Methods, apparatus, systems, and readable storage media for detecting repetitive defects

Abstract

A method, apparatus, system, and readable storage medium for detecting repetitive defects are disclosed. The method includes: acquiring first position information of defects on the surface of a workpiece, with the workpiece as a position reference; determining second position information of defects on the surface of the workpiece, with the reference region as a position reference, using one of a plurality of processed regions on the surface of the workpiece as a reference region; determining a defect density peak point with the reference region as a position reference based on the second position information of defects on all processed regions; and determining that the workpiece has a repetitive defect if the number of defects within a preset range of a certain defect density peak point is greater than or equal to a repetitive defect threshold. Since repetitive defects are densely distributed in a certain area when mapped to a reference region, this application detects repetitive defects by defect density, which can accurately and effectively detect repetitive defects in the workpiece.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and more specifically to methods, apparatus, systems, and readable storage media for detecting repetitive defects. Background Technology

[0002] With the rapid development of semiconductor technology, the complexity and sophistication of semiconductor manufacturing processes are constantly increasing. Due to the diversity of production equipment and processes, unpredictable defects may appear on the surface of the processed semiconductor workpieces. To ensure the smooth operation of the semiconductor manufacturing process, monitoring stations are usually set up after critical process steps to detect the presence of defects on the semiconductor workpieces, thereby promptly identifying and resolving potential problems and ensuring high product quality and reliability.

[0003] In some semiconductor manufacturing processes, a workpiece is needed to allow specific light to pass through it and process the semiconductor surface. For example, in photolithography, ultraviolet light is used to expose a wafer through a photomask to copy the circuit pattern from the photomask onto the wafer. If tiny dust particles are present on the photomask, these particles will block some of the light, preventing exposure on the wafer and creating photolithography defects. Because the exposure process is repetitive and step-by-step—after ultraviolet light exposes one area of ​​the wafer through the photomask, the wafer moves a certain distance so that ultraviolet light passes through the photomask to expose another area—multiple processed areas are formed on the wafer. Therefore, these photolithography defects have the characteristic of recurring on the wafer and are called repetitive defects. In the production process, repetitive defects have a wide impact, are difficult to detect, and significantly affect product reliability and yield. Therefore, detecting repetitive defects in processed semiconductor workpieces is particularly important. Summary of the Invention

[0004] To detect repetitive defects, this application provides a method, apparatus, system, and computer-readable storage medium for detecting repetitive defects.

[0005] According to a first aspect, embodiments of this application provide a method for detecting repeatability defects, comprising:

[0006] The defect location acquisition step acquires first location information of the defects on the surface of the workpiece with the workpiece as the location reference; wherein, the surface of the workpiece has multiple processing areas that are processed after being processed by specific light passing through the workpiece, and the multiple processing areas can be processed sequentially by specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece.

[0007] The defect mapping step involves taking one of the processed areas as a reference area. For a defect on the reference area, a second position information with the reference area as the position reference is determined based on its first position information. For a defect on a processed area other than the reference area, it is mapped to the corresponding position of the reference area based on its first position information, and a second position information with the reference area as the position reference is determined based on the corresponding position.

[0008] The dense defect detection step involves determining the defect density peak point with the reference area as the location reference based on the second location information of defects on all processed areas; calculating the number of defects within a preset range of each defect density peak point based on the second location information of defects on all processed areas; and comparing the number of defects within the preset range of each defect density peak point with the size of a preset repeatability defect threshold.

[0009] In the 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 repeatability defect threshold, then it is determined that the workpiece has repeatability defects, and the repeatability defects are used to indicate the defects present on the workpiece.

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

[0011] In some embodiments, the step of spatially clustering the defects based on the second location information of defects on all processed areas, and determining the defect density peak points of each category obtained after spatial clustering, includes: using a density-based noise-applied spatial clustering algorithm to spatially cluster the defects based on the second location information of defects on all processed areas; and using a density peak clustering algorithm to locate the defect density peak points of each category obtained after spatial clustering.

