Image processing method and device, medium and product

Abstract

The invention discloses an image processing method and device, a medium and a product, and relates to the technical field of semiconductor defect detection. The method comprises the steps that area images corresponding to a plurality of feature areas are divided from a scanning image of any crystal grain in a target wafer, the shapes of all patterns in one feature area are the same, and the patterns are arranged to have periodic features; fourier transform and longitudinal integration are carried out on the plurality of area images to obtain a plurality of spectrum profile curves; determining configuration parameters by identifying a target peak in the plurality of spectrum profile curves, the configuration parameters including the position and width of the target peak; and configuring a hardware filter according to the configuration parameters, and performing global filtering processing on the scanned image of the target wafer by using the hardware filter. According to the scheme, the Fourier filtering based on the hardware is guided by the Fourier filtering based on the software, the noise self-adaptive filtering of the periodic pattern area on the surface of the wafer is realized, and the filtering adaptability and the processing efficiency are remarkably improved.

Description

Technical Field

[0001] This application belongs to the technical field of semiconductor defect detection, and particularly relates to an image processing method, device, medium, and product. Background Art

[0002] In the scenario of detecting wafer defects in dark field, due to the scattering of incident light by defects, an interference effect will occur, resulting in speckle noise in the detection image. This speckle noise is particularly obvious in the area of high-density repetitive patterns on the wafer surface, which will significantly reduce the signal-to-noise ratio and affect the accurate identification of defects.

[0003] Currently, existing technical solutions use hardware-based Fourier filtering to filter specific frequency components in the frequency domain to suppress speckle noise and reduce background interference. However, hardware-based Fourier filtering is a global filtering method that can only apply the same filtering strategy to the entire wafer, and there are certain limitations in adaptability and flexibility. There may be problems such as insufficient filtering (such as residual background interference) or over-filtering (such as loss of valuable defect information), thus affecting the defect detection effect.

[0004] Therefore, how to improve the filtering adaptability and accurately suppress speckle noise has become an urgent technical problem to be solved. Summary of the Invention

[0005] Embodiments of this application provide an image processing method, device, medium, and product that can improve the filtering adaptability and accurately suppress speckle noise.

[0006] In the first aspect of the embodiments of this application, an image processing method is provided. The method includes: dividing a scanned image of any die in a target wafer into regional images corresponding to a plurality of feature regions, where the shapes of all patterns in one feature region are the same and the arrangement has periodic characteristics; performing Fourier transform and vertical integration on the plurality of regional images to obtain a plurality of spectral profile curves; determining configuration parameters by identifying target peaks in the plurality of spectral profile curves, where the configuration parameters include the positions and widths of the target peaks; configuring a hardware filter according to the configuration parameters, and using the hardware filter to perform global filtering on the scanned image of the target wafer.

[0007] In a second aspect of the embodiments of the present application, an image processing apparatus is provided. The apparatus includes: a division module configured to divide, from a scanned image of any die in a target wafer, area images corresponding to a plurality of feature regions, where the shapes of all patterns within one area image are the same and the arrangement has periodic characteristics; a virtual Fourier transform module configured to perform Fourier transform and longitudinal integration on the plurality of area images to obtain a plurality of spectral profile curves; a configuration parameter determination module configured to determine configuration parameters by identifying target peaks in the plurality of spectral profile curves; and a hardware Fourier filtering module configured to configure a hardware filter according to the configuration parameters and perform global filtering processing on the scanned image of the target wafer using the hardware filter.

[0008] In a third aspect of the embodiments of the present application, an electronic device is provided. The device includes: a memory and a program or instructions stored on the memory and executable on a processor, where when the program or instructions are executed by the processor, the image processing method provided in any one of the above embodiments of the present application is implemented.

[0009] In a fourth aspect of the embodiments of the present application, a readable storage medium is provided. A program or instructions are stored on the readable storage medium, where when the program or instructions are executed by a processor, the image processing method provided in any one of the above embodiments of the present application is implemented.

[0010] In a fifth aspect of the embodiments of the present application, a computer program product is provided. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to execute the image processing method provided in any one of the above embodiments of the present application.

[0011] In the image processing method provided by the embodiments of the present application, by identifying a plurality of feature regions with periodically arranged patterns in a scanned image of any die in a target wafer, performing Fourier transform calculation on the area image corresponding to each feature region, finding target peaks representing the feature regions in the obtained spectral profile curves, and using the positions and widths of the target peaks as configuration parameters, a hardware filter is configured with these parameters and global filtering processing is performed on the target wafer. This method uses software Fourier filtering to guide hardware Fourier filtering, realizes noise adaptive filtering of periodic pattern regions on the wafer surface, significantly improves the adaptability and processing efficiency of filtering, effectively enhances the noise filtering effect, and thus improves the accuracy and reliability of defect detection. Description of the Drawings

[0012] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0013] Figure 1It is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0014] Figure 2 It is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0015] Figure 3 It is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0016] Figure 4 It is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0017] Figure 5 (a) and (b) show schematic structural diagrams of local positions of crystal grains provided by an embodiment of the present application;

[0018] Figure 6 It shows a schematic example diagram of the process of generating a spectral profile curve provided by an embodiment of the present application;

[0019] Figure 7 It shows a schematic example diagram of the superimposed spectral profile curve provided by an embodiment of the present application;

[0020] Figure 8 It shows a schematic example diagram of determining a target spike provided by an embodiment of the present application;

[0021] Figure 9 It is a schematic structural diagram of an image processing apparatus provided by an embodiment of the present application;

[0022] Figure 10 It is a schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0023] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0024] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0025] It should be noted that in the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain industry-existing solutions such as software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0026] First, the noun terms involved in one or more embodiments of this application are explained.

