Detection method, detection device and storage medium for wafer

Abstract

Embodiments of the present application provide a wafer detection method, detection device, and storage medium. In some embodiments of the present application, the detection method for a wafer includes: determining image feature values ​​of pixels of a side image of the wafer; performing statistics on the image feature values ​​of pixels of the side image; and determining whether the outline of the wafer is abnormal based on the statistical results. The wafer detection method, detection device, and storage medium provided in the present application can perform abnormality detection on a wafer.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of semiconductor technology, and more specifically, to a detection method, a detection device, and a storage medium for a wafer. Background Art

[0002] Wafer refers to the silicon chip used to make silicon semiconductor integrated circuits. It is the carrier used to produce integrated circuits. Therefore, the quality of the wafer directly affects the yield rate and manufacturing cost of the chip. In the actual process of making wafers, it is inevitable that some wafers will have defects, such as gaps caused by cracks on the edge of the wafer. Therefore, it is necessary to test the wafers to obtain wafers that meet the standards.

[0003] Currently, most engineers usually use naked eye observation to find wafer defects when inspecting wafers. However, this manual inspection method greatly reduces the inspection efficiency and accuracy. Summary of the invention

[0004] The embodiments of the present application provide a detection method, a detection device, and a storage medium for a wafer that can at least partially solve the above-mentioned problems existing in the prior art.

[0005] On the one hand, an embodiment of the present application provides a detection method for a wafer, including: determining image feature values ​​of pixels of a side image of the wafer; performing statistics on the image feature values ​​of pixels of the side image; and determining whether a contour of the wafer is abnormal based on the statistical results.

[0006] In some embodiments of the present application, statistics on the image feature values ​​of the side image include: respectively calculating the sum or mean of the image feature values ​​of pixels in each row and / or column of the pixel array of the side image to obtain the image feature value of each row and / or column of the pixel array.

[0007] In some embodiments of the present application, determining whether the wafer contour is abnormal according to the statistical results includes: in response to the statistical results indicating that there are abnormal values ​​in the image feature values ​​of the rows and / or columns, determining that the wafer contour is abnormal.

[0008] In some embodiments of the present application, after determining that there is an abnormality in the wafer contour in response to the presence of an abnormal value in the image feature value of the statistical result indication column, the method further includes: determining the location of the wafer contour abnormality according to the column corresponding to the abnormal value.

[0009] In some embodiments of the present application, the method also includes: determining the longitudinal length of the contour abnormal area based on the pixels with abnormal image feature values ​​in the column corresponding to the abnormal value; determining the defect depth of the wafer based on the determined longitudinal length; and determining that there is a notch defect in the wafer in response to the defect depth being within a preset abnormal depth range.

[0010] In some embodiments of the present application, before determining whether the profile of the wafer is abnormal based on the statistical results, the method further includes: smoothing the image feature values ​​of the rows and / or columns.

[0011] In some embodiments of the present application, before determining the image feature values ​​of pixels of the side image of the wafer, the method also includes: acquiring the original side image of the wafer; performing grayscale processing on the original side image; and obtaining the side image based on the original side image after grayscale processing.

[0012] In some embodiments of the present application, obtaining the side image according to the original side image after grayscale processing includes: performing binarization processing on the original side image after grayscale processing to obtain the side image.

[0013] In some embodiments of the present application, a notch for positioning is provided on the wafer, and the method further includes: removing the image area corresponding to the notch for positioning of the wafer in the original side image by cropping the original side image.

[0014] In some embodiments of the present application, the image characteristic value includes a color value or a brightness value.

[0015] On the other hand, an embodiment of the present application provides a detection device for a wafer, including: a determination module for determining image feature values ​​of pixels of a side image of the wafer; a statistical module for performing statistics on the image feature values ​​of pixels of the side image; and a detection module for determining whether the contour of the wafer is abnormal based on the statistical results.

[0016] In some embodiments of the present application, the statistical module is configured to: calculate the sum or mean of the image feature values ​​of pixels in each row and / or column of the pixel array of the side image respectively, to obtain the image feature value of each row and / or column of the pixel array.

[0017] In some embodiments of the present application, the detection module is configured to: in response to the statistical result indicating that there are abnormal values ​​in the image feature values ​​of the row and / or column, determine that there is an abnormality in the contour of the wafer.

