Repetitive defect detection method, device and system and readable storage medium

Abstract

The invention discloses a repetitive defect detection method, device and system and a readable storage medium. The method comprises the following steps: acquiring first position information of a defect on the surface of a machined part by taking the machined part as a position reference; taking one of the plurality of processed areas on the surface of the processed workpiece as a reference area, and determining second position information of the defect on the surface of the processed workpiece by taking the reference area as a position reference; determining a defect density peak point taking the reference area as a position reference according to the second position information of the defects on all the processed areas; and if the number of the defects in the preset range of a certain defect density peak point is greater than or equal to a repetitive defect threshold value, judging that the processed workpiece has repetitive defects. As the repetitive defects are densely distributed in a certain area when mapped into the reference area, the repetitive defects are detected through the defect density, and the repetitive defects in the machined workpiece can be accurately and effectively detected.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular to a method, device, system and readable storage medium for detecting repetitive defects. Background Art

[0002] With the rapid development of semiconductor technology, the complexity and sophistication of semiconductor manufacturing processes continue to rise. Due to the diversity of production equipment and processes, unpredictable defects may appear on the surface of processed semiconductor workpieces. In order to ensure the smooth progress of the semiconductor manufacturing process, monitoring stations are usually set up after key process steps to detect whether there are defects on semiconductor workpieces, so as to timely discover and solve potential problems and ensure the high quality and reliability of products.

[0003] In some processing processes of semiconductor manufacturing, it is necessary to use a processing part to allow specific light to pass through the processing part to process on the surface of the semiconductor workpiece. For example, in the photolithography process, ultraviolet light is used to expose on the wafer through the mask to copy the circuit pattern on the mask to the wafer. If there are tiny dust particles on the mask, these dust particles will block part of the light, making it impossible to expose on the wafer, thereby causing photolithography defects on the wafer. Since the exposure process is carried out in a repetitive and stepping manner, that is, after the ultraviolet light is exposed on an area on the wafer through the mask, the wafer moves a certain distance so that the ultraviolet light is exposed on another area through the mask, so that multiple processed areas are formed on the wafer, so this type of photolithography defect has the characteristic of recurring on the wafer, which is called a repetitive defect. In the production process, repetitive defects have a wide range of influence and high detection difficulty, which has a significant impact on product reliability and yield. Therefore, it is particularly important to detect repetitive defects in the processed semiconductor workpieces. Summary of the invention

[0004] In order to detect repetitive defects, the present application provides a repetitive defect detection method, a detection device, a detection system and a computer-readable storage medium.

[0005] According to a first aspect, an embodiment of the present application provides a method for detecting repetitive defects, comprising:

[0006] a defect position acquisition step, acquiring first position information of a defect on the surface of a workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas that are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece;

[0007] a defect mapping step, taking one of the processed areas as a reference area, and determining, for defects on the reference area, second position information with the reference area as a position reference according to the first position information thereof; and mapping defects on processed areas other than the reference area to corresponding positions of the reference area according to the first position information thereof, and determining, according to the corresponding position, second position information with the reference area as a position reference thereof;

[0008] A dense defect detection step, according to the second position information of the defects on all the processed areas, determines the defect density peak point with the reference area as the position reference; according to the second position information of the defects on all the processed areas, respectively calculates the number of defects within the preset range of each of the defect density peak points, and compares the number of defects within the preset range of each of the defect density peak points with the size of the preset repetitive defect threshold;

[0009] A judgment step, if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that the workpiece has repetitive defects, and the repetitive defects are used to indicate defects existing on the workpiece.

[0010] In some embodiments, the method of determining the defect density peak point with the reference area as the position reference based on the second position information of the defects on all processed areas includes: spatially clustering the defects based on the second position information of the defects on all processed areas; and separately determining the defect density peak points of each category obtained after spatial clustering.

[0011] In some embodiments, the defects are spatially clustered according to the second position information of the defects on all processed areas, and the defect density peak points of each category obtained after the spatial clustering are determined respectively, including: according to the second position information of the defects on all processed areas, the defects are spatially clustered using a density-based noise application spatial clustering algorithm; and the defect density peak points of each category obtained after the spatial clustering are located using a density peak clustering algorithm.

