positions, at least based on multiple second similarity maps of the first and third patterns corresponding to the multiple second candidate alignment positions. The electronic device 110 can align the first and third patterns based on the target alignment position. The details of the alignment process described above (such as similarity map generation and / or candidate position selection) can be found in the preceding descriptions and will not be repeated here. In this way, when faced with pattern pairs that are periodically inconsistent (one globally, one locally), the electronic device 110 identifies and eliminates non-periodic sub-patterns to construct an intermediate representation (i.e., a third pattern), thereby significantly improving the accuracy of cross-pattern alignment. It should be noted that since the second pattern includes pattern 302 corresponding to the first pattern, the case where the first pattern includes non-periodic sub-patterns while the second pattern does not can be excluded.

[0067] In some embodiments, if both the first pattern and the second pattern are of the local periodic type, the electronic device 110 can determine whether both the first pattern and the second pattern include aperiodic sub-patterns. If both the first pattern and the second pattern include aperiodic sub-patterns, the electronic device 110 can align the first pattern and the second pattern by aligning the periodic sub-patterns in the second pattern to the periodic sub-patterns in the first pattern, and by aligning the aperiodic sub-patterns in the second pattern to the aperiodic sub-patterns in the first pattern. In this way, when both the first pattern and the second pattern include aperiodic sub-patterns, the electronic device 110 can eliminate the relatively complex processing described above, thereby achieving rapid alignment of the first pattern and the second pattern.

[0068] As an example, suppose the second pattern includes multiple sub-patterns A1, A2, A3, and A4, and the first pattern includes multiple sub-patterns B1, B2, and B3, where sub-patterns A4 and B3 are aperiodic sub-patterns. In this case, the electronic device 110 can align sub-pattern A2 to sub-pattern B1, sub-pattern A3 to sub-pattern B2, and sub-pattern A4 to sub-pattern B3, and so on.

[0069] As can be clearly understood from the various embodiments described above, the embodiments of this disclosure provide an alignment method for periodic patterns applicable to SEM images. The method of the embodiments of this disclosure improves the challenge of aligning various periodic patterns, increasing the alignment accuracy and success rate in semiconductor manufacturing inspection.

[0070] Figure 4 A block diagram is shown of an electronic device 400 in which one or more embodiments of the present disclosure may be implemented. The electronic device 400 may, for example, be used to implement... Figure 1 The electronic device 110 shown. It should be understood that, Figure 4 The electronic device 400 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0071] Reference Figure 4 Electronic device 400 is in the form of a general-purpose electronic device. Components of electronic device 400 may include, but are not limited to, one or more processors 410, memory 420, storage device 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. Processor 410 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 420. In a multiprocessor system, multiple processors execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 400.

[0072] Electronic device 400 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 400, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 420 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 430 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 400.

[0073] Electronic device 400 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 4 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 420 may include computer program product 425 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0074] Communication unit 440 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 400 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 400 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0075] Input device 450 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 460 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 400 can also communicate with one or more external devices (not shown) via communication unit 440 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 400, or with any device that enables electronic device 400 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0076] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0077] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0078] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0079] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0081] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is determined to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A pattern alignment method, comprising: Acquire a first image and a second image related to the wafer, wherein the first image includes a first pattern, the second image includes a second pattern, and the second pattern includes a sub-pattern corresponding to the first pattern; In response to determining that at least a portion of the first pattern is periodic, based on the fundamental periodic units of the first pattern, corresponding periodic types of the first pattern and the second pattern are determined, wherein the periodic type in the corresponding periodic type indicates the distribution range of the fundamental periodic units in the corresponding pattern within that pattern; and The first pattern and the second pattern are aligned based on an alignment strategy that matches the corresponding period type.

2. The method of claim 1, wherein determining the corresponding periodicity type of the first pattern and the second pattern comprises: For a given pattern in the first pattern and the second pattern, In response to determining that the similarity between the basic periodic unit of the first pattern and the given pattern meets the similarity requirement, the periodic type of the given pattern is determined to be a global periodic type; as well as In response to determining that the similarity between the basic periodic unit of the first pattern and the given pattern does not meet the similarity requirement, the periodicity type of the given pattern is determined to be a local periodicity type.

3. The method of claim 2, wherein the given pattern is divided into multiple sub-patterns, and the method further comprises: In response to determining that the similarity between the basic periodic unit of the first pattern and each of the plurality of sub-patterns is greater than a threshold similarity, it is determined that the similarity between the basic periodic unit of the first pattern and the given pattern satisfies the similarity requirement.

4. The method according to claim 3, further comprising: Based on the arrangement direction and size of the basic periodic units in the given pattern, the given pattern is divided into the plurality of sub-patterns.

5. The method according to claim 1, further comprising: Based on the frequency domain feature representation of the first pattern, a first estimate of whether the first pattern has periodicity is determined; Based on the self-similarity map of the first pattern, a second estimate of whether the first pattern is periodic is determined, wherein the self-similarity map at least indicates the similarity between multiple positions in the first pattern. as well as In response to at least one of the first estimate and the second estimate indicating that the first pattern is periodic, it is determined that the first pattern is periodic.