[0018] In some embodiments of the present application, the detection module is further configured to: in response to the presence of an abnormal value in the image feature value of the column indicating the statistical result, after determining that there is an abnormality in the wafer contour, determine the location of the wafer contour abnormality according to the column corresponding to the abnormal value.

[0019] In some embodiments of the present application, the detection module is further configured to: determine the longitudinal length of the contour abnormal area based on the pixels with abnormal image feature values ​​in the column corresponding to the abnormal value; determine the defect depth of the wafer based on the determined longitudinal length; and determine that there is a notch defect in the wafer in response to the defect depth being within a preset abnormal depth range.

[0020] In some embodiments of the present application, the detection device further includes: an alarm module for feeding back alarm information in response to an abnormality in the contour of the wafer or a notch defect in the wafer.

[0021] An embodiment of the present application further provides a detection device, comprising: a memory for storing computer instructions; and a processor for communicating with the memory to execute the computer instructions, thereby implementing the detection method mentioned in the above embodiment.

[0022] The embodiments of the present application further provide a readable storage medium, in which computer instructions are stored. When the computer instructions are executed by a processor, the detection method mentioned in the above embodiments is implemented.

[0023] According to the implementation mode of the present application, since the image feature values ​​of pixels in the abnormal area of ​​the wafer contour in the side image of the wafer are different from the image feature values ​​of pixels in the normal area, the detection device can analyze whether there is any abnormality in the wafer contour by statistically analyzing the image feature values ​​of the pixels in the side image of the wafer, thereby realizing automatic detection of wafer contour abnormalities, which improves detection efficiency and accuracy compared to manual detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings, in which:

[0025] Figure 1 is a flow chart of a method for detecting a wafer according to some embodiments of the present application;

[0026] Figure 2 is a schematic top view of a wafer according to some embodiments of the present application;

[0027] Figure 3 yes Figure 2 A local enlarged schematic diagram of area A in FIG.

[0028] Figure 4 is a side schematic diagram of a wafer according to some embodiments of the present application;

[0029] Figure 5 is a side schematic diagram of a wafer according to another embodiment of the present application;

[0030] Figure 6 is a schematic diagram of an original side image according to some embodiments of the present application;

[0031] Figure 7 is a schematic side image diagram according to some embodiments of the present application;

[0032] Figure 8 is a schematic diagram of a change trend of image characteristic values ​​of each column of a pixel array of a side image according to some embodiments of the present application;

[0033] Fig. 9 is a schematic block diagram of a detection device for a wafer according to an exemplary embodiment of the present application;

[0034] Fig.10 is a schematic block diagram of a detection device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to better understand the present application, a more detailed description will be made of various aspects of the present application with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application, and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] It should be noted that in this specification, the expressions first, second, third, etc. are only used to distinguish one feature from another feature region, but do not represent any limitation on the features, and especially do not represent any order of precedence.

[0037] In the drawings, the thickness, size and shape of the components have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are used as terms of approximation, not as terms of degree, and are intended to account for the inherent deviations in measurements or calculations that would be recognized by one of ordinary skill in the art.

[0038] It should also be understood that expressions such as "include", "comprising", "having", "including" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or their combinations. In addition, when describing the embodiments of the present application, the use of "may" means "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0039] Unless otherwise specified, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that, unless clearly stated in this application, words defined in common dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. In addition, unless explicitly limited or contradictory to the context, the specific steps included in the method described in the present application are not necessarily limited to the order described, but can be performed in any order or in parallel. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0041] Figure 1 1 is a flow chart of a method 1000 for detecting a wafer according to some embodiments of the present application, which can be performed, for example, by a detection device. Figure 1 As shown, the present application provides a wafer detection method 1000 including:

[0042] S11, determining image feature values ​​of pixels of the side image of the wafer.

[0043] S12, performing statistics on the image feature values ​​of the pixels of the side image.

[0044] S13, determining whether the wafer profile is abnormal based on the statistical results.

[0045] According to the implementation mode of the present application, since the image feature values ​​of pixels in the abnormal area of ​​the wafer contour in the side image of the wafer are different from the image feature values ​​of pixels in the normal area, the detection device can analyze whether there is any abnormality in the wafer contour by statistically analyzing the image feature values ​​of the pixels in the side image of the wafer, thereby realizing automatic detection of wafer contour abnormalities, which improves detection efficiency and accuracy compared to manual detection.