[0012] In some embodiments, the defects are spatially clustered according to the second position information of the defects on all processed areas, including: according to the second position information of the defects on all processed areas, the defects are spatially clustered according to a preset search radius, wherein, in the process of spatial clustering, a KD tree is used to search for defects within the search radius.

[0013] In some embodiments, the preset range is a range within the search radius centered at the defect density peak point.

[0014] In some embodiments, the dense defect detection step further includes: for each of the defect density peak points, if the number of defects within a preset range is greater than or equal to the repetitive defect threshold, retaining the defects within the preset range of the defect density peak point, otherwise eliminating the defects within the preset range of the defect density peak point;

[0015] The intensive defect detection step is repeatedly performed until a preset iteration stop condition is reached, and then the judgment step is performed.

[0016] In some embodiments, the iteration stopping condition is that the number of repeated executions of the intensive defect detection step reaches a preset execution number threshold, or the number of defect density peak points obtained in the current execution and the number of defects within a preset range of each defect density peak point are the same as those in the previous execution.

[0017] In some embodiments, after the judgment step, it also includes: for a defect density peak point where the number of defects within a preset range is greater than or equal to the repeatable defect threshold, based on the second position information of the defects within the preset range of the defect density peak point, obtaining the first position information of the defects within the preset range of the defect density peak point with the workpiece as the position reference.

[0018] According to a second aspect, an embodiment of the present application provides a repetitive defect detection device, comprising:

[0019] A defect position acquisition module, used for acquiring first position information of a defect on a surface of a workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas which are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece;

[0020] a defect mapping module connected to the defect position acquisition module, and configured to take one of the processed areas as a reference area, and for determining, for defects on the reference area, second position information with the reference area as a position reference according to the first position information thereof; and for defects on processed areas other than the reference area, mapping them to corresponding positions of the reference area according to the first position information thereof, and determining, according to the corresponding position, second position information with the reference area as a position reference thereof;

[0021] A dense defect detection module is connected to the defect mapping module and is used to determine the defect density peak point with the reference area as the position reference according to the second position information of the defects on all the processed areas; calculate the number of defects within the preset range of each defect density peak point according to the second position information of the defects on all the processed areas, and compare the number of defects within the preset range of each defect density peak point with the size of the preset repetitive defect threshold;

[0022] A judgment module is connected to the dense defect detection module and is used to determine that the object under test has repetitive defects if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold. The repetitive defects are used to indicate defects existing on the workpiece.

[0023] According to a third aspect, an embodiment of the present application provides a repetitive defect detection system, comprising:

[0024] A defect detection device, used for performing defect detection on a workpiece, and obtaining first position information of defects on the surface of the workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas which are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece;

[0025] A processor is connected to the defect detection device and is used to obtain first position information of defects on the surface of a workpiece with the workpiece as a position reference; and one of the processed areas is used as a reference area. For defects on the reference area, second position information with the reference area as a position reference is determined according to the first position information; for defects on processed areas other than the reference area, defects are mapped to corresponding positions of the reference area according to the first position information, and second position information with the reference area as a position reference is determined according to the corresponding position; based on the second position information of defects on all processed areas, a defect density peak point with the reference area as a position reference is determined; based on the second position information of defects on all processed areas, the number of defects within a preset range of each defect density peak point is calculated, and the number of defects within the preset range of each defect density peak point is compared with the size of a preset repeatability defect threshold; if the number of defects within the preset range of a certain defect density peak point is greater than or equal to the repeatability defect threshold, it is determined that the object under test has repeatability defects, and the repeatability defects are used to indicate defects existing on the workpiece.

[0026] According to a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored, and the program can be executed by a processor to implement the detection method of any embodiment in the above-mentioned first aspect.