[0012] In some embodiments, the step of spatially clustering the defects based on the second location information of defects on all processed areas includes: spatially clustering the defects based on the second location information of defects on all processed areas and according to a preset search radius, wherein, during the spatial clustering process, a KD tree is used to find defects located within the search radius.

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

[0014] In some embodiments, the dense defect detection step further includes: for each defect density peak point, if the number of defects within its preset range is greater than or equal to the repeatability defect threshold, then retain the defects within the preset range of the defect density peak point; otherwise, remove the defects within the preset range of the defect density peak point.

[0015] The dense defect detection step is repeated until a preset iteration stop condition is reached, at which point the judgment step is executed.

[0016] In some embodiments, the iteration stopping condition is that the number of times the dense defect detection step is repeatedly executed reaches a preset execution number threshold, or that 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 in the previous execution.

[0017] In some embodiments, after the determination step, the method further 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, obtaining 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 located within the preset range of the defect density peak point.

[0018] According to a second aspect, embodiments of this application provide a detection device for repetitive defects, comprising:

[0019] A defect location acquisition module is used to acquire first location information of defects on the surface of a workpiece with the workpiece as the location reference; wherein, the surface of the workpiece has multiple processing areas that are processed after being processed by specific light passing through the workpiece, and the multiple processing areas can be processed sequentially by specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece.

[0020] The defect mapping module, connected to the defect location acquisition module, is used to take one of the processed areas as a reference area, and for a defect on the reference area, determine its second location information with the reference area as the location reference based on its first location information; for a defect on a processed area other than the reference area, map it to the corresponding position of the reference area based on its first location information, and determine its second location information with the reference area as the location reference based on the corresponding position.

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

[0022] The judgment module, connected to the dense defect detection module, is used to determine that the tested object has a repeatable defect if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repeatable defect threshold. The repeatable defect is used to indicate the defects present on the workpiece.

[0023] According to a third aspect, embodiments of this application provide a system for detecting repeatable defects, comprising:

[0024] A defect detection device is used to detect defects in a workpiece and obtain first position information of the defects on the surface of the workpiece with the workpiece as a position reference; wherein the surface of the workpiece has multiple processing areas that are processed after specific light passes through the workpiece, and the multiple processing areas can be processed sequentially by specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece.

[0025] The processor, connected to the defect detection device, is used to acquire first position information of defects on the surface of the workpiece, with the workpiece as the position reference; and using one of the workpiece areas as the reference area, for defects on the reference area, determine second position information with the reference area as the position reference based on their first position information; for defects on workpiece areas other than the reference area, map them to the corresponding position of the reference area based on their first position information, and determine second position information with the reference area as the position reference based on the corresponding position; based on the second position information of defects on all workpiece 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 workpiece areas, calculate the number of defects within a 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 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 tested object has a repeatability defect, and the repeatability defect is used to indicate the defects present on the workpiece.

[0026] According to a fourth aspect, embodiments of this application provide a computer-readable storage medium storing a program that can be executed by a processor to implement the detection method of any of the embodiments of the first aspect described above.

[0027] According to the above embodiments of the method, apparatus, system and computer-readable storage medium for detecting repetitive defects, firstly, first position information of defects on the surface of a workpiece is obtained with the workpiece as the position reference. Then, using one of the multiple processed areas on the surface of the workpiece as the reference area, second position information of defects on the surface of the workpiece is determined with the reference area as the position reference, thus obtaining the corresponding position of the defects on the surface of the workpiece mapped to the reference area. Since repetitive defects mapped to the reference area will be densely distributed in a certain area, this application detects repetitive defects by defect density. Based on the second position information of defects on all processed areas, the peak point of defect density with the reference area as the position reference is determined. If the number of defects within a preset range of a certain peak point of defect density is greater than or equal to the repetitive defect threshold, it is determined that there are repetitive defects in the workpiece, thereby accurately and effectively detecting repetitive defects in the workpiece. Attached Figure Description

[0028] Figure 1 A flowchart illustrating a method for detecting repetitive defects in some embodiments;

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

[0030] Figure 3(a) is a schematic diagram of the image coordinate system of the defect detection map in one embodiment, and Figure 3(b) is a schematic diagram of the first coordinate system in one embodiment.

