Wafer edge collapse detection method, device and storage medium

Abstract

The application discloses a wafer edge collapse detection method and device and a storage medium, and the method comprises the following steps: acquiring all edge images of the edges of a wafer to be detected; extracting edge pixel points corresponding to wafer edges in each edge image respectively, and confirming abnormal edges and counting the number of the abnormal edges based on the distribution characteristics of the edge pixel points; confirming pixel points with a coordinate change degree exceeding a threshold value as outlier pixel points by using the positions of the edge pixel points, and counting the number of the outlier pixel points; analyzing the proportion of wafer edge regions with abnormalities in each edge image in the total wafer edge regions by combining the number of the abnormal edges and the number of the outlier pixel points; and confirming that the corresponding edge image has edge collapse when the proportion exceeds a preset proportion threshold value. The application can accurately and quickly determine whether the wafer to be detected has edge collapse by traversing the edge images of the wafer to be detected, preliminarily confirming normal edges and abnormal edges, and then accurately analyzing the gradient characteristics of the edge pixel points.

Description

Technical Field

[0001] This application relates to the field of wafer inspection technology, and in particular to a wafer edge chipping detection method, equipment and storage medium. Background Technology

[0002] In semiconductor manufacturing, the wafer, as the fundamental material, directly impacts chip performance and yield due to its processing quality. During dicing, transfer, or subsequent processes, wafer edges (especially the outer contour boundaries) are prone to chipping—localized defects, cracks, or breakage—due to mechanical stress or improper handling. Chipping not only interferes with subsequent defect detection processes (such as photolithography alignment or thin film deposition) but can also lead to wafer breakage or circuit failure, severely reducing product yield. Therefore, high-precision, high-efficiency wafer chipping detection technology is a critical step in semiconductor manufacturing.

[0003] Currently, wafer edge damage detection mainly relies on image processing technology. However, existing wafer edge damage detection methods based on image processing technology have significant limitations. To achieve accurate detection, complex image segmentation and feature matching are required, which are computationally intensive, time-consuming, resource-intensive, and have low detection efficiency. To improve detection efficiency, the image processing process becomes relatively coarse, with low sensitivity to minor edge damage (such as shallow defects and gradual breakage), and is easily affected by imaging noise, leading to missed or false detections. As a result, existing wafer edge damage detection methods cannot balance detection accuracy and efficiency, making it difficult to meet the needs of practical applications. Summary of the Invention

[0004] In view of this, this application provides a wafer edge chipping detection method, apparatus, device and storage medium to solve the problem that existing wafer edge chipping detection methods cannot simultaneously achieve detection accuracy and detection efficiency.

[0005] To address the aforementioned technical problems, this application provides a wafer edge chipping detection method, comprising: acquiring all edge images of the wafer to be tested; extracting edge pixels corresponding to the wafer edge in each edge image, distinguishing normal edges from abnormal edges based on the distribution characteristics of the edge pixels, and counting the number of abnormal edges; calculating gradient features using the positions of the edge pixels, analyzing the degree of pixel coordinate change based on the gradient features, marking edge pixels whose coordinate change reaches a threshold as outliers, and counting the number of outliers; combining the number of abnormal edges and the number of outliers to analyze the proportion of abnormal wafer edge regions in each edge image to the total wafer edge region; and confirming that the corresponding edge image has edge chipping when the proportion exceeds a preset proportion threshold.

[0006] As a further improvement of this application, edge pixels are extracted from each edge image, and normal edges and abnormal edges are divided based on the distribution characteristics of the edge pixels. The number of normal edges and the number of abnormal edges are counted. This includes: thresholding the edge image, traversing each pixel of the segmented edge image to confirm the edge pixels and their positions; dividing each edge pixel into normal pixels or abnormal pixels based on its position; distinguishing between normal edges and abnormal edges based on normal pixels and abnormal pixels, and counting the number of abnormal edges.

[0007] As a further improvement of this application, after performing adaptive threshold segmentation on the edge image, each pixel is traversed to confirm the edge pixels and their positions. This includes: segmenting the edge image according to a preset adaptive threshold segmentation algorithm to separate the wafer edge region and the background region; traversing the grayscale value of each pixel in the edge image sequentially in the column direction, and when there are multiple consecutive pixels with a grayscale value of 0, the first pixel with a grayscale value of 0 is taken as the edge pixel and its position is recorded.

[0008] As a further improvement of this application, normal pixels of normal edges satisfy position conditions and continuity conditions, while abnormal pixels of abnormal edges do not satisfy position conditions or continuity conditions.

[0009] Positional conditions are expressed as:

[0010] y center -h≤y i ≤y cemter +h;

[0011] Among them, y center The y-coordinate of the edge pixel represents the median value, and h represents the offset. i This represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image;

[0012] The continuity condition is expressed as:

[0013]

[0014] Among them, l ij L represents the distance between adjacent edge pixels i and j, L is the total distance between all edge pixels, and T represents the preset ratio threshold.