[0027] According to the detection method, device, system and computer-readable storage medium of repetitive defects of the above-mentioned embodiments, firstly, the first position information of the defect on the surface of the workpiece is obtained with the workpiece as the position reference, and then one of the multiple processed areas on the surface of the workpiece is used as the reference area to determine the second position information of the defect on the surface of the workpiece with the reference area as the position reference, and the defect on the surface of the workpiece is mapped to the corresponding position in the reference area; because the repetitive defects mapped to the reference area will be densely distributed in a certain area, the present application detects repetitive defects by defect density, and determines the defect density peak point with the reference area as the position reference according to the second position information of the defects on all processed areas; if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that the workpiece has repetitive defects, so that the repetitive defects in the workpiece can be accurately and effectively detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of a method for detecting repetitive defects in some embodiments;

[0029] Figure 2 is a defect detection diagram obtained by performing defect detection on a workpiece in one embodiment;

[0030] FIG3(a) is a schematic diagram of an image coordinate system of a defect detection image in one embodiment, and FIG3(b) is a schematic diagram of a first coordinate system in one embodiment;

[0031] Figure 4 is a defect map in one embodiment;

[0032] FIG. 5( a ) is a schematic diagram of an image coordinate system of a defect map in one embodiment, and FIG. 5( b ) is a schematic diagram of a second coordinate system in one embodiment;

[0033] Figure 6 A schematic diagram of the positional relationship between the processed area and the reference area in an embodiment;

[0034] Figure 7 Flow charts of methods for detecting repetitive defects in other embodiments;

[0035] Figure 8 is an effect diagram of repetitive defects on a wafer detected in an embodiment;

[0036] Fig. 9is a schematic structural diagram of a detection device for repetitive defects in some embodiments;

[0037] Fig.10 is a schematic structural diagram of a detection device for repetitive defects in other embodiments;

[0038] Fig.11 Schematic diagram of the structure of a detection system for repetitive defects in some embodiments. DETAILED DESCRIPTION

[0039] The present application is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are intended to enable the present application to be better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0040] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0041] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0042] As described in the background technology, in the relevant processing process, a specific light passes through the processing piece to process the surface of the workpiece to form a processed area, and the workpiece and the processing piece move relative to each other at a certain step length, and then the specific light passes through the processing piece to continue processing the surface of the workpiece to form another processed area, and this is repeated. Therefore, the surface of the workpiece has multiple processed areas that are processed after the specific light passes through the processing piece, and these multiple processed areas can be processed in sequence by the specific light passing through the processing piece through the gradual relative movement of the workpiece and the processing piece. It can be seen that the defects caused by the processing piece will basically appear in each processed area, causing repetitive defects.

[0043] The specific light can be visible light, ultraviolet light, extreme ultraviolet light, etc. The workpiece refers to the workpiece or product being processed, such as a wafer, a glass panel, and a PCB panel. The workpiece is a workpiece that cooperates with the specific light to achieve the processing goal, such as a photomask.

[0044] In the detection of repetitive defects, the traditional method usually uses an optical microscope to manually sample and inspect parts of the workpiece. However, due to the limited inspection area, this method is difficult to effectively detect random and tiny repetitive defects, resulting in false detection and missed detection, which in turn affects the product yield. Therefore, a more efficient and accurate detection method is needed to detect repetitive defects in order to improve product reliability and yield.

[0045] The applicant found that if the defects of each processed area are mapped to the same processed area and their position information with the processed area as the position reference is obtained, then according to the position information, the repetitive defects are densely distributed in one area. By detecting the defect density and locating the peak point of the defect density, it can be determined whether there are repetitive defects. Based on this, the applicant proposed a repetitive defect detection method that can accurately and efficiently detect repetitive defects on the processed parts. Please refer to Figure 1 In some embodiments, the method includes a defect location acquisition step 100, a defect mapping step 200, a dense defect detection step 300 and a judgment step 400, which are described in detail below with reference to the accompanying drawings.

[0046] Defect position acquisition step 100: acquiring first position information of a defect on the surface of a workpiece with the workpiece as a position reference.