[0027] A wafer is a silicon wafer processed by a specific processing technology and is the basic material for semiconductor manufacturing.

[0028] A die is a single chip unit separated by cutting on a wafer, and each die contains a complete integrated circuit or functional module.

[0029] Dark field detection is an optical detection technology mainly used to detect minute defects on the surface of a wafer. Its principle is to irradiate the sample with obliquely incident light and collect the light scattered by the defects for detection.

[0030] Speckle noise is a common noise phenomenon in optical imaging, which is generated when coherent light (such as laser) irradiates a rough surface or passes through a scattering medium. It appears as randomly distributed bright and dark spots in the image, which will reduce the resolution and contrast of the image and cause interference to detection and analysis.

[0031] Hardware-based Fourier filtering is a technology that directly performs Fourier transform and filtering at the physical level using an optical system. Its basic principle is to perform Fourier transform on the incident light through a lens, place a spatial filter (such as an occlusion sheet, diffraction grating or spatial light modulator) in the Fourier plane (i.e., the frequency domain) to shield or enhance specific frequency components, and then restore the imaging through inverse Fourier transform.

[0032] The Mask in Fourier filtering is used to select or suppress specific frequency components in the frequency domain.

[0033] Software-based Fourier filtering is a technique that implements Fourier transform and filtering through algorithms in the digital domain. Its basic principle is to use a computer to perform a fast Fourier transform on a signal or image, filter out specific frequency components in the frequency domain, and then restore the signal or image through an inverse transform.

[0034] When using dark field detection technology to detect defects on a wafer, a laser or a highly coherent light source is usually used. When light irradiates the wafer surface, the defects on it will cause the incident light to scatter, and defect detection can be achieved by collecting the scattered light. However, in areas with a high density of repetitive patterns on the wafer surface, due to the regular and periodic surface structure, it is easier to form a stable interference pattern between the scattered light fields in different regions, making the speckle noise more obvious, resulting in severe intensity fluctuations in the background signal, reducing the signal-to-noise ratio, and affecting the accuracy of subsequent defect recognition.

[0035] Currently, there are means to use hardware-based Fourier filtering technology to reduce noise interference. For example, a hardware Fourier filter is placed in the optical path system to shield noise components of specific frequencies. Since this method is based on physical optical filtering and does not require computer operations, it can achieve real-time processing and has a high processing speed.

[0036] However, once the hardware Fourier filtering is designed, its filtering strategy is fixed and can only perform global filtering on the entire wafer image. It cannot be adaptively adjusted according to the pattern characteristics of different regions, and there are certain limitations in terms of adaptability and flexibility, which may lead to insufficient filtering (such as residual background interference) or over-filtering (such as loss of valuable defect information), thus affecting the defect detection effect.

[0037] To address the above technical problems, this application provides an image processing method, device, medium, and product. In the image processing method provided by the embodiments of this application, software Fourier filtering is used to guide hardware Fourier filtering to achieve adaptive noise filtering in the areas of patterns periodically arranged on the wafer surface. It can not only dynamically set the filtering strategy according to the pattern characteristics to improve adaptability, but also maintain a high processing efficiency, significantly enhance the noise filtering effect, and thus improve the accuracy and reliability of defect detection.

[0038] For example, the image processing method provided in the embodiments of the present application can be applied to the production line of a semiconductor manufacturing enterprise for defect detection of semiconductor devices generated during the production process. In practical applications, first, a wafer defect image is obtained, and based on the image of any typical grain region in the wafer, the spectral characteristics of the image are analyzed using software Fourier transform to determine the relevant parameters corresponding to the filtering strategy. Subsequently, based on these parameters, hardware-based Fourier filtering is performed to remove noise from the wafer defect image, obtaining a high-quality image with noise suppression. Finally, based on this high-quality image, further processing such as defect identification, classification, and quantification is performed to improve the accuracy and reliability of defect detection and provide support for production quality control.

[0039] It should be noted that the application scenarios described in the above embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those of ordinary skill in the art know that with the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems. The image processing method provided by the embodiments of the present application can be applied to various application scenarios that require noise removal from wafer images.

[0040] The following introduces the image processing method provided by the embodiments of the present application. In practical applications, the execution subject of the image processing method of the embodiments of the present application can be an electronic device.

[0041] Figure 1 The flowchart of the image processing method provided by an embodiment of the present application is shown. As Figure 1 shown, the method includes steps S110 to S140.