6. The method according to claim 5, wherein the frequency domain feature representation of the first pattern is determined using an algorithm based on Fourier transform, and the self-similarity map of the first pattern is determined using an algorithm based on self-similarity.

7. The method of claim 1, wherein aligning the first pattern and the second pattern comprises: In response to determining that the corresponding periodic types of the first pattern and the second pattern are both global periodic types, a plurality of first candidate alignment positions for the first pattern and the second pattern are obtained; Based on the first pattern and the second pattern, and multiple first similarity maps corresponding to the multiple first candidate alignment positions, a target alignment position is selected from the multiple first candidate alignment positions; as well as Align the first pattern and the second pattern based on the target alignment position.

8. The method of claim 7, wherein selecting a target alignment position from the plurality of first candidate alignment positions comprises: In response to determining that the plurality of first similarity maps meet the similarity map requirements, the target alignment position is randomly selected from the plurality of first candidate alignment positions; as well as In response to determining that at least one of the plurality of first similarity maps does not meet the similarity map requirements, the target alignment position is selected from the plurality of first candidate alignment positions based at least on the plurality of similarity values ​​of the first pattern and the second pattern corresponding to the plurality of first candidate alignment positions.

9. The method of claim 8, wherein the similarity value corresponding to a given alignment position among the plurality of first candidate alignment positions is determined based on the average pixel value of each pixel in a first similarity map corresponding to the given alignment position.

10. The method of claim 8, further comprising: For a given similarity map among the plurality of first similarity maps, determine the pixels in the given similarity map whose pixel values ​​are lower than a threshold pixel value, so as to obtain at least one target pixel; Based on the proportion of the at least one target pixel to the total pixels in the given similarity map, a reference pixel proportion of the given similarity map is determined; as well as In response to the fact that the proportion of reference pixels in each of the plurality of first similarity maps is less than the threshold proportion, it is determined that the plurality of first similarity maps meet the similarity map requirements.

11. The method of claim 8, wherein, for a given alignment position among the plurality of first candidate alignment positions, a first similarity map of the first pattern and the second pattern corresponding to the given alignment position is determined by: Based on structural similarity, a similarity index is determined between the first pattern and the second pattern at the given alignment position; and Based at least on the similarity index, a first similarity map of the first pattern and the second pattern corresponding to the given alignment position is generated.

12. The method of claim 11, wherein generating a first similarity map of the first pattern and the second pattern corresponding to the given alignment position comprises: The similarity index is adjusted based on an adjustment factor, wherein the adjustment factor is used to introduce at least one of the following: Translation invariance related to patterns Scaling invariance related to patterns, and Rotational invariance related to patterns; as well as Based on the adjusted similarity index, a first similarity map of the first pattern and the second pattern corresponding to the given alignment position is generated.

13. The method of claim 11, wherein determining the similarity index between the first pattern and the second pattern at the given alignment position comprises: Based on scaling factors of multiple scales, the first pattern and the second pattern are scaled respectively to obtain multiple first scaled patterns of the first pattern and multiple second scaled patterns of the second pattern. as well as The similarity index is determined based on the structural similarity, at least based on the first pattern, the plurality of first scaled patterns, the second pattern, and the plurality of second scaled patterns.

14. The method of claim 1, wherein aligning the first pattern and the second pattern comprises: In response to determining that the periodicity type of the first pattern is a global periodicity type and the periodicity type of the second pattern is a local periodicity type, it is determined whether the second pattern includes a non-periodic sub-patterns; In response to determining that the second pattern includes the aperiodic sub-pattern, the aperiodic sub-pattern in the second pattern is removed to obtain the third pattern; Obtain multiple second candidate alignment positions for the first pattern and the third pattern; Based at least on the first pattern and the third pattern, a target alignment position is selected from the plurality of second candidate alignment positions corresponding to the plurality of second candidate alignment positions; as well as Align the first pattern and the third pattern based on the target alignment position.

15. The method of claim 1, wherein aligning the first pattern and the second pattern comprises: In response to determining that the periodicity types of both the first pattern and the second pattern are local periodicity types, it is determined whether both the first pattern and the second pattern include aperiodic sub-patterns; and In response to determining that both the first pattern and the second pattern include aperiodic sub-patterns, the first pattern and the second pattern are aligned by aligning the periodic sub-patterns in the second pattern to the periodic sub-patterns in the first pattern, and aligning the aperiodic sub-patterns in the second pattern to the aperiodic sub-patterns in the first pattern.

16. The method of claim 1, wherein the periodic type includes a global periodic type and a local periodic type, the global periodic type indicating that the basic periodic units in the corresponding pattern are distributed throughout the pattern, and the local periodic type indicating that the basic periodic units in the corresponding pattern are distributed locally in the pattern.

17. An electronic device comprising: At least one processor; as well as At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions causing the electronic device to perform the method according to any one of claims 1 to 16 when executed by the at least one processor.

18. A computer-readable storage medium having stored thereon computer-executable instructions that can be executed by a processor to implement the method according to any one of claims 1 to 16.

19. A computer program product comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 16.

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