[0047] Specifically, the defects on the surface of the workpiece are detected to determine the defects on the surface of the workpiece and their first position information. The defects on the surface of the workpiece can be detected by taking an image of the surface of the workpiece with a camera, and the defects on the surface of the workpiece are detected using the image and shown in the image to obtain a defect detection map of the workpiece. Figure 2 FIG. 1 is a defect detection diagram obtained by performing defect detection on a wafer in an embodiment, which includes a plurality of squares A, each of which is a processed area, and the tiny black dots in the diagram are detected defects; in this embodiment, the specific light is exposed on the wafer each time to form four bare chips (also called dies), so each square A includes four small squares, and each small square is a bare chip (see FIG. 1 ). Figure 4 ). The method of defect detection on the surface of the workpiece can refer to the relevant existing technology and will not be described in detail here. In addition, defect detection equipment such as a KLA scanner can also be used to scan the surface of the workpiece to obtain a defect detection map of the workpiece.

[0048] The first position information includes a first coordinate. In some embodiments, the first coordinate of the defect on the surface of the workpiece with the workpiece as the position reference is the image coordinate of the defect on the defect detection map, that is, the coordinate with the image coordinate system of the defect detection map as the reference system. The image coordinate system of the defect detection map is shown in Figure 3 (a), and the coordinate unit is pixel. In other embodiments, the center of the workpiece can be used as the origin, and the X-axis and Y-axis can be established along the horizontal and vertical directions of the image, respectively, to establish a first coordinate system, as shown in Figure 3 (b), and the coordinate unit is pixel, then the first coordinate of the defect on the surface of the workpiece with the workpiece as the position reference is the coordinate with the first coordinate system as the reference system.

[0049] Defect mapping step 200: taking one of the processed areas as a reference area, for defects on the reference area, determining its second position information with the reference area as a position reference based on its first position information; for defects on processed areas other than the reference area, mapping them to corresponding positions of the reference area based on their first position information, and determining their second position information with the reference area as a position reference based on the corresponding position.

[0050] The reference area can be any processed area. The main purpose of this step is to obtain the position of defects in each processed area after being mapped to the reference area. A defect map can be generated according to the size of the reference area to record the defects mapped to the reference area, such as Figure 4 shown.

[0051] The second position information includes a second coordinate. In some embodiments, the second coordinate with the reference area as the position reference is the image coordinate of the defect on the defect map, that is, the coordinate with the image coordinate system of the defect map as the reference system. The image coordinate system of the defect map is shown in FIG5(a), and the coordinate unit is pixel. In other embodiments, the center of the reference area can be used as the origin, and the X-axis and Y-axis can be established along the horizontal and vertical directions of the defect map to establish a second coordinate system, as shown in FIG5(b), and the coordinate unit is pixel. Then, the second coordinate with the reference area as the position reference is the coordinate with the second coordinate system as the reference system.

[0052] For example, a first coordinate system as shown in FIG3(b) is established, with its origin being the center of the wafer and the processed area A being the reference area. A second coordinate system as shown in FIG5(b) is established, with its origin being the center of the reference area. For a defect in the reference area, assuming that its first coordinate is (-140, 260) and the first coordinate of the center point of the reference area is (-150, 250), then the second coordinate of the defect is (10, 10). For a processed area B other than the reference area, assuming that it is separated from the reference area by one processed area in the row direction and is flush with the reference area in the column direction, such as Figure 6As shown in the figure, the size of each processed area is 100×100. For a defect on the processed area B, assuming that its first coordinate is (50,280), it is mapped to the corresponding position of the reference area according to its first coordinate. The corresponding position is the position where the defect on the processed area B corresponds to the reference area after the processed area B is superimposed on the reference area. The corresponding position of the defect in the reference area is (-150,280). According to the corresponding position, its second coordinate can be determined to be (0,30). For defects on other processed areas, the same can be applied.

[0053] Dense defect detection step 300: based on the second position information of defects on all processed areas, determine the defect density peak point with the reference area as the position reference; based on the second position information of defects on all processed areas, calculate the number of defects within the preset range of each defect density peak point, and compare the number of defects within the preset range of each defect density peak point with the size of the preset repeatability defect threshold.

[0054] Specifically, according to the second position information of defects on all processed areas, the defect density peak point with the reference area as the position reference is determined, that is, the second coordinates of the defect density peak point are determined according to the second coordinates of defects on all processed areas. A clustering algorithm such as Density Peak Clustering (DPC) can be used to determine the defect density peak point.