[0046] For ease of understanding, each step of the wafer inspection method 1000 is exemplarily described below.

[0047] Step S11

[0048] Figure 2 is a schematic top view of a wafer 20 according to some embodiments of the present application, Figure 3 yes Figure 2 A partial enlarged schematic diagram of area A in FIG. Figure 4 is a side schematic diagram of a wafer 20 according to some embodiments of the present application.

[0049] In some embodiments of the present application, the side image of the wafer may be a circumferential image of the wafer. For example, the original side image of the wafer is obtained by photographing the wafer from the side and rotating around the wafer 20 for one circle. The photographing area is focused on the edge of the wafer, so that the side image of the wafer can better highlight the shape of the edge of the wafer, which is conducive to the detection device to detect the contour abnormality of the wafer.

[0050] In some embodiments of the present application, the side image of the wafer can also be a side image of the top oblique angle or the bottom oblique angle of the wafer. Among them, the top oblique angle may refer to the angle of view tilted from the top angle to the side, and the bottom oblique angle may refer to the angle of view tilted from the bottom angle to the side. For example, if the angle of view roughly perpendicular to the top surface of the wafer is 0 degrees, the viewing angle range of the top oblique angle may be (0°, 90°), and the viewing angle range of the bottom oblique angle may be (90°, 180°). By shooting the side image of the top oblique angle of the wafer, so that the image of the top of the wafer is included in the side image shot, the difference between the image of the defect and the image of the normal area of ​​the wafer can be highlighted, which is conducive to the detection device to detect the abnormality of the wafer profile.

[0051] In some embodiments of the present application, the image feature value of a pixel may be, for example, a color value (such as an RGB value or a grayscale value) of the pixel or a brightness value of the pixel. For example, due to a notch defect in the wafer, the wafer profile is abnormal. Figures 2 to 4 It can be seen that the image area corresponding to the wafer notch (such as Figure 4 The color or brightness of area B and area C) is different from the color or brightness of other areas, the color of the image area corresponding to the notch is black, the color around the notch can be, for example, the color of the wafer body, and the brightness of the image area corresponding to the notch is lower than the brightness around the notch. The detection device can detect whether the wafer has defects by counting the color or brightness of each pixel in the side image.

[0052] In some embodiments of the present application, a notch for positioning is provided on the wafer, and the detection device can obtain the original side image, and by cropping the original side image, remove the image area in the original side image corresponding to the notch for positioning of the wafer. In order to facilitate the detection device and / or other devices for the wafer to correct the position of the wafer, a notch for positioning is usually provided at a fixed position of the wafer so that the detection device and / or other devices for the wafer can position the wafer. The detection device can reduce the impact of the notch for positioning on the detection of wafer contour anomalies by cropping the original side image and removing the image area corresponding to the notch for positioning.

[0053] For ease of understanding, the following is an exemplary description of a method for removing an image region corresponding to a notch used for positioning.

[0054] For example, the method of acquiring the original side image includes: detecting the position of the notch for positioning of the wafer through a predefined notch detection algorithm for positioning; starting from the position of the notch for positioning of the wafer, taking images around the wafer to obtain the original side image.

[0055] Figure 5 is a schematic side view of a wafer according to another embodiment of the present application. The left region (region D) and the right region (region E) of the original side image of the wafer contain images of the notch used for positioning.

[0056] As an example, the detection device can determine the size of the notch used for positioning in the x direction (i.e., the horizontal length) in the original side image based on the approximate range of the actual width of the notch used for positioning on the wafer along the circumferential direction of the wafer given by the detector and the actual length represented by each pixel in the image captured by the device that captured the original side image. Since the original side image is captured starting from the position of the notch used for positioning, the image area corresponding to the notch used for positioning is located in the left area (also referred to as the starting shooting area) and the right area (also referred to as the ending shooting area) of the original side image, the detection device can cut the image area with a width of at least the determined horizontal length of the notch used for positioning from the left area and the right area respectively.