[0015] As a further improvement of this application, gradient features are calculated using the positions of edge pixels, and the gradient features are analyzed to obtain the number of outlier pixels in each edge image. This includes: using the direction perpendicular to the upper and lower boundaries of the edge image as a reference direction, calculating the gradient value of each edge pixel in each edge image using the positions of the edge pixels; calculating a filtering threshold using the gradient values, and filtering the edge pixels in the corresponding edge image based on the filtering threshold; calculating an outlier threshold based on the gradient values ​​of the filtered edge pixels, and identifying the outlier pixels in each edge image based on the outlier threshold, and obtaining the number of outlier pixels.

[0016] As a further improvement to this application, the gradient value calculation process is expressed as follows:

[0017] d i =y i+m -y i ;

[0018] Where, d i y represents the gradient value of the i-th edge pixel. i The coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image are represented by y, m represents the scale range for calculating the gradient value, and y represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image. i+m This represents the coordinates of the (i+m)th edge pixel along the upper and lower boundaries of the edge image;

[0019] The calculation process for the filtering threshold is expressed as follows:

[0020]

[0021] th1 = mean c +coeff c *std c ;

[0022] Where, mean c d represents the average gradient of all edge pixels. i1 std represents the edge pixels before filtering. c The variance of all edge pixels is represented by n1, the total number of edge pixels is th1, and the filtering threshold is coeff. c Indicates the first preset outlier coefficient;

[0023] The calculation process for the outlier threshold is expressed as follows:

[0024]

[0025] th2 = mean a +coeff a *std a ;

[0026] Where, mean a d represents the mean gradient of the filtered edge pixels. i2 std represents the filtered edge pixels. a The variance of the filtered edge pixels is represented by n2, the number of filtered edge pixels is represented by th2, and the outlier threshold is represented by coeff. a This represents the second preset outlier coefficient.

[0027] As a further improvement of this application, after confirming that the corresponding edge image has edge collapse, the method further includes: constructing a one-dimensional edge collapse feature map based on the edge image with edge collapse, and setting the gray value of the pixels in the region corresponding to abnormal edges and outlier pixels to 255, and setting the gray value of the pixels in the region corresponding to normal edges to 0; determining the starting and ending pixels where the edge collapse intersects with the normal edge in each edge image based on the gray value; obtaining the position information of the starting and ending pixels, including the sequence number of the edge image corresponding to the starting or ending pixel and the pixel coordinates corresponding to the starting or ending pixel; obtaining the angle range corresponding to each edge image in a pre-constructed polar coordinate system; and calculating the angle range of each edge collapse in the polar coordinate system using the position information of the starting and ending pixels and the angle range corresponding to the edge image.

[0028] As a further improvement to this application, the calculation process for the angle range of the collapsed edge in the polar coordinate system is expressed as follows:

[0029]

[0030] damageEdgeAngle s =(ID) s *imgwidth+x s resolution angle ;

[0031] damageEdgeAngle e =(ID) e *imgwidth+x e resolution angle ;

[0032] Where, resolution angle Indicates angular resolution, imgwidth represents the width of the edge image, [angle e ,angle s [damageEdgeAngle] indicates the angular range of the edge image. s damageEdgeAngle e] indicates the angle range of the edge breakage, ID s ID represents the index of the edge image corresponding to the starting pixel. e The x represents the index of the edge image corresponding to the ending pixel. s The x-coordinate represents the pixel coordinates of the starting pixel. e This indicates the pixel coordinates of the ending pixel.

[0033] To address the aforementioned technical problems, another technical solution adopted in this application is: providing a wafer edge chipping detection device, comprising: an acquisition module for acquiring all edge images of the edge of the wafer to be tested; a pixel extraction module for extracting edge pixels corresponding to the wafer edge in each edge image, distinguishing normal edges from abnormal edges based on the distribution characteristics of the edge pixels, and counting the number of abnormal edges; a gradient feature calculation module for calculating gradient features using the position of the edge pixels, analyzing the degree of pixel coordinate change based on the gradient features, marking edge pixels whose coordinate change reaches a threshold as outlier pixels, and counting the number of outlier pixels; a proportion calculation module for analyzing the proportion of abnormal wafer edge regions in each edge image to the total wafer edge region by combining the number of abnormal edges and the number of outlier pixels; and a confirmation module for confirming that the corresponding edge image has edge chipping when the proportion exceeds a preset proportion threshold.

[0034] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide a computer device, the computer device including a processor and a memory coupled to the processor, the memory storing program instructions, and when the program instructions are executed by the processor, causing the processor to perform the steps of the wafer edge detection method as described above.