[0055] The preset range of the defect density peak point may be a range within a preset radius centered on the defect density peak point, or a range within a rectangle of a preset size centered on the defect density peak point, etc. The preset range and repeatability defect threshold may be set according to the production environment, parameters such as the shape and size of the workpiece and the workpiece being processed, and the requirements of the detection accuracy.

[0056] Determination step 400: If the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that the workpiece has repetitive defects.

[0057] Among them, repetitive defects are used to indicate defects existing on the workpiece. If the workpiece has repetitive defects, it means that there are defects on the workpiece. The position of the defect on the workpiece can be roughly determined by the position of the repetitive defects on the workpiece.

[0058] In some embodiments, the peak point of defect density based on the reference area is determined based on the second position information of defects on all processed areas, including: spatially clustering the defects based on the second position information of defects on all processed areas; and respectively determining the peak points of defect density of each category obtained after spatial clustering. Spatial clustering can use density-based spatial clustering of applications with noise (DBSCAN), Gaussian mixture model (GMM), etc.

[0059] In this embodiment, firstly, the defects on all processed areas are spatially clustered, and subsequently only the defect density peak point needs to be determined for each category, thereby reducing the amount of calculation and improving the efficiency of detecting the defect density peak point.

[0060] In some embodiments, the defects are spatially clustered according to the second position information of the defects on all processed areas, and the defect density peak points of each category obtained after the spatial clustering are determined respectively, including: according to the second position information of the defects on all processed areas, the defects are spatially clustered using a density-based noise application spatial clustering algorithm; and the defect density peak points of each category obtained after the spatial clustering are located using a density peak clustering algorithm.

[0061] In this embodiment, a density peak clustering algorithm is used to determine the defect density peak point, which is conducive to accurately locating the defect density peak point of each category. Since the time complexity of the density peak clustering algorithm is relatively high, in order to quickly locate the defect density peak point, a density-based noise application spatial clustering algorithm is used to spatially cluster the defects. Since the density-based noise application spatial clustering algorithm also clusters according to density and can filter noise to a certain extent, the algorithm is used for clustering and then the density peak clustering algorithm is used to locate the defect density peak point of each category. This can effectively improve the computational efficiency and greatly improve the problem of low computational efficiency of the density peak clustering algorithm.

[0062] In some embodiments, defects are spatially clustered based on the second position information of defects on all processed areas, including: based on the second position information of defects on all processed areas, defects are spatially clustered according to a preset search radius, wherein, in the process of spatial clustering, a KD tree is used to search for defects within the search radius.

[0063] For some spatial clustering methods such as the DBSCAN algorithm, it is necessary to preset a search radius, and perform spatial clustering of defects according to the preset search radius. That is, in the clustering process, it is necessary to search for defects within the search radius for points in space, and the time complexity is very high. In this embodiment, when the defects are spatially clustered according to the preset search radius, the KD tree is used to search for defects within the search radius, which can effectively speed up the search. On the basis of this embodiment, in order to keep the search range consistent, the preset range in step 300 can be a range within the search radius centered on the peak point of the defect density.

[0064] Please refer to Figure 7 In some embodiments, the dense defect detection step 300 further includes: for each defect density peak point, if the number of defects within the preset range is greater than or equal to the repetitive defect threshold, the defects within the preset range of the defect density peak point are retained, otherwise the defects within the preset range of the defect density peak point are removed. The dense defect detection step 300 is repeatedly executed until the preset iteration stop condition is reached, and then the judgment step 400 is executed.

[0065] Among them, the iteration stopping condition can be that the number of repeated executions of the dense defect detection step 300 reaches a preset execution number threshold, or the number of defect density peak points obtained in the current execution and the number of defects within a preset range of each defect density peak point are the same as those in the previous execution.