[0057] As another example, the detection device can determine the size of the notch used for positioning in the x direction (i.e., the horizontal length) in the original side image based on the approximate range of the actual width of the notch used for positioning on the wafer along the circumferential direction of the wafer given by the detector, and the actual length represented by each pixel in the image captured by the device that captured the original side image. The detection device can determine the size of the notch used for positioning in the y direction (i.e., the longitudinal length) in the original side image based on the approximate range of the length of the notch used for positioning on the wafer along the radial direction of the wafer given by the detector, the shooting angle, and the actual length represented by each pixel in the image captured by the device that captured the original side image. The detection device can determine the image area in the original side image corresponding to the notch used for positioning based on the determined horizontal and longitudinal lengths of the notch used for positioning in the original side image, and cut off the part of the image area.

[0058] It should be understood that, without departing from the teachings of the present application, the predefined gap detection algorithm for positioning can be set according to the morphological features of the gap used for positioning, the image features around the gap used for positioning, etc., and the present application does not impose any restrictions on this.

[0059] It should be understood that, without departing from the teaching of the present application, the detection device may also lock the image area of ​​the notch used for positioning in other ways, and the present application does not impose any limitation on this.

[0060] In some embodiments of the present application, the process of the detection device acquiring the side image of the wafer may include, for example: acquiring the original side image of the wafer; grayscale processing the original side image; and obtaining the side image of the wafer according to the original side image after grayscale processing. The original side image of the wafer may be a cropped original side image or an uncropped original side image, which is not limited in the present application.

[0061] As an example, Figure 6 It is a schematic diagram of the original side image according to some embodiments of the present application. The detection device can grayscale the original side image by methods such as component method, maximum value method, average value method, weighted average method, etc. Among them, if the detection device adopts the component method, the brightness of the three RGB components of the pixels in the original side image can be used as the grayscale value of the pixels respectively, to obtain three candidate images, and select one candidate image as the original side image after grayscale processing as needed. If the detection device adopts the maximum value method, the maximum value of the brightness of the three RGB components of the pixels in the original side image can be used as the grayscale value of the pixels, so as to obtain the original side image after grayscale processing. If the detection device adopts the average value method, the average value of the brightness of the three RGB components of the pixels in the original side image can be used as the grayscale value of the pixels, so as to obtain the original side image after grayscale processing. If the detection device adopts the weighted average method, the three components can be weighted averaged with different weights according to the importance of each component in the three RGB components of the pixel or other indicators, so as to obtain the original side image after grayscale processing. For example, the detection device cuts out Figure 5 After the original side image shown in the figure is used to locate the gap area image, the cropped original side image is gray-scale processed to obtain the original side image after gray-scale processing as shown in the figure. Figure 6 As shown. Figure 6 It can be seen that the areas where the gaps other than the gap used for positioning are located (area F) in the original side image after grayscale processing are different from the image features of other areas. Figure 6 Conduct defect detection.

[0062] It should be understood that, without departing from the teaching of the present application, the grayscale processing of the original side image may be performed in other ways, and the present application does not limit this.

[0063] Optionally, the detection device may obtain the side image according to the original side image after grayscale processing, for example, including: binarizing the original side image after grayscale processing to obtain the side image of the wafer. Figure 6 After the original side image after grayscale processing is binarized, the side image is obtained. Figure 7 As shown, the defective area is area G.

[0064] As an example, the process of binarization processing performed by the detection device may include: obtaining a predetermined threshold (for example, 127, the median of 0 to 255), and for each pixel in the original side image after grayscale processing, in response to the grayscale value of the pixel being less than or equal to the threshold, changing the grayscale value of the pixel to 0 (i.e., black); in response to the grayscale value of the pixel being greater than the threshold, changing the grayscale value of the pixel to 255 (i.e., white).

[0065] As another example, the process of binarization processing performed by the detection device may, for example, include: determining the grayscale average value of each pixel in the pixel array; for each pixel in the original side image after grayscale processing, in response to the grayscale value of the pixel being less than or equal to the grayscale average value, changing the grayscale value of the pixel to 0 (i.e., black); in response to the grayscale value of the pixel being greater than the grayscale average value, changing the grayscale value of the pixel to 255 (i.e., white).

[0066] It should be understood that, without departing from the teaching of the present application, the detection device may select other methods to perform binarization processing on the image after grayscale processing, and the present application does not impose any limitation on this.

[0067] Step S12 and Step S13

[0068] In some embodiments of the present application, the way in which the detection device performs statistics on the image features of the pixels of the side image may include: respectively calculating the sum or mean of the image feature values ​​of the pixels in each row and / or column of the pixel array of the side image, to obtain the image feature value of each row and / or column of the pixel array.