[0031] Figure 4 This is a defect mapping diagram in one embodiment;

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

[0033] Figure 6 This is a schematic diagram illustrating the positional relationship between the processed area and the reference area in one embodiment;

[0034] Figure 7 A flowchart illustrating a method for detecting repetitive defects in other embodiments;

[0035] Figure 8 This is an illustration of a repeatable defect detected on a wafer in one embodiment.

[0036] Figure 9This is a schematic diagram of the structure of a detection device for repeatable defects in some embodiments;

[0037] Figure 10 This is a schematic diagram of the structure of a detection device for repetitive defects in some other embodiments;

[0038] Figure 11 This is a schematic diagram of the structure of a detection system for repetitive defects in some embodiments. Detailed Implementation

[0039] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments. Similar elements in different embodiments are referred to by related similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the present application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present application are not shown or described in the specification. This is to avoid obscuring the core parts of the present application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0040] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0041] The serial numbers assigned to components in this article, such as "first" and "second", are used only to distinguish the objects being described and have no sequential or technical meaning.

[0042] As described in the background section, during the relevant processing, specific light passes through the workpiece to form a processed area on its surface. The workpiece and the workpiece move relative to each other in steps, and then the specific light continues to pass through the workpiece to process the surface of the workpiece, forming another processed area, and so on. Therefore, the surface of the workpiece has multiple processed areas that have been processed by the specific light passing through the workpiece. These multiple processed areas can be processed sequentially by the specific light passing through the workpiece through the gradual relative movement of the workpiece and the workpiece. It is evident that defects caused by the workpiece will appear in almost every processed area, resulting in repetitive defects.

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

[0044] In the detection of repetitive defects, traditional methods typically employ optical microscopes, involving manual sampling and inspection of specific areas of the workpiece. However, due to the limited inspection area, this method struggles to effectively detect randomly occurring and minute repetitive defects, leading to false positives and false negatives, which in turn affect product yield. Therefore, a more efficient and accurate detection method is needed to identify repetitive defects, thereby improving product reliability and yield.

[0045] The applicant discovered that if defects in each processed area are mapped to the same processed area, and their location information relative to that processed area is obtained, then based on this location information, repetitive defects are densely distributed within a region. By detecting the defect density and locating the peak point of the defect density, the presence of repetitive defects can be determined. Based on this, the applicant proposes a method for detecting repetitive defects, which can accurately and efficiently detect repetitive defects on a processed part. Please refer to [reference needed]. 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 location acquisition step 100: Acquire the first location information of the defect on the surface of the workpiece with the workpiece as the location reference.

[0047] Specifically, by performing defect detection on the surface of the workpiece, the defects on the workpiece surface and their initial location information are determined. Defect detection on the workpiece surface can be performed by capturing an image of the workpiece surface with a camera, using this image to detect the defects and display them in the image, thus obtaining a defect detection map of the workpiece. For example... Figure 2 This is a defect detection map obtained from defect detection on a wafer in one embodiment. It includes multiple squares A, each square A being a processed area. The tiny black dots in the map are the detected defects. In this embodiment, a specific light is exposed on the wafer each time to form 4 dies. Therefore, each square A includes 4 smaller squares, each smaller square being a die (see...). Figure 4 Methods for defect detection on the surface of a workpiece can be found in existing technologies and will not be described in detail here. Alternatively, defect detection equipment such as a KLA scanner can be used to scan the surface of the workpiece to obtain a defect detection image.

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

[0049] Defect mapping step 200: Taking one of the processed areas as the reference area, for a defect on the reference area, determine its second position information with the reference area as the position reference based on its first position information; for a defect on a processed area other than the reference area, map it to the corresponding position of the reference area based on its first position information, and determine its second position information with the reference area as the position reference based on the corresponding position.