[0066] If the number of defects within a preset range of a defect density peak point is greater than or equal to the repetitive defect threshold, it means that these defects may be repetitive defects. In this embodiment, by retaining defects that may be repetitive defects and eliminating the remaining defects, further screening is continued. After continuous iterations, noise defects can be effectively eliminated and the accuracy of the defect density peak point can be improved, thereby improving the stability and accuracy of the repetitive defect detection results.

[0067] In some embodiments, after judging step 400, it also includes: for a defect density peak point where the number of defects within a preset range is greater than or equal to a repeatability defect threshold, based on second position information of defects within a preset range of the defect density peak point, obtaining first position information of defects within a preset range of the defect density peak point with the workpiece as a position reference.

[0068] Specifically, defects within a preset range of the defect density peak point can be determined as repetitive defects, and their first coordinates can be obtained based on their second coordinates, thereby obtaining their positions on the workpiece, which is helpful for troubleshooting repetitive defects on the workpiece. Figure 8As shown, it is a rendering of the repetitive defects on the wafer detected in one embodiment, and the "+" symbol in the figure represents the repetitive defects.

[0069] Based on the above-mentioned repetitive defect detection method, the present application also provides a repetitive defect detection device, please refer to Fig. 9 In some embodiments, the device includes a defect position acquisition module 10, a defect mapping module 20, a dense defect detection module 30 and a judgment module 40, which are described below respectively.

[0070] The defect position acquisition module 10 is used to execute the above-mentioned defect position acquisition step 100, that is, to obtain the first position information of the defect on the surface of the workpiece with the workpiece as the position reference. The specific process is shown in the defect position acquisition step 100, which will not be repeated here.

[0071] The defect mapping module 20 is connected to the defect position acquisition module 10, and is used to perform the above-mentioned defect mapping step 200, that is, taking one of the processed areas as the reference area, for defects on the reference area, according to its first position information, its second position information with the reference area as the position reference is determined; for defects on the processed area other than the reference area, according to its first position information, it is mapped to the corresponding position of the reference area, and according to the corresponding position, its second position information with the reference area as the position reference is determined. The specific process is shown in the defect mapping step 200, which will not be repeated here.

[0072] The dense defect detection module 30 is connected to the defect mapping module 20, and is used to perform the above-mentioned dense defect detection step 300, that is, according to the second position information of the defects on all processed areas, determine the defect density peak point with the reference area as the position reference; according to the second position information of the defects on all processed areas, respectively calculate the number of defects within the preset range of each defect density peak point, and compare the number of defects within the preset range of each defect density peak point with the size of the preset repetitive defect threshold. The specific process is shown in the dense defect detection step 300, which will not be repeated here.

[0073] The judgment module 40 is connected to the dense defect detection module 30, and is used to execute the above-mentioned judgment step 400, that is, if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that repetitive defects exist in the object under test, wherein repetitive defects are used to indicate defects existing on the workpiece.

[0074] In some embodiments, the dense defect detection module 30 determines the defect density peak point with the reference area as the position reference based on the second position information of the defects on all processed areas, including: spatially clustering the defects based on the second position information of the defects on all processed areas; and separately determining the defect density peak points of each category obtained after spatial clustering.

[0075] In some embodiments, the dense defect detection module 30 spatially clusters the defects according to the second position information of the defects on all processed areas, and determines the defect density peak points of each category obtained after spatial clustering, including: spatially clustering the defects according to the second position information of the defects on all processed areas using a density-based noise application spatial clustering algorithm; and locating the defect density peak points of each category obtained after spatial clustering using a density peak clustering algorithm.

[0076] In some embodiments, the dense defect detection module 30 spatially clusters the defects based on the second position information of the defects on all processed areas, including: spatially clustering the defects based on a preset search radius based on the second position information of the defects on all processed areas, wherein, in the process of spatial clustering, a KD tree is used to search for defects within the search radius.

[0077] In some embodiments, the dense defect detection step 300 further includes: for each defect density peak point, if the number of defects within the preset range is greater than or equal to the repetitive defect threshold, then the defects within the preset range of the defect density peak point are retained, otherwise the defects within the preset range of the defect density peak point are eliminated. That is, the dense defect detection module 30 is also used for, for each defect density peak point, if the number of defects within the preset range is greater than or equal to the repetitive defect threshold, then the defects within the preset range of the defect density peak point are retained, otherwise the defects within the preset range of the defect density peak point are eliminated. And the dense defect detection module 30 repeatedly executes the dense defect detection step 300 until the above-mentioned iteration stop condition is reached.