[0069] Optionally, the detection device may determine whether the wafer profile is abnormal based on the statistical results, including: in response to the statistical results indicating that there are abnormal values ​​in the image feature values ​​of the rows and / or columns, determining that the wafer profile is abnormal. The abnormal value refers to the value that is significantly deviated from the other values. Whether there are abnormal values ​​in the image feature values ​​of the rows and / or columns can be detected by an abnormal value detection algorithm.

[0070] The following is an exemplary description using the image feature values ​​of a column as an example.

[0071] For example, the image feature values ​​of each column of the pixel array are calculated in a sequence from left to right or from right to left. Starting from j=1, for the image feature value of the jth column, the extreme difference between the image feature values ​​of the jth to j+Uth columns is compared to see whether it is greater than a preset value. If it is determined to be yes, it is determined that there are abnormal values ​​in the image feature values ​​of the jth to j+Uth columns. If it is determined not to be, it is determined that there are no abnormal values ​​in the image feature values ​​of the jth to j+Uth columns, and j=j+1 is set, and the step of comparing whether the extreme difference between the image feature values ​​of the jth to j+Uth columns is greater than the preset value is returned until j+U is equal to the number of columns of the pixel array. Among them, the specific values ​​of the preset value and U can be determined according to information such as the size of the side image. For example, the preset value can be a value greater than or equal to 20, and U can be a value greater than or equal to 10. After determining that there are abnormal values ​​in the image feature values ​​of the jth to j+Uth columns, the maximum value in the image feature values ​​of the jth to j+Uth columns can be used as the abnormal value. The maximum value may be a maximum value or a minimum value, and the specific situation is determined according to the processing process of the detection device on the side image.

[0072] Optionally, after detecting the abnormal value, the detection device can also determine whether there is an abnormality in the wafer contour based on the distribution of the abnormal value. For example, for the original side image after cropping, since the image area of ​​the notch used for positioning has been cropped in advance, if the detection device detects an abnormal value, it determines that there is an abnormality in the wafer contour. For the original side image that has not been cropped, since the side image contains the image area of ​​the notch used for positioning, the detection device can detect the abnormal value and determine the abnormal value distribution area. For example, in response to the number of abnormal value distribution areas being less than or equal to N, the detection device determines that the wafer contour is normal; in response to the number of abnormal value distribution areas being greater than N, it determines that the wafer contour is abnormal. Among them, the value of N can be set according to the shooting method of the original side image. For example, if the same part in the image of the starting shooting area and the image of the ending shooting area of ​​the original side image is eliminated, N is equal to 1. If the same part in the image of the starting shooting area and the image of the ending shooting area of ​​the original side image is not eliminated, the value of N is determined according to the starting shooting position. For example, if the original side image is shot from the gap used for positioning, N=2; if the original side image is shot from a position other than the gap used for positioning, N=1.

[0073] As an example, the detection device obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each row of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the row as the image feature value of the row. If there is a defect in the wafer, the image feature value of the pixel corresponding to the defect in the pixel array is different from the image feature value of the pixels around the pixel. By counting the feature values ​​of each row of pixels, if the image feature value of a row is different from the image feature value of the rows around it, it can be judged whether the pixels in the row include the pixels corresponding to the defect according to the difference between the image feature value of the row and the image feature value of the rows around it. For example, after counting the image feature values ​​of each row, if the detection device determines that the image feature difference between the i-th row and the i+1-th row is extremely large, for example, greater than the first threshold, it can be determined that the i-th row and the i+1-th row are the boundary of the wafer (for example, the intersection between the top surface and the side surface of the wafer), rather than a defect; if it is determined that the image feature difference between the i-th row and the i+1-th row is less than the first threshold, it can be determined that the outline of the wafer may be abnormal.

[0074] It should be understood that, without departing from the teaching of the present application, the first threshold value can be determined according to the processing method of the original side image in the process of the detection device obtaining the side image from the original side image, and the number of columns of the pixel array of the side image. For example, if the detection device performs grayscale processing and binarization processing on the original side image to obtain the side image, and the number of columns of the pixel array is a, then the first threshold value can be, for example, equal to 255*a*T, and T can be, for example, any value between (70%, 100%), for example, 75%, 80%, 85%, 90% and 95%, etc. The present application does not limit the first threshold value.