[0042] S110. From the scanned image of any grain in the target wafer, divide out the regional images corresponding to multiple feature regions, and the shapes of all the patterns within one regional image are the same and the arrangement has periodic characteristics.

[0043] Target wafer: The wafer to be subjected to defect detection.

[0044] It should be understood that a wafer includes multiple repeatedly appearing grains, and these grains are consistent in structure and pattern.

[0045] Scanned image of the grain: The original image of the grain obtained by a scanning device.

[0046] In one embodiment, before performing step S110, a pre-scanning operation can be first performed, that is, using a scanning device to perform a complete scan on any grain in the target wafer to obtain its overall image.

[0047] Exemplarily, the scanning device can be an imaging device such as an optical microscope or an electron microscope.

[0048] In one implementation, a scanning device can be used to take multiple local images of the die at a high magnification, and the multiple local images can be combined into a complete scanned image of the die through image stitching technology, so as to ensure the integrity and clarity of image details and improve the accuracy of overall imaging.

[0049] It can be understood that since the dies on the wafer have a high degree of repeatability, only a filtering strategy needs to be designed for one die, and then this strategy can be applied to all dies, thereby realizing efficient processing of the entire wafer.

[0050] For the convenience of understanding and description, the die used for filtering strategy design will be referred to as the target die in the following text. However, it should be understood that this naming is only for simplicity of expression and does not limit the selection of specific dies.

[0051] In one implementation, multiple feature regions in the scanned image of the target die can be identified first according to the periodic pattern shape and arrangement rules, and then multiple region images corresponding to the multiple feature regions can be divided from the scanned image of the die.

[0052] A feature region is a region in the die where the pattern shapes are the same and the arrangement has periodic characteristics. That is to say, all the patterns within one region image have the same shape and the arrangement has periodic characteristics.

[0053] "The same shape" means that all the patterns within the feature region are basically the same in geometric appearance, including boundary contours, size ratios, etc.

[0054] "The arrangement has periodic characteristics" means that the patterns of the same shape are arranged repeatedly in space according to certain rules, such as equidistant arrangement, matrix arrangement, or periodic distribution in a specific direction (horizontal or vertical). This arrangement makes the spatial distribution pattern of the patterns within this region predictable and repeated at different positions.

[0055] It can be understood that the high-density repetitive pattern region mentioned in the background art part can be regarded as an example of the feature region in the embodiments of the present application.

[0056] For example, Figure 5 shows a schematic structural diagram of a local position of a die provided by an embodiment of the present application.

[0057] Figure 5 It includes two local positions (a) and (b) of the die.

[0058] From Figure 5 it can be seen from (a) that, taking the dividing line X as the boundary, it is divided into two regions:

[0059] Region 1 contains multiple identical long rectangular line patterns, which are arranged horizontally, with an overall layout of 9×1, that is, there are 9 in the horizontal direction and 1 in the vertical direction;

[0060] Region 2 contains multiple identical short rectangular line patterns, which are arranged in a 4×4 manner, that is, there are 4 in both the horizontal and vertical directions.

[0061] From Figure 5 (b), it can be seen that, bounded by the dividing line Y, it is divided into two regions:

[0062] Region 3 contains multiple identical short rectangular line patterns, which are arranged in a 4×12 manner, that is, there are 4 in the horizontal direction and 12 in the vertical direction;

[0063] Region 4 contains multiple identical long rectangular line patterns, which are arranged in a 9×1 manner, that is, there are 9 in the horizontal direction and 1 in the vertical direction.

[0064] As can be seen from the figure, the pattern shapes in Regions 1, 2, 3, and 4 are the same and the arrangement patterns have periodic characteristics, and the specific characteristics such as the pattern shapes, sizes, quantities, and spacings between them are not exactly the same. Therefore, Regions 1, 2, 3, and 4 can all be regarded as characteristic regions.

[0065] Region image, a scanned image corresponding to a specific characteristic region divided from the grain scanned image.

[0066] In one embodiment, the obtaining method of the region image may include the following steps:

[0067] S111. In the scanned image of the target grain, according to the pattern shape and arrangement periodic characteristics, identify multiple characteristic regions and determine their boundaries;

[0068] S112. Encode and identify the identified characteristic regions;

[0069] S113. Based on the encoding identification, divide the scanned image of the target grain according to the characteristic regions to generate multiple corresponding region images, and each region image only contains the scanned information of a specific characteristic region.

[0070] In one implementation, step S111 can be implemented using a pre-trained visual recognition algorithm, that is, using the visual recognition algorithm to automatically identify the shapes and arrangement rules of all patterns in the scanned image of the target grain, and accordingly identify multiple characteristic regions. This method is applicable to large-scale automated processing scenarios.

[0071] In one implementation, step S111 can be implemented based on a projection analysis method, that is, the scanned image of the target grain is projected in a fixed projection direction (such as horizontally or vertically), and by analyzing the shape characteristics of the projection result, the characteristic regions with periodic arrangement characteristics are identified. This method is applicable to scenarios where the pattern arrangement rules are obvious.