[0050] The reference region can be any processed region. The main purpose of this step is to obtain the positions of defects in each processed region after they have been mapped to the reference region. A defect mapping map can be generated based on the size of the reference region to record the defects mapped to it, such as... Figure 4 As shown.

[0051] The second position information includes second coordinates. In some embodiments, the second coordinates, with the reference area as the position reference, are the image coordinates of the defect on the defect mapping map, that is, coordinates with the image coordinate system of the defect mapping map as the reference system, as shown in Figure 5(a), where the coordinate unit is pixels. In other embodiments, the center of the reference area can be used as the origin, and X-axis and Y-axis can be established along the horizontal and vertical directions of the defect mapping map, respectively, to establish a second coordinate system, as shown in Figure 5(b), where the coordinate unit is pixels. In this case, the second coordinates with the reference area as the position reference are coordinates with the second coordinate system as the reference system.

[0052] For example, establish a first coordinate system as shown in Figure 3(b), with its origin at the center of the wafer and the processed area A as the reference area. Establish a second coordinate system as shown in Figure 5(b), with its origin at the center of the reference area. For a defect in the reference area, assuming 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 it is separated from the reference area by one processed area in the row direction and aligned with it in the column direction, such as... Figure 6As shown, each processed area is 100×100 in size. For a defect in processed area B, assuming its first coordinate is (50, 280), its first coordinate is mapped to the corresponding position in the reference area. The corresponding position is the position of the defect in processed area B in the reference area after processed area B is superimposed on the reference area. Therefore, the corresponding position of the defect in the reference area is (-150, 280). Based on this corresponding position, its second coordinate can be determined as (0, 30). The same principle applies to defects in other processed areas.

[0053] Dense Defect Detection Step 300: Based on the second location information of defects on all processed areas, determine the defect density peak point with the reference area as the location reference; based on the second location 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 preset repeatability defect threshold.

[0054] Specifically, based on the second location information of defects in all processed areas, the defect density peak point is determined with the reference area as the location reference. This means determining the second coordinates of the defect density peak point based on the second coordinates of the defects in all processed areas. Clustering algorithms 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 can 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. The preset range and repeatability defect threshold can be set according to parameters such as the production environment, the shape and size of the workpiece and the workpiece being processed, and the requirements for detection accuracy.

[0056] Judgment step 400: 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, then it is determined that the workpiece has repeatability defects.

[0057] Among them, repeatable defects are used to indicate the defects present on the workpiece. If the workpiece has repeatable defects, it indicates that there are defects on the workpiece. By the location of the repeatable defects on the workpiece, the location of the defects on the workpiece can be roughly determined.

[0058] In some embodiments, determining the defect density peak points with a reference region as the location reference based on the second location information of defects on all processed areas includes: spatially clustering the defects based on the second location information of defects on all processed areas; and determining the defect density peak points of each category obtained after spatial clustering. The spatial clustering can employ density-based spatial clustering of applications with noise (DBSCAN), Gaussian Mixture Module (GMM), etc.

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

[0060] In some embodiments, the step of spatially clustering the defects based on the second location information of the defects on all processed areas, and determining the defect density peak points of each category obtained after spatial clustering, includes: using a density-based noise-applied spatial clustering algorithm to spatially cluster the defects based on the second location information of the defects on all processed areas; and using a density peak clustering algorithm to locate the defect density peak points of each category obtained after spatial clustering.

[0061] In this embodiment, the density peak clustering algorithm is used to determine the defect density peak points, which is beneficial for accurately locating the defect density peak points of each category. However, since the density peak clustering algorithm has a high time complexity, in order to quickly locate the defect density peak points, a density-based noise applied spatial clustering algorithm is used to spatially cluster the defects. Since the density-based noise applied spatial clustering algorithm also clusters based on density and can filter noise to a certain extent, using this algorithm for clustering and then using the density peak clustering algorithm to locate the defect density peak points of each category 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, spatial clustering of defects is performed based on the second location information of defects on all processed areas, including: spatial clustering of defects based on the second location information of defects on all processed areas and based on a preset search radius, wherein, during the spatial clustering process, a KD tree is used to find defects located within the search radius.