[0035] To solve the above-mentioned technical problems, another technical solution adopted in this application is to provide a storage medium storing program instructions capable of implementing the wafer edge chipping detection method described above.

[0036] The beneficial effects of this application are as follows: The wafer edge chipping detection method of this application acquires all edge images of the wafer to be tested, then extracts edge pixels from the edge images to initially obtain normal edges and abnormal edges, then calculates the gradient features of the edge pixels, analyzes the degree of coordinate change based on the gradient features, and identifies pixels with drastic coordinate changes as outliers. Finally, it uses the proportion of outliers and abnormal edges in all wafer edge regions to analyze whether edge chipping exists. It has low computational load and high efficiency, and can quickly and accurately determine whether the wafer to be tested has edge chipping, meeting the needs of real-time wafer detection and improving wafer detection efficiency. Attached Figure Description

[0037] Figure 1This is a schematic flowchart of an embodiment of the wafer edge chipping detection method of the present invention;

[0038] Figure 2 This is a schematic diagram of edge image capture of an embodiment of the wafer edge chipping detection method of the present invention;

[0039] Figure 3 This is a schematic diagram of an edge image of an embodiment of the wafer edge chipping detection method of the present invention;

[0040] Figure 4 This is a schematic diagram of a normal edge in an embodiment of the wafer edge chipping detection method of the present invention;

[0041] Figure 5 This is a schematic diagram of the first type of abnormal edge in an embodiment of the wafer edge breakage detection method of the present invention;

[0042] Figure 6 This is a schematic diagram of the second type of abnormal edge in an embodiment of the wafer edge breakage detection method of the present invention;

[0043] Figure 7 This is a schematic flowchart of another embodiment of the wafer edge chipping detection method of the present invention;

[0044] Figure 8 This is a functional module schematic diagram of one embodiment of the wafer edge chipping detection device of the present invention;

[0045] Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0046] Figure 10 This is a schematic diagram of the structure of the storage medium according to an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0048] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0049] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] Figure 1 This is a schematic flowchart of the wafer edge chipping detection method according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, the method of the present invention is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, the wafer edge chipping detection method includes the following steps:

[0051] Step S1: Obtain the complete edge image of the edge of the wafer to be tested.

[0052] like Figure 2As shown, this embodiment uses a photographic camera to take 360° panoramic photos of the edge of the wafer under test, obtaining a series of edge images. These edge images are tangent or overlap to some extent, ensuring complete coverage of all wafer edge areas for subsequent inspection. Specifically, during edge image acquisition, the wafer under test is placed on a rotating stage. The parameters of the photographic camera are calibrated, and the rotation speed of the stage and the shooting frequency of the X-ray equipment are adjusted to complete the image acquisition of the wafer's edge. It should be noted that in this embodiment, the photographic camera can be a bright-field camera, a dark-field camera, or any device capable of effectively imaging the wafer edge; this embodiment is not limited to any particular device.

[0053] Step S2: Extract the edge pixels corresponding to the wafer edge in each edge image, distinguish normal edges from abnormal edges based on the distribution characteristics of the edge pixels, and count the number of abnormal edges.

[0054] Specifically, such as Figure 3 As shown, after acquiring the edge image, the edge image is preprocessed to obtain a grayscale image that is generally high-brightness with a certain thickness of low-brightness edges in the middle area. Then, the grayscale image is subjected to threshold segmentation. Edge pixels are extracted from the segmented image, and normal edges and abnormal edges are divided based on the distribution of edge pixels, and the corresponding number is counted.

[0055] Furthermore, step S2 specifically includes:

[0056] 1. Perform threshold segmentation on the edge image, and traverse each pixel of the segmented edge image to confirm the edge pixel and its position.

[0057] Specifically, this embodiment can use fixed threshold segmentation or adaptive threshold segmentation to process the image to obtain foreground and background images. When using fixed threshold segmentation, a segmentation threshold is preset based on experience, and then the pixels in the edge image are traversed according to the segmentation threshold to determine whether a pixel is foreground or background; pixels classified as foreground pixels are considered edge pixels.

[0058] Furthermore, the steps of thresholding the edge image and traversing each pixel of the segmented edge image to confirm the edge pixels and their positions specifically include:

[0059] 1.1 The edge image is segmented according to the preset adaptive threshold segmentation algorithm to separate the wafer edge region and the background region.

[0060] Specifically, in this embodiment, to ensure the accuracy of image segmentation, an adaptive threshold segmentation algorithm is preferably used to process the edge image. When using the adaptive threshold segmentation method, each pixel can be pre-defined as a target pixel, and a neighborhood of each target pixel can be constructed. Then, the segmentation threshold of each target pixel is calculated using the pixel values ​​of all pixels in the neighborhood. By comparing the pixel value of the target pixel with the segmentation threshold, it is determined whether the target pixel is foreground or background. Pixels classified as foreground pixels, i.e. edge pixels, form the wafer edge region, while pixels classified as background pixels form the background region.