[0078] Please refer to Fig.10 In some embodiments, the repetitive defect detection device also includes a repetitive defect determination module 50, which is used to obtain the first position information of the defects within the preset range of the defect density peak point with the workpiece as the position reference based on the second position information of the defects within the preset range of the defect density peak point for a defect density peak point where the number of defects within the preset range is greater than or equal to the repetitive defect threshold.

[0079] Please refer to Fig.11 The present application also provides a repetitive defect detection system, including a defect detection device 1 and a processor 2. The defect detection device 1 may be a KLA scanner, etc., for performing defect detection on a workpiece, and obtaining first position information of defects on the surface of the workpiece with the workpiece as a position reference.

[0080] The processor 2 is connected to the defect detection device 1 and is used to execute the detection method of repetitive defects in any embodiment of the present application. For example, in one embodiment, the processor 2 is used to obtain the first position information of the defects on the surface of the workpiece with the workpiece as the position reference; and one of the processed areas is used as the reference area. For the defects on the reference area, the second position information with the reference area as the position reference is determined according to its first position information; for the defects on the processed areas other than the reference area, they are mapped to the corresponding positions of the reference area according to their first position information, and the second position information with the reference area as the position reference is determined according to the corresponding position; according to the second position information of the defects on all the processed areas, the defect density peak point with the reference area as the position reference is determined, and according to the second position information of the defects on all the processed areas, the number of defects within the preset range of each defect density peak point is calculated respectively, and the number of defects within the preset range of each defect density peak point is compared with the size of the preset repetitive defect threshold; if the number of defects within the preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that the object under test has repetitive defects, wherein the repetitive defects are used to indicate defects existing on the workpiece. The specific process is described in the above description of the detection method for repetitive defects, which will not be repeated here.

[0081] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0082] The above specific examples are used to illustrate the present invention, which are only used to help understand the present invention and are not used to limit the present invention. For those skilled in the art to which the present invention belongs, some simple deductions, modifications or substitutions can be made according to the idea of ​​the present invention.

Claims

1. A method for detecting repetitive defects, characterized in that: include: a defect position acquisition step, acquiring first position information of a defect on the surface of a workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas that are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece; a defect mapping step, taking one of the processed areas as a reference area, and determining, for defects on the reference area, second position information with the reference area as a position reference according to the first position information thereof; and mapping defects on processed areas other than the reference area to corresponding positions of the reference area according to the first position information thereof, and determining, according to the corresponding position, second position information with the reference area as a position reference thereof; A dense defect detection step, according to the second position information of the defects on all the processed areas, determines the defect density peak point with the reference area as the position reference; according to the second position information of the defects on all the processed areas, respectively calculates the number of defects within the preset range of each of the defect density peak points, and compares the number of defects within the preset range of each of the defect density peak points with the size of the preset repetitive defect threshold; A judgment step, if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold, it is determined that the workpiece has repetitive defects, and the repetitive defects are used to indicate defects existing on the workpiece.

2. The detection method according to claim 1, characterized in that The step of determining the defect density peak point based on the reference area as the position reference according to the second position information of the defects on all processed areas comprises: spatially clustering the defects according to the second position information of the defects on all processed areas; Determine the defect density peak points of each category obtained after spatial clustering.

3. The detection method according to claim 2, characterized in that The method of spatially clustering the defects according to the second position information of the defects on all processed areas and respectively determining the peak points of defect density of each category obtained after spatial clustering includes: According to the second position information of the defects on all processed areas, a density-based noise application spatial clustering algorithm is used to spatially cluster the defects; and a density peak clustering algorithm is used to locate the defect density peak point of each category obtained after spatial clustering.

4. The detection method according to claim 2, characterized in that The method of spatially clustering the defects according to the second position information of the defects on all processed areas includes: spatially clustering the defects according to a preset search radius according to the second position information of the defects on all processed areas, wherein, in the process of spatial clustering, a KD tree is used to search for defects within the search radius.