[0072] In one implementation, step S111 can be implemented by manual annotation or using a semi-automatic auxiliary tool, that is, through manual annotation or a semi-automatic auxiliary tool, interactive operations are performed on the scanned image of the target grain, and multiple characteristic regions are identified and divided. This method is applicable to the high-precision processing requirements for specific grain structures or as a supplementary verification means for automatic division.

[0073] S120. Perform Fourier transform and longitudinal integration on multiple regional images to obtain multiple spectral profile curves.

[0074] In one implementation, step S120 may include steps S121 - S122.

[0075] S121. Perform Fourier transform on multiple regional images to obtain multiple spectral images.

[0076] It can be understood that the regional images and the spectral images are in one-to-one correspondence. That is, for each of the multiple regional images, Fourier transform is performed on the regional image to obtain the Fourier spectral image corresponding to the regional image. That is to say, for the multiple regional images divided from the grain image, Fourier transform can be performed one by one, the frequency domain information of each regional image can be extracted, and the corresponding Fourier spectral image can be obtained.

[0077] The spectral image, a two-dimensional matrix, where each element in the matrix represents the amplitude of the corresponding frequency component.

[0078] Fourier transform is used to convert the image in the spatial domain into a frequency domain representation, where different frequency components correspond to different scale characteristics of the pattern.

[0079] Since the patterns in the regional images have periodic arrangements, specific frequency components in their corresponding spectral images will exhibit obvious periodic peaks.

[0080] In one implementation, the Fast Fourier Transform (FFT) algorithm can be used to improve the calculation efficiency.

[0081] Exemplarily, before performing the Fourier transform, the regional images can be normalized to reduce noise interference.

[0082] S122. Perform longitudinal integration on multiple spectral images to obtain multiple spectral profile curves.

[0083] It is understandable that the spectral image and the spectral profile curve correspond one-to-one. That is, for each of the multiple spectral images, the spectral image is integrated longitudinally to obtain the spectral profile curve corresponding to the spectral image.

[0084] Longitudinal integration accumulates the pixel gray values of the spectral image in the vertical direction (Y direction) to generate a curve reflecting the spectral energy distribution.

[0085] The curve obtained by longitudinal integration is the longitudinal integration gray profile curve of the spectral image (abbreviated as the spectral profile curve). The horizontal axis of this curve represents the frequency (or pixel position), the vertical axis represents the cumulative result of the gray values, and the peak position and width reflect the distribution characteristics of the main frequency components in the spectral image.

[0086] It is understandable that the spectral image is obtained by transforming the regional image, and its main frequency components reflect the arrangement characteristics of the patterns in the regional image. Since the patterns in the feature region are arranged periodically, their structure shows a repeating pattern in the spatial domain and corresponds to specific frequency components in the spectral image. Therefore, the main frequency components in the spectral image are actually the manifestation forms of the periodic structure of the patterns in the feature region in the frequency domain.

[0087] S130. Determine the configuration parameters by identifying the target spikes in multiple spectral profile curves.

[0088] The configuration parameters include the position and width of the target spike.

[0089] The target spike, among multiple spectral profile curves, is a frequency point with a significant peak.

[0090] The position of the target spike, the position coordinates corresponding to the peak of the target spike, corresponding to the abscissa of the peak in the spectral profile curve (i.e., the frequency or pixel position).

[0091] The width of the target spike, the horizontal span of the target spike on the spectral profile curve, that is, the frequency range when the values on both sides of the peak drop to a certain threshold.

[0092] In one implementation, all spectral profile curves are analyzed to identify all the spikes in each curve, and all the spikes in multiple spectral profile curves are determined as target spikes. For each target spike, its position and width are obtained as configuration parameters. This method is applicable to scenarios where all frequency components in the spectral image need to be comprehensively analyzed to ensure that each significant frequency feature is captured.

[0093] In one implementation, spikes in each spectral profile curve are identified, and the spikes that meet the preset conditions are determined as target spikes. The preset conditions may include, for example, the amplitude size, width, position range, etc. of the spikes, and are used to filter out the frequency components that are most representative and effective for a specific task. For example, only spikes with an amplitude exceeding a certain threshold and a moderate width will be identified as target spikes, and other spikes that do not meet the conditions will be ignored. This method can reduce the interference of noise or unimportant frequency components on the final configuration parameters and is applicable to scenarios with strict requirements for specific frequency characteristics.

[0094] S140. Configure the hardware filter according to the configuration parameters, and use the hardware filter to perform global filtering on the scanned image of the target wafer.

[0095] It should be noted that in the embodiment of the present application, a dedicated hardware device is provided in the optical path to implement Fourier filtering, so as to efficiently process and convert the frequency components in the optical signal.

[0096] Exemplarily, the dedicated hardware device may include an optical Fourier transform module, an optical processing unit (OPU), or an application-specific integrated circuit (ASIC), etc.

[0097] It can be understood that the above dedicated hardware device includes a hardware filter, and the hardware filter includes a filtering mask (i.e., mask). Exemplarily, the mask may be a specific pattern generated by an optical filter, a spatial light modulator, or a digital micromirror device (DMD), and is used to selectively filter or adjust the optical signals of different frequency components.