[0063] For some spatial clustering methods, such as the DBSCAN algorithm, a preset search radius is required. Based on this preset search radius, defects are spatially clustered. That is, during the clustering process, for each point in space, defects within its search radius need to be found, resulting in high time complexity. In this embodiment, when spatially clustering defects according to the preset search radius, using a KD-tree to find defects within the search radius can effectively speed up the search. Based on this embodiment, to maintain a consistent search range, the preset range in step 300 can be the range centered on the defect density peak point and within the search radius.

[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 its preset range is greater than or equal to the repeatability 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 discarded. The dense defect detection step 300 is repeated until a preset iteration stop condition is reached, at which point the judgment step 400 is executed.

[0065] The iteration stopping condition can be that the number of times the dense defect detection step 300 is repeated reaches a preset execution number threshold, or that 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 in the previous execution.

[0066] If the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repeatability defect threshold, it indicates that these defects may be repeatable defects. In this embodiment, by retaining the defects that may be repeatable defects and eliminating the remaining defects, further screening can be carried out. Through continuous iteration, 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 repeatability defect detection results.

[0067] In some embodiments, after the determination step 400, the method further 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, obtaining 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 located within the preset range of the defect density peak point.

[0068] Specifically, defects located within a preset range of the defect density peak point can be identified as repetitive defects. Their first coordinates can be obtained from their second coordinates, thus determining their position on the workpiece, which is beneficial for identifying repetitive defects on the workpiece. For example... Figure 8The image shown is an illustration of repeatable defects detected on a wafer in one embodiment. The "+" symbol in the image represents a repeatable defect.

[0069] Based on the above-described method for detecting repetitive defects, this application also provides a device for detecting repetitive defects. Please refer to [reference needed]. Figure 9 In some embodiments, the device includes a defect location acquisition module 10, a defect mapping module 20, a dense defect detection module 30, and a judgment module 40, which are described below.

[0070] The defect location acquisition module 10 is used to perform the above defect location acquisition step 100, that is, to acquire the first location information of the defect on the surface of the workpiece with the workpiece as the location reference. The specific process is described in the defect location acquisition step 100, and will not be repeated here.

[0071] The defect mapping module 20 is connected to the defect location acquisition module 10 and is used to execute the aforementioned defect mapping step 200. Specifically, taking one of the processed areas as a reference area, for defects in the reference area, its second position information, with the reference area as the position reference, is determined based on its first position information. For defects in processed areas other than the reference area, its first position information is mapped to the corresponding position in the reference area, and its second position information, with the reference area as the position reference, is determined based on the corresponding position. The specific process is detailed in defect mapping step 200 and 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 aforementioned dense defect detection step 300, which involves determining the defect density peak points with the reference area as the location reference based on the second location information of defects on all processed areas; calculating the number of defects within a preset range of each defect density peak point based on the second location information of defects on all processed areas; and comparing the number of defects within the preset range of each defect density peak point with the preset repeatability defect threshold. The specific process is detailed in dense defect detection step 300 and will not be repeated here.

[0073] The judgment module 40 is connected to the dense defect detection module 30 and is used to perform the above 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 repeatability defect threshold, it is determined that the tested object has repeatability defects, wherein repeatability defects are used to indicate defects present 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 location reference based on the second location information of defects on all processed areas, including: spatially clustering the defects based on the second location information of defects on all processed areas; and determining the defect density peak point of each category obtained after spatial clustering.