[0061] 1.2. Traverse the grayscale value of each pixel in the edge image in the column direction. When there are multiple consecutive pixels with a grayscale value of 0, take the first pixel with a grayscale value of 0 as the edge pixel and record the position of the edge pixel.

[0062] Specifically, after thresholding, the grayscale value of the wafer edge region in the image is set to 0, while the grayscale value of the background region is set to 255. Figure 3 It can be seen that when capturing edge images, the overall direction of the wafer edge region in the edge image is distributed along the left and right direction of the image, presenting a strip-shaped region. Therefore, when traversing the edge image in the column direction, when the gray value of a pixel changes from high brightness to low brightness (i.e., the gray value changes from 255 to 0), and there are multiple consecutive pixels with a gray value of 0, it can be confirmed that an edge pixel has been traversed. The first pixel with a gray value of 0 is marked as an edge pixel, and the coordinate position of each edge pixel in the edge image is recorded.

[0063] 2. Based on the position of the edge pixels, each edge pixel is divided into normal pixels or abnormal pixels.

[0064] 3. Distinguish between normal and abnormal edges based on normal and abnormal pixels, and count the number of abnormal edges.

[0065] It should be noted that there are three possible scenarios at the edge of a wafer, as follows: Figure 4 , Figure 5 , Figure 6 As shown. Figure 4 This diagram illustrates a normal edge, where edge pixels are located in the middle region of the image. Figure 5 The diagram illustrates the first type of abnormal edge, where some edge pixels are closer to the lower boundary of the image due to edge collapse. Figure 6 The diagram illustrates the second type of abnormal edge, where edge pixels are discontinuous, exhibiting a break-in phenomenon. Based on this, this embodiment constructs the criteria for determining normal and abnormal pixels, as follows:

[0066] Normal pixels on a normal edge satisfy both position and continuity conditions, while abnormal pixels on an abnormal edge do not satisfy either position or continuity conditions.

[0067] Positional conditions are expressed as:

[0068] y center -h≤y i ≤y center +h;

[0069] Among them, y center The y-coordinate of the edge pixel represents the median value, and h represents the offset. i This represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image;

[0070] The continuity condition is expressed as:

[0071]

[0072] Among them, l ij L represents the distance between adjacent edge pixels i and j, L is the total distance between all edge pixels, and T represents the preset ratio threshold.

[0073] Specifically, in this embodiment, the distribution of edge pixels can be obtained by their positions, and the distribution of edge pixels can be used to determine whether an edge pixel is a normal pixel or an abnormal pixel. Finally, the number of abnormal edges is determined based on the distribution of normal and abnormal pixels.

[0074] Step S3: Calculate gradient features using the positions of edge pixels, analyze the gradient features, and obtain the number of outlier pixels in each edge image.

[0075] It should be noted that the gradient reflects the local rate of change of pixel values. The gradient distribution of normal edges is relatively uniform, while the gradient in chipped edges will show outliers (such as sudden increases or decreases). Wafer chipping usually causes abrupt changes in edge shape (such as fractures, sudden changes in depth, etc.), and the gradient value changes in these areas will be significantly higher than the gradual changes in normal edges. Therefore, in this embodiment, after segmenting the edge pixels, the gradient feature of each edge pixel is calculated based on its position. By analyzing the gradient features, outlier pixels are screened out from the edge pixels.

[0076] Furthermore, step S3 specifically includes:

[0077] 1. Using the direction perpendicular to the upper and lower boundaries of the edge image as the reference direction, calculate the gradient value of each edge pixel in each edge image using the position of the edge pixel.

[0078] The gradient value calculation process is expressed as follows:

[0079] d i =y i+m -y i ;

[0080] Where, d i y represents the gradient value of the i-th edge pixel. i The coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image are represented by y, m represents the scale range for calculating the gradient value, and y represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image. i+m This represents the coordinates of the (i+m)th edge pixel along the upper and lower boundaries of the edge image.

[0081] 2. Calculate the filtering threshold using the gradient value, and filter the edge pixels in the corresponding edge image based on the filtering threshold.

[0082] The calculation process for the filtering threshold is as follows:

[0083]

[0084] th1 = mean c +coeff c *std c ;

[0085] Where, mean c d represents the average gradient of all edge pixels. i1 std represents the edge pixels before filtering. c The variance of all edge pixels is represented by n1, the total number of edge pixels is th1, and the filtering threshold is coeff. c Coeff represents the first preset outlier coefficient. c The smaller the value, the higher the sensitivity to outlier pixels; it is usually set to 6.