[0098] In this step, the role of the configuration parameters is to provide the required setting and control information for the hardware-based Fourier filtering process, so that the hardware can perform global filtering on the scanned image of the target wafer.

[0099] In order to apply the configuration parameters to the hardware-based Fourier filtering, the configuration parameters can be passed to the hardware device through the user interface or the configuration menu of the hardware control system.

[0100] Exemplarily, the configuration parameters can be manually input by the user or automatically input by an automated system and saved to the device memory to ensure that the hardware can perform subsequent operations according to the configuration parameters.

[0101] The hardware device reads the configuration parameters in the configuration menu and performs Fourier filtering operations based on the configuration parameters. Specifically, the scanned image is transformed from the spatial domain to the frequency domain, and a filtering operation is applied to suppress specific frequency components. During the filtering process, the parameters of the filter are adjusted according to the configuration parameters, such as setting the frequency bandwidth, filtering intensity, etc.

[0102] After the Fourier transform is completed, the filtering process will act on the entire scanned image to remove unnecessary noise, highlight specific frequency components, or enhance the periodic features in the image. This improves the quality of the scanned image of the target wafer, facilitating subsequent defect detection.

[0103] The image processing method proposed in the embodiments of this application identifies multiple feature regions with periodically arranged patterns in the scanned image of any die in the target wafer, calculates the Fourier transform of the regional image corresponding to each feature region, finds the target spikes representing the feature regions in the obtained spectral profile curve, and uses the position and width of the target spikes as configuration parameters to configure the hardware filter and perform global filtering on the target wafer. This method uses software Fourier filtering to guide hardware Fourier filtering, realizes the noise adaptive filtering of the periodic pattern regions on the wafer surface, significantly improves the adaptability and processing efficiency of filtering, effectively enhances the noise filtering effect, and thus improves the accuracy and reliability of defect detection.

[0104] It should be noted that the method of this application fully combines the advantages of hardware Fourier filtering and software Fourier filtering and solves their respective limitations: Although hardware Fourier filtering has a relatively fast processing speed, it is limited by a fixed filtering strategy, with poor adaptability and flexibility and unable to dynamically adjust according to different image features; while software Fourier filtering has strong flexibility and can adjust the filtering strategy according to different requirements, but its calculation is slow and it is difficult to meet the requirements of real-time analysis.

[0105] To make up for this deficiency, this method uses the configuration parameters obtained by software Fourier filtering to characterize the filtering frequency to guide the adjustment of the position of the mask in the hardware Fourier filter so that it can be directly applied to the hardware filtering process. In this way, software calculation provides the necessary flexibility and adaptability, while the hardware accelerator accelerates the execution of frequency domain filtering through logic circuits. It should be emphasized that compared with the scheme of using virtual filters in software Fourier filtering, this scheme directly completes frequency domain filtering in the optical path using a hardware filter, avoiding the dependence on external CPUs / GPUs and having the advantages of high real-time performance, fast calculation efficiency, and strong anti-noise ability. In this way, the entire filtering process can not only flexibly adjust the filtering strategy according to the changes in the feature regions, but also efficiently apply the adjusted strategy to the filtering of the entire wafer image, achieving the combination of high efficiency and flexibility.

[0106] Figure 2 The figure shows a flowchart example of an image processing method provided by an embodiment of the present application.

[0107] It should be understood that Figure 2 The illustrated embodiment can be regarded as an example of step S140.

[0108] As Figure 2 shown, the method may include the following steps.

[0109] S210. Configure the position and coverage range of the filtering mask in the hardware filter according to the configuration parameters.

[0110] Configure the filtering mask in the hardware filter according to the position and width of the target spike in the configuration parameters.

[0111] Specifically, the position of the target spike is used to determine the position of the filtering mask in the spectral image, while the width of the target spike is used to set the coverage range of the filtering mask.

[0112] Through configuration, the filtering mask can accurately cover the area corresponding to the target spike in the spectral image, so as to ensure that the filter can suppress the interfering frequency components at the correct position and within an appropriate range.

[0113] S220. Perform Fourier transform to convert the scanned image of the target wafer to the frequency domain to obtain the spectral image of the target wafer.

[0114] S230. In the frequency domain, apply the configured filtering mask to suppress the interfering frequency components in the spectral image to obtain an optimized spectral image.

[0115] Specifically, the mask will apply filtering to the part of the spectral image that coincides with the interfering frequency components to suppress these unwanted frequency components.

[0116] It can be understood that the interfering frequency components in this step are actually the frequency components of the periodic pattern.

[0117] S240. Perform Fourier transform to restore the optimized spectral image to the spatial domain to obtain the filtered target image.

[0118] As can be seen from the above, step S210 is a configuration process, and steps S220 - S240 are application processes. The application process can be summarized as: using the configured filtering mask to perform global filtering on the scanned image of the target wafer to obtain the filtered target image.

[0119] For example, Figure 6 The figure shows a schematic example of the spectral profile curve generation process provided by an embodiment of the present application.

[0120] In this example, the feature region a and the feature region b respectively correspond to different regional images a and b.