[0075] In some embodiments, the dense defect detection module 30 performs spatial clustering of defects based on the second location information of defects on all processed areas, and determines the defect density peak points of each category obtained after spatial clustering, including: performing spatial clustering of defects using a density-based noise application spatial clustering algorithm based on the second location information of defects on all processed areas; 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 performs spatial clustering of defects based on the second location information of defects on all processed areas, including: performing spatial clustering of defects based on the second location information of defects on all processed areas and based on a preset search radius, wherein, during the spatial clustering process, a KD tree is used to find defects located 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 its preset range is greater than or equal to a repeatability defect threshold, then retain the defects within the preset range of the defect density peak point; otherwise, discard the defects within the preset range of the defect density peak point. That is, the dense defect detection module 30 is further configured to, for each defect density peak point, if the number of defects within its preset range is greater than or equal to a repeatability defect threshold, retain the defects within the preset range of the defect density peak point; otherwise, discard the defects within the preset range of the defect density peak point. Furthermore, the dense defect detection module 300 repeatedly executes the dense defect detection step 300 until the aforementioned iteration stop condition is met.

[0078] Please refer to Figure 10 In some embodiments, the repeatability defect detection device further includes a repeatability defect determination module 50, which is used to obtain, based on the workpiece as the position reference, the first 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 repeatability defect threshold, based on the second position information of the defects within the preset range of the defect density peak point.

[0079] Please refer to Figure 11 This application also provides a system for detecting repetitive defects, including a defect detection device 1 and a processor 2. The defect detection device 1 may be a KLA scanner or the like, used to detect defects on a workpiece and obtain first position information of the defects on the surface of the workpiece with the workpiece as the position reference.

[0080] The processor 2 is connected to the defect detection device 1 and is used to execute the repetitive defect detection method in any embodiment of this application. For example, in one embodiment, the processor 2 is used to acquire first position information of defects on the surface of the workpiece with the workpiece as the position reference; and with one of the workpiece areas as the reference area, for defects on the reference area, second position information with the reference area as the position reference is determined according to the first position information; for defects on the workpiece areas other than the reference area, the first position information is mapped to the corresponding position of the reference area, and the second position information with the reference area as the position reference is determined according to the corresponding position; based on the second position information of defects on all workpiece areas, the defect density peak point with the reference area as the position reference is determined; based on the second position information of defects on all workpiece 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 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 tested object has a repetitive defect, wherein the repetitive defect is used to indicate the defects present on the workpiece. The specific process is described above in the section on detection methods for repetitive defects, and will not be repeated here.

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

[0082] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for detecting repeatable defects, characterized in that, include: The defect location acquisition step involves acquiring first location information of defects on the surface of the workpiece, with the workpiece as the location reference; wherein the surface of the workpiece has multiple processing areas that are processed after being processed by specific light passing through the workpiece, and the multiple processing areas can be processed sequentially by specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece, wherein the specific light is visible light, ultraviolet light or extreme ultraviolet light. The defect mapping step involves taking one of the processed areas as a reference area. For a defect on the reference area, a second position information with the reference area as the position reference is determined based on its first position information. For a defect on a processed area other than the reference area, it is mapped to the corresponding position of the reference area based on its first position information, and a second position information with the reference area as the position reference is determined based on the corresponding position. The dense defect detection step involves determining the defect density peak point with the reference area as the location reference based on the second location information of defects on all processed areas; calculating the number of defects within a preset range of each defect density peak point based on the second location information of defects on all processed areas; and comparing the number of defects within the preset range of each defect density peak point with the size of a preset repeatability defect threshold. In the 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 repeatability defect threshold, then it is determined that the workpiece has repeatability defects, and the repeatability defects are used to indicate the defects present on the workpiece.

2. The detection method as described in claim 1, characterized in that, The step of determining the defect density peak point with the reference area as the location reference based on the second location information of defects on all processed areas includes: Based on the second location information of defects on all processed areas, the defects are spatially clustered. Determine the peak point of defect density for each category obtained after spatial clustering.