[0086] Specifically, by calculating the filtering threshold, edge pixels are filtered using the filtering threshold, thereby initially filtering out gradient anomalies caused by image noise or minor fluctuations.

[0087] 3. Calculate the outlier threshold based on the gradient values ​​of the filtered edge pixels, identify the outlier pixels in each edge image based on the outlier threshold, and obtain the number of outlier pixels.

[0088] The calculation process for the outlier threshold is as follows:

[0089]

[0090] th2 = mean a +coeffa a *std a ;

[0091] Where, mean a d represents the mean gradient of the filtered edge pixels. i2 std represents the filtered edge pixels. a The variance of the filtered edge pixels is represented by n2, the number of filtered edge pixels is represented by th2, and the outlier threshold is represented by coeff. a This represents the second preset outlier coefficient.

[0092] Specifically, after coarsely filtering the edge pixels, the outlier threshold is calculated using the filtered edge pixels. The outlier threshold is then used to accurately determine the filtered edge pixels, thereby identifying outlier pixels from the edge pixels.

[0093] Step S4: Analyze the proportion of abnormal wafer edge regions in each edge image to the total wafer edge regions by combining the number of normal edges, the number of abnormal edges, and the number of outlier pixels.

[0094] The calculation process for the proportion of abnormal wafer edge regions to the total wafer edge regions is expressed as follows:

[0095]

[0096] Where R represents the proportion, count outliera The count represents the number of outlier pixels. AbnEdge The count represents the number of anomalous edges (including both Type I and Type II anomalous edges). NEdges This indicates the number of normal edges.

[0097] Step S5: When the proportion exceeds the preset proportion threshold, confirm that the corresponding edge image has edge breakage.

[0098] Specifically, the preset weight threshold is set in advance. The smaller the preset weight threshold, the higher the sensitivity to abnormal edge points. It is usually set to 0.05. By comparing the proportion of abnormal wafer edge regions to the total wafer edge regions with the preset weight threshold, it can be confirmed whether there is edge chipping in the edge image.

[0099] The wafer edge chipping detection method in this embodiment acquires all edge images of the wafer under test, then extracts edge pixels from the edge images to obtain preliminary normal edge information and abnormal edge information, then calculates the gradient features of the edge pixels, and finally uses the gradient features, normal edge information, and abnormal edge information to analyze whether edge chipping exists. It has low computational load and high efficiency, and can quickly and accurately determine whether the wafer under test has edge chipping, meeting the needs of real-time wafer detection and improving wafer detection efficiency.

[0100] Furthermore, in order to further locate the position information of the chipped edge, based on the above embodiments, other embodiments, such as... Figure 7 As shown, after step S5, the following steps are also included:

[0101] Step S6: Construct a one-dimensional edge collapse feature map based on the edge image with edge collapse, and set the gray value of the pixels in the region corresponding to abnormal edges and outlier pixels in the edge collapse feature map to 255, and set the gray value of the pixels in the region corresponding to normal edges to 0.

[0102] Specifically, the edge pixels in the edge image are divided into columns, and each column of edge pixels is replaced by a reference pixel. If there are pixels corresponding to abnormal edges or outliers in a column of edge pixels, the gray value of the reference pixel corresponding to the edge pixels in that column is set to 255. If all the edge pixels in that column are pixels corresponding to normal edges, the gray value of the reference pixel corresponding to the edge pixels in that column is set to 0, thereby constructing a one-dimensional collapsing edge feature map.

[0103] Step S7: Determine the starting and ending pixels where the broken edge intersects with the normal edge in each edge image based on the grayscale value.

[0104] Specifically, after constructing the one-dimensional edge collapse feature map, the search is performed sequentially from left to right or from right to left until a series of reference pixels with a gray value of 255 are found. The first pixel in the series of reference pixels is taken as the starting pixel and the last pixel is taken as the ending pixel.

[0105] Step S8: Obtain the position information of the starting pixel and the ending pixel. The position information includes the sequence number of the edge image corresponding to the starting pixel or the ending pixel and the pixel coordinates corresponding to the starting pixel or the ending pixel.

[0106] Step S9: Obtain the angle range corresponding to each edge image in the pre-constructed polar coordinate system.

[0107] Specifically, the angular range of the edge image is represented as [angle]. e ,angle s The polar coordinate system is constructed with the center point of the wafer as the origin.

[0108] Step S10: Calculate the angle range of each broken edge in the polar coordinate system using the position information of the starting and ending pixels and the angle range corresponding to the edge image.