[0121] The regional image a corresponding to the feature region a is subjected to Fourier transform to obtain a spectral image a.

[0122] Through Fourier transform, an image can be transformed from the spatial domain to the frequency domain, and the information of each frequency component in the image can be extracted.

[0123] The spectral image a is subjected to longitudinal integration to obtain a spectral profile curve a.

[0124] The longitudinal integration process sums each column in the spectral image to extract the frequency distribution characteristics of the image in a specific direction and form a spectral profile curve.

[0125] Similarly, the regional image b corresponding to the feature region b is subjected to Fourier transform to obtain a spectral image b; the spectral image b is subjected to longitudinal integration to obtain a spectral profile curve b.

[0126] Among them, the horizontal axis of the spectral profile curves a and b represents frequency, and the vertical axis represents the cumulative result of the gray value.

[0127] Through the above steps, the regional image can be converted into a spectral profile curve, which further provides basic data for subsequent filtering processing.

[0128] In the solution of this embodiment, the filtering mask in the hardware filter is configured according to the configuration parameters, the position and coverage range of the mask are determined, and the configured filtering mask is used to apply global filtering processing to the scanned image of the target wafer, and finally the filtered target image is obtained. This method uses the aforementioned obtained configuration parameters to perform global filtering processing on the scanned image of the entire target wafer at the hardware level, accurately filtering out the noise in the periodic pattern area, and significantly improving the adaptability and processing efficiency of filtering.

[0129] Specifically, after performing Fourier transform on the scanned image of the target wafer to obtain a spectral image, the configured filtering mask is applied to suppress the interfering frequency components, optimize the spectral image, and then the optimized spectral image is restored to the spatial domain through inverse Fourier transform, thereby obtaining the target image. This process can accurately filter out the interfering frequency components corresponding to multiple feature regions, achieve effective noise suppression, and at the same time retain the important features in the image, significantly improving the image quality and the accuracy of defect detection.

[0130] Figure 3 The flowchart shows a schematic flow of an image processing method provided by an embodiment of the present application.

[0131] It should be understood that Figure 3 The illustrated embodiment can be regarded as Figure 1An example of step S130 in the embodiment.

[0132] As Figure 3 shown, the method may include the following steps.

[0133] S310. Superimpose multiple spectral profile curves to obtain a comprehensive spectral profile curve.

[0134] Add the gray values (intensity values) at the same frequency positions in each spectral profile curve to obtain a new spectral profile curve.

[0135] Exemplarily, during the intensity superposition process, weighted average (if different spectral curves have different importance) or simple addition (each curve is equally important) can be used to synthesize the final comprehensive spectral profile curve.

[0136] Exemplarily, before performing the intensity superposition, each curve can be normalized so that the intensity ranges of different curves are consistent, thereby avoiding the excessive influence of certain curves on the result.

[0137] Figure 7 Shows a schematic example diagram of superimposing spectral profile curves provided by an embodiment of the present application.

[0138] Following the previous example, add the gray values (intensity values) at the same frequency positions in spectral profile curve a and spectral profile curve b to obtain a new comprehensive spectral profile curve c.

[0139] S320. Identify all local spikes in the comprehensive spectral profile curve, and determine the local spikes whose peak values meet the preset requirements as target spikes.

[0140] A local spike represents a significant frequency component in the spectral image, whose shape is higher than adjacent points and forms a local peak.

[0141] In one implementation, the process of identifying local spikes includes:

[0142] On the comprehensive spectral profile curve, find all local maxima higher than adjacent points, and these local extreme values are spikes. Exemplarily, a peak detection algorithm (such as the difference method or the sliding window method) can be used to determine the position of each local spike.

[0143] After identifying all local spikes, filter out the target spikes through preset conditions. For example, set conditions such as a minimum peak amplitude, maximum width, or frequency range, and only retain those local spikes that meet these requirements as target spikes. This filtering helps to remove noise or irrelevant frequency components and only retain the frequency features valuable for subsequent processing.

[0144] Figure 8Shows a schematic example diagram of determining a target peak provided by an embodiment of the present application.

[0145] Continuing with the previous example, peaks 1-6 are initially identified in the comprehensive spectral profile curve c. In this example, the preset requirement for determining the target peak is that the peak amplitude is not less than 20,000. On this basis, peaks 1-6 are respectively judged, and it is determined that peaks 1-6 are all target peaks.

[0146] Exemplarily, before identifying all local peaks in the comprehensive spectral profile curve, the comprehensive spectral profile curve can be smoothed based on a preset window size.

[0147] The smoothing process can be implemented using a sliding window.

[0148] The preset window size determines the neighborhood range considered each time for smoothing. It should be understood that the preset window size can be set according to the frequency distribution and noise characteristics, and the present application does not limit this.

[0149] Exemplarily, a smoothing algorithm (such as Gaussian smoothing) can be used to perform weighted average processing on the spectral data within a sliding window to generate smoothed data points.

[0150] The sliding window slides on the comprehensive spectral profile curve, gradually smoothing each part of the curve until the processing is completed.