[0075] As another example, the detection device obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each column of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the column as the image feature value of the column. Taking the notch defect as an example, if there is no notch in the wafer, the image feature values ​​of each column in the pixel array of the side image are basically the same. If there is a notch in the wafer, usually, the notch of the wafer extends from the edge of the wafer to the inside of the wafer, so that the image feature values ​​of individual pixels in multiple rows of pixels in the side image of the wafer have a large difference from other pixels in the row. By statistically analyzing the image feature values ​​of each column of pixels in the pixel array to obtain the image feature value of the column, and comparing the image feature values ​​of each column to determine the area where the notch is located, it is possible to reduce the situation where the area where the notch is located is misidentified due to different image feature values ​​of pixels in different columns in the same row due to noise or the like. By way of example, the detection device sums the image feature values ​​of the pixels in each column of the pixel array of the side image, and obtains the change trend of the image feature values ​​of each column as shown in the figure. Figure 8As shown, Figure 8 In , the horizontal axis is the number of columns in the pixel array (j), and the vertical axis is the image feature value of the column (F). Figure 8 It can be seen that there is an abnormal value at j=K, and the detection device can determine that there is a notch in the wafer corresponding to the side image and the wafer contour is abnormal.

[0076] It should be understood that, without departing from the teachings of the present application, different methods for determining whether there are outliers can be configured for the detection device, for example, by using an outlier detection algorithm to detect whether there are outliers in the image feature values ​​of each column, and the present application does not impose any restrictions on this.

[0077] Optionally, in the process of detecting wafer contour anomalies based on the image feature values ​​of each column of the pixel array, the detection device may also determine the position of the wafer contour anomaly according to the column corresponding to the abnormal value after determining the wafer contour anomaly. As described above, since the image feature values ​​of each column in the pixel array of the side image are substantially the same, the detection device may locate the position of the wafer contour anomaly according to the column corresponding to the detected abnormal value.

[0078] Optional. After determining the column corresponding to the abnormal value, the detection device determines the longitudinal length of the contour abnormal area according to the pixels with abnormal image characteristic values ​​in the column corresponding to the abnormal value; determines the defect depth of the wafer according to the determined longitudinal length; and determines that the wafer has a notch defect in response to the defect depth being within a preset abnormal depth range. The defect depth of the wafer may refer to the length along the radial direction of the wafer, and the longitudinal length may refer to the length of the acquired side image along the Y direction.

[0079] For example, if the detection device determines that the image feature values ​​of the j1th column to the j2th column of the side image are abnormal, the pixels with abnormal image feature values ​​among the pixels of the j1th column to the j2th column of the side image are determined, and the longitudinal length of the abnormal area (i.e., the length in the y direction) is determined accordingly. For example, if the pixels with abnormal image feature values ​​are continuously distributed in the side image, the longitudinal length is equal to the difference in the number of rows from the top of the side image to the bottom of the side image between the first pixel with abnormal image feature value and the last pixel with abnormal image feature value. For example, if the pixels with abnormal image feature values ​​are pixels from the i1th row to the i2th row with abnormal image feature values, the longitudinal length can be determined to be i2-i1 (for example Figure 7 The straight line distance between the midpoint P and the point Q). If the pixels with abnormal image feature values ​​are distributed in segments in the side image, multiple segments are integrated. After the integration is completed, the segment corresponding to the defect in each integrated segment is determined according to the preset search rule, and the number of pixels in the segment corresponding to the defect is used as the longitudinal length. The integration method can be, for example: for each segment, if the number of pixels between adjacent segments is less than the preset pixel number threshold (i pre), the adjacent segments are integrated into one segment. The preset search rule is determined according to the parameters of the shooting device of the side image. For example, the search rule indicates: max row and i min The segments between the rows are taken as the segments corresponding to the defects. max and i min For example, if the pixels with abnormal image characteristic values ​​in the j1th column are pixels in the i1th row to the i2th row, and pixels in the i3th row to the i4th row, in response to i3-i2<i pre , determine the longitudinal length is i4-i1, in response to i3-i2≥i pre , i2 and i1 are in [i min ,i max ], the longitudinal length is equal to i2-i1 (for example Figure 7 The straight-line distance between the midpoint P and the point Q). After determining the longitudinal length, the detection device uses the product of the longitudinal length and the actual length represented by each pixel in the side image as the defect depth of the wafer in that column. The detection device obtains the maximum value of the defect depth of the wafer in each column, and determines whether the obtained maximum value is within the abnormal depth range. If it is determined to be, it is determined that the wafer has a notch defect. If it is determined not to be, it can be determined that the wafer has other defects. Among them, the method of distinguishing other defects can be defined according to the morphological characteristics of other defects, which are not listed here one by one.