[0109] The calculation process for the angular range of the collapsed edge in the polar coordinate system is expressed as follows:

[0110]

[0111] damageEdgeAngle s =(ID) s *imgwidth+x s )*resouktion angle ;

[0112] damageEdgeAngle e =(ID) e *imgwidth+x e resolution angle ;

[0113] Where, resolution angle Indicates angular resolution, imgwidth represents the width of the edge image, [angle e ,angle s [damageEdgeAngle] indicates the angular range of the edge image. s damageEdgeAngle e ] indicates the angle range of the edge breakage, ID s ID represents the index of the edge image corresponding to the starting pixel. e The x represents the index of the edge image corresponding to the ending pixel. s The x-coordinate represents the pixel coordinates of the starting pixel. e This indicates the pixel coordinates of the ending pixel.

[0114] This embodiment converts the edge image into a one-dimensional edge collapse feature map. Based on the one-dimensional edge collapse feature map, the start and end positions of the edge collapse are determined. Then, by combining the position of each edge image in the polar coordinate system, as well as the positions of the start and end positions of the edge collapse in the edge image, the coordinate position of the edge collapse in the polar coordinate system can be calculated, helping users to quickly locate the area where the edge collapse is located and the size of the edge collapse range.

[0115] Figure 8 This is a schematic diagram of the functional modules of the wafer edge chipping detection device according to an embodiment of the present invention. Figure 8 As shown, the wafer edge chipping detection device 20 includes: an acquisition module 21, a pixel extraction module 22, a gradient feature calculation module 23, a specific gravity calculation module 24, and a confirmation module 25.

[0116] The acquisition module 21 is used to acquire the complete edge image of the edge of the wafer under test;

[0117] The pixel extraction module 22 is used to extract the edge pixels corresponding to the wafer edge in each edge image, and to distinguish normal edges and abnormal edges based on the distribution characteristics of the edge pixels, and to count the number of abnormal edges.

[0118] The gradient feature calculation module 23 is used to calculate gradient features using the position of edge pixels, analyze the degree of pixel coordinate change based on the gradient features, mark edge pixels whose coordinate change reaches a threshold as outlier pixels, and count the number of outlier pixels.

[0119] The proportion calculation module 24 is used to analyze the proportion of abnormal wafer edge regions in each edge image to the total wafer edge regions by combining the number of abnormal edges and the number of outlier pixels;

[0120] The confirmation module 25 is used to confirm that there is edge chipping in the corresponding edge image when the proportion exceeds the preset proportion threshold.

[0121] Optionally, the pixel extraction module 22 performs the following operations: extracting the edge pixels corresponding to the wafer edge in each edge image, distinguishing normal edges from abnormal edges based on the distribution characteristics of the edge pixels, and counting the number of abnormal edges. Specifically, this includes: performing threshold segmentation on the edge image, traversing each pixel of the segmented edge image to confirm the edge pixels and their positions; classifying each edge pixel as a normal pixel or an abnormal pixel based on its position; distinguishing normal edges from abnormal edges based on normal pixels and abnormal pixels, and counting the number of abnormal edges.

[0122] Optionally, the pixel extraction module 22 performs threshold segmentation on the edge image and traverses each pixel of the segmented edge image to confirm the edge pixel and its position. Specifically, this includes: segmenting the edge image according to a preset adaptive threshold segmentation algorithm to segment the wafer edge region and the background region; traversing the gray value of each pixel of the edge image in the column direction, and when there are multiple consecutive pixels with a gray value of 0, taking the first pixel with a gray value of 0 as the edge pixel and recording its position.

[0123] Optionally, normal pixels of normal edges satisfy position and continuity conditions, while abnormal pixels of abnormal edges do not satisfy position or continuity conditions.

[0124] Positional conditions are expressed as:

[0125] y center -h≤y i ≤y center +h;

[0126] Among them, y center The y-coordinate of the edge pixel represents the median value, and h represents the offset. i This represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image;

[0127] The continuity condition is expressed as:

[0128]

[0129] Among them, l ij L represents the distance between adjacent edge pixels i and j, L is the total distance between all edge pixels, and T represents the preset ratio threshold.

[0130] Optionally, the gradient feature calculation module 23 performs the following operations: calculating gradient features using the positions of edge pixels, analyzing the degree of pixel coordinate change based on the gradient features, marking edge pixels whose coordinate change reaches a threshold as outliers, and counting the number of outliers. Specifically, this includes: using the direction perpendicular to the upper and lower boundaries of the edge image as a reference direction, calculating the gradient value of each edge pixel in each edge image using the position of the edge pixels; calculating a filtering threshold using the gradient value, and filtering the edge pixels in the corresponding edge image based on the filtering threshold; calculating an outlier threshold based on the gradient value of the filtered edge pixels, confirming the outliers in each edge image based on the outlier threshold, and obtaining the number of outliers.