3. The detection method as described in claim 2, characterized in that, The step of spatially clustering the defects based on the second location information of defects on all processed areas, and determining the defect density peak points of each category obtained after spatial clustering, includes: Based on the second location information of defects on all processed areas, a density-based noise 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 as described in claim 2, characterized in that, The step of spatially clustering the defects based on the second location information of the defects on all processed areas includes: spatially clustering the defects based on the second location information of the defects on all processed areas and according to a preset search radius, wherein, during the spatial clustering process, a KD tree is used to find defects located within the search radius.

5. The detection method as described in claim 4, characterized in that, The preset range is the range within the search radius centered on the peak point of the defect density.

6. The detection method according to any one of claims 1 to 5, characterized in that, The dense defect detection step further includes: for each defect density peak point, if the number of defects within its preset range is greater than or equal to the repeatability defect threshold, then retain the defects within the preset range of the defect density peak point; otherwise, remove the defects within the preset range of the defect density peak point. The dense defect detection step is repeated until a preset iteration stop condition is reached, at which point the judgment step is executed.

7. The detection method as described in claim 6, characterized in that, The iteration stopping condition is when the number of times the dense defect detection step is repeated reaches a preset execution number threshold, or when 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 in the previous execution.

8. The detection method as described in claim 1, characterized in that, Following the determination step, the method further 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, obtaining 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 located within the preset range of the defect density peak point.

9. A device for detecting repeatable defects, characterized in that, include: The defect location acquisition module is used to acquire first location information of defects on the surface of a workpiece with the workpiece as the location reference; wherein, the surface of the workpiece has multiple processing areas that are processed after being processed by specific light passing through the workpiece, and the multiple processing areas can be processed sequentially by specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece, wherein the specific light is visible light, ultraviolet light or extreme ultraviolet light. The defect mapping module, connected to the defect location acquisition module, is used to take one of the processed areas as a reference area, and for a defect on the reference area, determine its second location information with the reference area as the location reference based on its first location information; for a defect on a processed area other than the reference area, map it to the corresponding position of the reference area based on its first location information, and determine its second location information with the reference area as the location reference based on the corresponding position. A dense defect detection module, connected to the defect mapping module, is used to determine the defect density peak point with the reference area as the location reference based on the second location information of defects on all processed areas; calculate the number of defects within a preset range of each defect density peak point based on the second location information of defects on all processed areas, and compare the number of defects within the preset range of each defect density peak point with the size of a preset repeatability defect threshold. The judgment module, connected to the dense defect detection module, is used to determine that the workpiece has a repeatable defect if the number of defects within a preset range of a certain defect density peak point is greater than or equal to the repeatable defect threshold. The repeatable defect is used to indicate the defects present on the workpiece.

10. The detection device as described in claim 9, characterized in that, The dense defect detection module determines the defect density peak point with the reference area as the location reference based on the second location information on all processed areas, including: Based on the second location information of defects on all processed areas, the defects are spatially clustered. Determine the peak point of defect density for each category obtained after spatial clustering.

11. A detection system for repetitive defects, characterized in that, include: A defect detection device is used to detect defects in a workpiece and obtain first position information of the defects on the surface of the workpiece with the workpiece as a position reference; wherein the surface of the workpiece has multiple processing areas that are processed after specific light passes through the workpiece, and the multiple processing areas can be processed sequentially by the specific light passing through the workpiece through the gradual relative movement between the workpiece and the workpiece, wherein the specific light is visible light, ultraviolet light or extreme ultraviolet light; The processor, connected to the defect detection device, is used to acquire first position information of defects on the surface of a workpiece, with the workpiece as a position reference; and using one of the workpiece areas as a reference area, for defects on the reference area, determine second position information with the reference area as a position reference based on their first position information; for defects on workpiece areas other than the reference area, map them to the corresponding position in the reference area based on their first position information, and determine second position information with the reference area as a position reference based on the corresponding position; based on the second position information of defects on all workpiece areas, determine the defect density peak points with the reference area as a position reference; based on the second position information of defects on all workpiece areas, calculate the number of defects within a 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 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 workpiece has a repeatability defect, and the repeatability defect is used to indicate the defects present on the workpiece.

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

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