[0151] Smoothing the comprehensive spectral profile curve before identifying local peaks effectively reduces noise interference and makes the spectral features clearer and more prominent.

[0152] S330. Obtain the position and width of the target peak and determine them as configuration parameters.

[0153] This step has been introduced in step S130 and will not be elaborated here.

[0154] The method of this embodiment, through intensity superposition of multiple spectral profile curves, the obtained comprehensive spectral profile curve can present the overall characteristics of the frequency components in all spectral profile curves, enhancing the recognition ability of spectral features; in addition, by setting screening conditions to select peaks that meet the preset requirements as target peaks, the accuracy and effectiveness of spectral features are further improved. Through the above steps, the identification of local peaks is more accurate, avoiding misidentification and noise interference, and at the same time making the final position and width of the target peak more precise, thereby improving the reliability and accuracy of the extraction of configuration parameters.

[0155] In one embodiment, after performing global filtering processing on the scanned image of the target wafer, an image quality evaluation mechanism can also be introduced to ensure the effectiveness of the filtering operation. Specifically, the method of the present application can further include:

[0156] At each preset sampling period, a local scanned image of the target wafer after filtering is extracted and evaluated to obtain an image quality evaluation value.

[0157] Exemplarily, the image quality evaluation value can be obtained by analyzing multiple visual features of the local scanned image, including brightness, contrast, saturation, and orthogonality of patterns, etc.

[0158] According to the comparison result between the image quality evaluation value and the preset image quality index, it is judged whether the filtering is completed.

[0159] Correspondingly, the preset image quality index can be a comprehensive index set in advance by combining the brightness, contrast, saturation of the image, the orthogonality of patterns in the image, etc.

[0160] Exemplarily, the preset image quality index can be transmitted to the hardware device through configuration parameters as a control basis.

[0161] When the image quality evaluation value is less than the preset image quality index, it indicates that the image quality has not reached the expected standard, and the step of obtaining configuration parameters will be executed again, and the filtering operation will be performed again according to the new configuration parameters.

[0162] It should be understood that this process includes re-measuring the selection or width of the target spike in the spectral profile curve, analyzing the position from the left end to the right end of the target spike, and adjusting the position of the filtering mask according to this change to further guide the hardware to perform the filtering operation.

[0163] When the image quality evaluation value is greater than or equal to the preset image quality index, it indicates that the image quality already meets the requirements, and the configuration parameters remain unchanged, and the filtering operation continues to be performed.

[0164] In the solution of this embodiment, after performing Fourier filtering based on hardware, a local scanned image is extracted and evaluated at each preset sampling period to obtain an image quality evaluation value, and compared with the preset image quality index, which can dynamically monitor the change of image quality, automatically judge whether the filtering is completed, thereby improving the adaptability and accuracy of processing. When the image quality requirements are not met, the spike characteristics in the spectral profile curve are re-analyzed, the configuration parameters are adjusted, and the hardware filter is guided to perform the filtering operation again, thereby ensuring the continuous optimization of the image processing quality to meet the expected image quality standard and ensuring that the final target image meets the quality requirements.

[0165] Figure 4 The flowchart shows a flowchart of an image processing method provided by an embodiment of the present application.

[0166] It should be understood, Figure 4An embodiment can be regarded as an example introducing the complete process of the method of the present application.

[0167] As Figure 4 shown, the method may include the following steps.

[0168] S410. Start.

[0169] S420. Pre-acquire a complete image of a single die in the target wafer.

[0170] S430. In the complete image of the die, divide all feature regions and encode each feature region.

[0171] S440. Perform Fourier transform on the region image corresponding to each encoded feature region to obtain a spectrum image.

[0172] S450. Perform longitudinal integration on the spectrum image to construct a spectrum profile curve.

[0173] S460. Integrate all spectrum profile curves, identify multiple target peaks, and use the positions and widths of the multiple target peaks, as well as the preset image quality index Q as configuration parameters.

[0174] S470. Write the above configuration parameters into the configuration menu of the hardware filter.

[0175] S480. Start full-wafer scanning and filtering processing.

[0176] S490. Sample and inspect the filtered image and calculate the image quality evaluation value Q1.

[0177] S500. Determine whether Q1≥Q is satisfied.

[0178] If yes, execute S600. Continue the image detection process; if not, return to execute S440 until Q1≥Q.

[0179] Based on the image processing method. Correspondingly, the present application also provides a specific embodiment of an image processing device.

[0180] As Figure 9 shown, the image processing device 1000 provided by the embodiment of the present application includes the following modules.

[0181] A division module 1001, configured to divide region images corresponding to multiple feature regions from the scanned image of any die in the target wafer.

[0182] The shapes of all patterns in one region image are the same and the arrangement has periodic characteristics.

[0183] The virtual Fourier transform module 1002 is used to perform Fourier transform on multiple regional images and integrate them longitudinally to obtain multiple spectral profile curves.

[0184] The configuration parameter determination module 1003 is used to determine the configuration parameters by identifying the target spikes in multiple spectral profile curves.

[0185] The hardware Fourier filtering module 1004 is used to configure the hardware filter according to the configuration parameters, and use the hardware filter to perform global filtering on the scanned image of the target wafer.