5. The detection method according to claim 4, characterized in that: The preset range is a range within the search radius centered at the defect density peak point.

6. The detection method according to any one of claims 1 to 5, characterized in that: The intensive defect detection step further includes: for each of the defect density peak points, if the number of defects within the preset range is greater than or equal to the repetitive defect threshold, retaining the defects within the preset range of the defect density peak point, otherwise eliminating the defects within the preset range of the defect density peak point; The intensive defect detection step is repeatedly performed until a preset iteration stop condition is reached, and then the judgment step is performed.

7. The detection method according to claim 6, characterized in that The iteration stopping condition is that the number of repeated executions of the dense defect detection step reaches a preset execution number threshold, or the number of defect density peak points obtained in the current execution and the number of defects within a preset range of each defect density peak point are the same as those in the previous execution.

8. The detection method according to claim 1, characterized in that After the judgment step, it also includes: for a defect density peak point where the number of defects within a preset range is greater than or equal to the repeatability defect threshold, based on the second position information of the defects within the preset range of the defect density peak point, obtaining the first position information of the defects within the preset range of the defect density peak point with the workpiece as the position reference.

9. A device for detecting repetitive defects, characterized in that: include: A defect position acquisition module, used for acquiring first position information of a defect on a surface of a workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas which are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece; a defect mapping module connected to the defect position acquisition module, and configured to take one of the processed areas as a reference area, and for determining, for defects on the reference area, second position information with the reference area as a position reference according to the first position information thereof; and for defects on processed areas other than the reference area, mapping them to corresponding positions of the reference area according to the first position information thereof, and determining, according to the corresponding position, second position information with the reference area as a position reference thereof; A dense defect detection module is connected to the defect mapping module and is used to determine the defect density peak point with the reference area as the position reference according to the second position information of the defects on all the processed areas; calculate the number of defects within the preset range of each defect density peak point according to the second position information of the defects on all the processed areas, and compare the number of defects within the preset range of each defect density peak point with the size of the preset repetitive defect threshold; A judgment module is connected to the dense defect detection module and is used to determine that the object under test has repetitive defects if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repetitive defect threshold. The repetitive defects are used to indicate defects existing on the workpiece.

10. The detection device according to claim 9, characterized in that: The dense defect detection module determines the defect density peak point with the reference area as the position reference according to the second position information on all processed areas, including: spatially clustering the defects according to the second position information of the defects on all processed areas; Determine the defect density peak points of each category obtained after spatial clustering.

11. A repetitive defect detection system, characterized in that: include: A defect detection device, used for performing defect detection on a workpiece, and obtaining first position information of defects on the surface of the workpiece with the workpiece as a position reference; wherein the surface of the workpiece has a plurality of processed areas which are processed after a specific light passes through the workpiece, and the plurality of processed areas can be processed in sequence by the specific light passing through the workpiece through the stepwise relative movement of the workpiece and the workpiece; A processor is connected to the defect detection device and is used to obtain first position information of defects on the surface of a workpiece with the workpiece as a position reference; and one of the processed areas is used as a reference area. For defects on the reference area, second position information with the reference area as a position reference is determined according to the first position information; for defects on processed areas other than the reference area, defects are mapped to corresponding positions of the reference area according to the first position information, and second position information with the reference area as a position reference is determined according to the corresponding position; based on the second position information of defects on all processed areas, a defect density peak point with the reference area as a position reference is determined; based on the second position information of defects on all processed areas, the number of defects within a preset range of each defect density peak point is calculated, and the number of defects within the preset range of each defect density peak point is compared with the size of a preset repeatability defect threshold; if the number of defects within the preset range of a certain defect density peak point is greater than or equal to the repeatability defect threshold, it is determined that the object under test has repeatability defects, and the repeatability defects are used to indicate defects existing on the workpiece.

12. A computer-readable storage medium, characterized in that: The medium stores a program, which can be executed by a processor to implement the detection method according to any one of claims 1 to 8.

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