[0131] Optionally, the gradient value calculation process is expressed as follows:

[0132] d i =y i+m -y i ;

[0133] Where, d i y represents the gradient value of the i-th edge pixel. i The coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image are represented by y, m represents the scale range for calculating the gradient value, and y represents the coordinates of the i-th edge pixel along the upper and lower boundaries of the edge image. i+m This represents the coordinates of the (i+m)th edge pixel along the upper and lower boundaries of the edge image;

[0134] The calculation process for the filtering threshold is expressed as follows:

[0135]

[0136] th1 = mean c +coeff c *std c ;

[0137] Where, mean c d represents the average gradient of all edge pixels. i1 std represents the edge pixels before filtering. c The variance of all edge pixels is represented by n1, the total number of edge pixels is th1, and the filtering threshold is coeff.c Indicates the first preset outlier coefficient;

[0138] The calculation process for the outlier threshold is expressed as follows:

[0139]

[0140] th2 = mean a +coeff a *std a ;

[0141] Where, mean a d represents the mean gradient of the filtered edge pixels. i2 std represents the filtered edge pixels. a The variance of the filtered edge pixels is represented by n2, the number of filtered edge pixels is represented by th2, and the outlier threshold is represented by coeff. a This represents the second preset outlier coefficient.

[0142] Optionally, after the confirmation module 25 performs the operation of confirming that the corresponding edge image has a broken edge, it is further configured to: construct a one-dimensional broken edge feature map based on the edge image with broken edges, and set the gray value of the pixels in the region corresponding to the abnormal edge and outlier pixels in the broken edge feature map to 255, and set the gray value of the pixels in the region corresponding to the normal edge to 0; determine the starting pixel and ending pixel of the intersection between the broken edge and the normal edge in each edge image based on the gray value; obtain the position information of the starting pixel and ending pixel, the position information including the sequence number of the edge image corresponding to the starting pixel or ending pixel and the pixel coordinates corresponding to the starting pixel or ending pixel; obtain the angle range corresponding to each edge image in the pre-constructed polar coordinate system; and calculate the angle range of each broken edge in the polar coordinate system using the position information of the starting pixel and ending pixel and the angle range corresponding to the edge image.

[0143] Optionally, the calculation process for the angular range of the collapsed edge in the polar coordinate system is expressed as follows:

[0144]

[0145] damageEdgeAngle s =(ID) s *imgwidth+x s resolution angle ;

[0146] damageEdgeAngle e =(ID) e *imgwidth+x e resolutionangle ;

[0147] Where, resolution angle Indicates angular resolution, imgwidth represents the width of the edge image, [angle e ,angle s [damageEdgeAngle] indicates the angular range of the edge image. s damageEdgeAngle e ] indicates the angle range of the edge breakage, ID s ID represents the index of the edge image corresponding to the starting pixel. e The x represents the index of the edge image corresponding to the ending pixel. s The x-coordinate represents the pixel coordinates of the starting pixel. e This indicates the pixel coordinates of the ending pixel.

[0148] For other details regarding the implementation techniques of each module in the wafer edge breakage detection device of the above embodiments, please refer to the description in the wafer edge breakage detection method of the above embodiments, which will not be repeated here.

[0149] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0150] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Figure 9 As shown, the computer device 30 includes a processor 31 and a memory 32 coupled to the processor 31. The memory 32 stores program instructions. When the program instructions are executed by the processor 31, the processor 31 performs the wafer edge chipping detection method steps described in any of the above embodiments.

[0151] The processor 31 can also be referred to as a Central Processing Unit (CPU). The processor 31 may be an integrated circuit chip with signal processing capabilities. The processor 31 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0152] See Figure 10 , Figure 10 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present invention. The storage medium of this embodiment stores program instructions 41 capable of implementing the aforementioned wafer edge chipping detection method. These program instructions 41 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or computer devices such as computers, servers, mobile phones, and tablets.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed computer devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0154] Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for detecting wafer edge chipping, characterized in that, It includes: Obtain the complete edge image of the edge of the wafer under test; The edge pixels corresponding to the wafer edge in each edge image are extracted, and normal edges and abnormal edges are distinguished based on the distribution characteristics of the edge pixels, and the number of abnormal edges is counted. Gradient features are calculated using the positions of edge pixels, and the degree of pixel coordinate change is analyzed based on the gradient features. Edge pixels whose coordinate change reaches a threshold are marked as outliers, and the number of outliers is counted. The proportion of abnormal wafer edge regions in each edge image to the total wafer edge regions is analyzed by combining the number of abnormal edges and the number of outlier pixels. When the proportion exceeds a preset proportion threshold, it is confirmed that the corresponding edge image has edge chipping. The process involves extracting edge pixels corresponding to the wafer edge in each edge image, distinguishing between normal and abnormal edges based on the distribution characteristics of these edge pixels, and counting the number of abnormal edges. The edge image is segmented using a threshold, and each pixel of the segmented edge image is traversed to confirm the edge pixels and their positions. Each edge pixel is divided into a normal pixel or an abnormal pixel based on its position. Based on the normal pixels and the abnormal pixels, normal edges and abnormal edges are distinguished, and the number of abnormal edges is counted; Normal pixels on a normal edge satisfy both the position condition and the continuity condition, while abnormal pixels on an abnormal edge do not satisfy either the position condition or the continuity condition. The location condition is expressed as follows: ； in, Represents edge pixels Coordinate median, Indicates the offset. Indicates the first The coordinates of each edge pixel along the upper and lower boundaries of the edge image; The continuity condition is expressed as: ； in, Indicates the adjacent first The edge pixel and the first The distance between edge pixels The total distance between all edge pixels This indicates the preset percentage threshold.