[0186] Based on the image processing method. Accordingly, the present application also provides a specific embodiment of the electronic device.

[0187] Figure 10 The figure shows a schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present application.

[0188] The electronic device may include a processor 7001 and a memory 7002 storing computer program instructions.

[0189] Specifically, the above-mentioned processor 7001 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0190] The memory 7002 may include a mass storage for data or instructions. By way of example and not limitation, the memory 7002 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 7002 may include removable or non-removable (or fixed) media. In a suitable case, the memory 7002 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 7002 is a non-volatile solid state memory.

[0191] The processor 7001 reads and executes the computer program instructions stored in the memory 7002 to implement any one of the image processing methods in the above embodiments.

[0192] In one example, the electronic device may further include a communication interface 7003 and a bus 7004. Among them, as Figure 5 shown, the processor 7001, the memory 7002, and the communication interface 7003 are connected through the bus 7004 and complete communication with each other.

[0193] The communication interface 7003 is mainly used to implement the communication between various modules, devices, units, and / or devices in the embodiments of the present application.

[0194] The bus 7004 includes hardware, software, or both, and couples the components of the electronic device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. In suitable cases, the bus 7004 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0195] In addition, in combination with the image processing method in the above embodiments, the embodiments of the present application can be implemented by providing a computer storage medium. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the image processing methods in the above embodiments is implemented.

[0196] In addition, in combination with the image processing method in the above embodiments, the embodiments of the present application can be implemented by providing a computer program product. When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device is caused to execute the image processing method provided in any aspect of the above embodiments of the present application.

[0197] It should be clear that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between the steps after understanding the spirit of the present application.

[0198] The functional blocks shown in the above-described structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, and so on. When implemented in software, the elements of the present application are programs or code segments for performing the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted via a data signal carried in a carrier wave over a transmission medium or a communication link. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0199] It should also be noted that in the exemplary embodiments mentioned in the present application, some methods or systems are described based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0200] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and the combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware for performing the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0201] As described above, this is only the specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. An image processing method, characterized in that, Including: From the scanned image of any die in the target wafer, divide the regional images corresponding to multiple feature regions, where the shapes of all patterns within one feature region are the same and the arrangement has periodic characteristics; Perform Fourier transform on the multiple regional images and integrate them longitudinally to obtain multiple spectral profile curves; Determine the configuration parameters by identifying the target spikes in the multiple spectral profile curves, where the configuration parameters include the positions and widths of the target spikes; Configure a hardware filter according to the configuration parameters, and use the hardware filter to perform global filtering on the scanned image of the target wafer.

2. The method according to claim 1, wherein The step of configuring a hardware filter according to the configuration parameters and using the hardware filter to perform global filtering on the scanned image of the target wafer includes: According to the configuration parameters, configure the positions and coverage ranges of the filtering masks in the hardware filter; Use the configured filtering masks to perform global filtering on the scanned image of the target wafer to obtain a filtered target image.

3. The method according to claim 2, wherein The step of using the configured filtering masks to perform global filtering on the scanned image of the target wafer to obtain a filtered target image includes: Perform Fourier transform to convert the scanned image of the target wafer to the frequency domain to obtain the spectral image of the target wafer; In the frequency domain, apply the configured filtering masks to suppress the interference frequency components in the spectral image to obtain an optimized spectral image; Perform inverse Fourier transform to restore the optimized spectral image to the spatial domain to obtain the target image.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the configuration parameters by identifying the target spikes in the multiple spectral profile curves, where the configuration parameters include the positions and widths of the target spikes, includes: Overlay the multiple spectral profile curves to obtain a comprehensive spectral profile curve; Identify all local spikes in the comprehensive spectral profile curve, and determine the local spikes whose peak values meet the preset requirements as the target spikes; Obtain the positions and widths of the target spikes and determine them as the configuration parameters.

5. The method according to claim 4, characterized in that, Before identifying all local spikes in the comprehensive spectral profile curve, the method further includes: Based on a preset window size, smooth the comprehensive spectral profile curve.

6. The method according to any one of claims 1 to 3, characterized in that, The configuration parameters further include a preset image quality index; After configuring a hardware filter according to the configuration parameters and using the hardware filter to perform global filtering on the scanned image of the target wafer, the method further includes: At every preset sampling period, extract and evaluate the local scanned image of the filtered target wafer to obtain an image quality evaluation value; Judge whether the filtering is completed according to the comparison result between the image quality evaluation value and the preset image quality index.

7. The method according to claim 6, characterized in that, The step of judging whether the filtering is completed according to the comparison result between the image quality evaluation value and the preset image quality index includes: In the case where the image quality evaluation value is less than the preset image quality index, re-execute the steps of obtaining the configuration parameters, configuring the hardware filter according to the configuration parameters, and using the hardware filter to perform global filtering on the scanned image of the target wafer; When the image quality evaluation value is greater than or equal to the preset image quality index, continue to perform the filtering operation.

8. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the image processing method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, they implement the image processing method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to execute the image processing method according to any one of claims 1-7.

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