2. The wafer edge chipping detection method according to claim 1, characterized in that, The step of thresholding the edge image and traversing each pixel of the segmented edge image to confirm the edge pixels and their positions includes: The edge image is segmented according to a preset adaptive threshold segmentation algorithm to separate the wafer edge region and the background region; The grayscale value of each pixel in the edge image is traversed sequentially in the column direction. When there are multiple consecutive pixels with a grayscale value of 0, the first pixel with a grayscale value of 0 is taken as the edge pixel and its position is recorded.

3. The wafer edge chipping detection method according to claim 1, characterized in that, The process involves calculating gradient features based on the positions of edge pixels, analyzing the degree of pixel coordinate change based on these gradient features, marking edge pixels whose coordinate changes reach a threshold as outliers, and counting the number of outliers. Using the direction perpendicular to the upper and lower boundaries of the edge image as a reference direction, the gradient value of each edge pixel in each edge image is calculated using the position of the edge pixel. The filtering threshold is calculated using the gradient value, and edge pixels in the corresponding edge image are filtered based on the filtering threshold. The outlier threshold is calculated based on the gradient value of the filtered edge pixels, and the outlier pixels in each edge image are identified based on the outlier threshold, and the number of outlier pixels is obtained.

4. The wafer edge chipping detection method according to claim 3, characterized in that, The process of calculating the gradient value is expressed as follows: ； in, Indicates the first The gradient value of each edge pixel. Indicates the first The coordinates of each edge pixel along the upper and lower boundaries of the edge image. Indicates the scale range for calculating gradient values. Indicates the first The coordinates of each edge pixel along the upper and lower boundaries of the edge image; The calculation process for the filtering threshold is expressed as follows: ； ； ； in, This represents the mean gradient of all edge pixels. This represents the edge pixels before filtering. This represents the variance of all edge pixels. This represents the total number of edge pixels. Indicates the filtering threshold. Indicates the first preset outlier coefficient; The calculation process for the outlier threshold is expressed as follows: ； ； ； in, This represents the mean gradient value of the edge pixels after filtering. This represents the filtered edge pixels. This represents the variance value of the filtered edge pixels. This indicates the number of edge pixels after filtering. Indicates the outlier threshold. This represents the second preset outlier coefficient.

5. The wafer edge chipping detection method according to claim 1, characterized in that, After confirming that the corresponding edge image has chipped edges, the process also includes: A one-dimensional edge-collapse feature map is constructed based on the edge image with edge collapse. In the edge-collapse feature map, the gray value of the pixels in the region corresponding to abnormal edges and outlier pixels is set to 255, and the gray value of the pixels in the region corresponding to normal edges is set to 0. Based on the grayscale value, determine the starting and ending pixel points where the broken edge intersects with the normal edge in each edge image; Obtain the position information of the starting pixel and the ending pixel, wherein the position information includes the sequence number of the edge image corresponding to the starting pixel or the ending pixel and the pixel coordinates corresponding to the starting pixel or the ending pixel; Obtain the angle range corresponding to each edge image in the pre-constructed polar coordinate system; The angle range of each broken edge in the polar coordinate system is calculated using the position information of the starting pixel and the ending pixel, as well as the angle range corresponding to the edge image.

6. The wafer edge chipping detection method according to claim 5, characterized in that, The calculation process for the angle range of the edge breakage in the polar coordinate system is expressed as follows: ； ； ； in, Indicates angular resolution. Indicates the width of the edge image, [ [Indicates the angular range of the edge image] ， Indicates the range of angles of edge breakage. This indicates the index of the edge image corresponding to the starting pixel. This indicates the index of the edge image corresponding to the ending pixel. This represents the pixel coordinates of the starting pixel. This indicates the pixel coordinates of the ending pixel.

7. A computer device, characterized in that, The computer device includes a processor and a memory coupled to the processor, the memory storing program instructions that, when executed by the processor, cause the processor to perform the steps of the wafer edge chipping detection method as described in any one of claims 1-6.

8. A storage medium, characterized in that, The device stores program instructions capable of implementing the wafer edge chipping detection method as described in any one of claims 1-6.

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