[0080] It should be understood that, without departing from the teachings of the present application, the abnormal depth range can be determined based on wafer structural characteristics and process requirements, for example, the abnormal depth range can be (0, 0.1 mm), and the present application does not impose any restrictions on this.

[0081] As another example, the detection device obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each row of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the row as the image feature value of the row; and for each column of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the column as the image feature value of the column. The detection device can combine the pixel features of the rows and the pixel features of the columns of the pixel array of the side image to comprehensively determine whether the contour of the wafer is abnormal. For example, if the detection device determines that the wafer has defects based on the pixel features of the rows of the pixel array of the side image, or determines that the wafer has defects based on the pixel features of the columns of the pixel array of the side image, it determines that the wafer contour is abnormal. By detecting the abnormality of the wafer contour through multiple features, it can reduce the situation where the wafer with abnormal contour is misjudged as a wafer with normal contour, and reduce the probability of missed judgment.

[0082] As an option, the detection device smoothes the image feature values ​​of the rows and / or columns before determining whether the wafer profile is abnormal based on the statistical results. For example, there may be noise in the side image of the wafer. In order to reduce the influence of the noise on the statistical image feature values ​​of the rows and / or columns, the statistical image feature values ​​of the rows and / or columns may be smoothed.

[0083] It should be understood that without departing from the teaching of the present application, a smoothing algorithm for row and / or column image feature values ​​may be selected as needed, and the present application does not impose any limitation on this.

[0084] It should be understood that without departing from the teachings of the present application, the detection device may also perform statistics on the image feature values ​​of pixels based on other rules, and adjust the method of judging whether the contour of the wafer is abnormal based on the statistical rules. For example, statistics may be performed on the rows and / or columns of the pixel array of a partial area of ​​the side image, and whether the contour is abnormal may be judged based on the statistical results. The present application does not impose any restrictions on this.

[0085] In some embodiments of the present application, the detection device may also feedback warning information to the detector after determining that the wafer contour is abnormal, or after determining that the wafer has a notch defect. For example, the detection device may feedback warning information to the detector by sending warning information, turning on a warning ring, generating a detection result, and sending an email to the detector, etc., and the present application does not limit this.

[0086] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0087] Fig. 9 is a schematic block diagram of a wafer inspection device 2000 according to an exemplary embodiment of the present application.

[0088] like Fig. 9 As shown, the detection device 2000 may include, for example: a determination module 2100, a statistics module 2200, and a detection module 2300. The determination module 2100 may be used to determine the image feature values ​​of the pixels of the side image of the wafer. The statistics module 2200 may be used to count the image feature values ​​of the pixels of the side image. The detection module 2300 may be used to determine whether the profile of the wafer is abnormal based on the statistical results.

[0089] According to the implementation mode of the present application, since the image feature values ​​of pixels in the abnormal area of ​​the wafer contour in the side image of the wafer are different from the image feature values ​​of pixels in the normal area, the detection device can analyze whether there is any abnormality in the wafer contour by statistically analyzing the image feature values ​​of the pixels in the side image of the wafer, thereby realizing automatic detection of wafer contour abnormalities, which improves detection efficiency and accuracy compared to manual detection.

[0090] In some embodiments of the present application, see Figures 2 to 4 The side image of the wafer 20 may be a circumferential image of the wafer 20. For example, the side image obtained by photographing the wafer 20 from the side while rotating around the wafer 20 may focus the photographing area to the edge of the wafer, so that the side image of the wafer may highlight the shape of the edge of the wafer, which is beneficial for the detection device 2000 to detect abnormalities in the wafer profile.

[0091] In some embodiments of the present application, the side image of the wafer can also be a side image of the top oblique angle or bottom oblique angle of the wafer. Wherein, the top oblique angle may refer to the angle of view tilted from the top angle to the side, and the bottom oblique angle may refer to the angle of view tilted from the bottom angle to the side. For example, if the angle of view roughly perpendicular to the top surface of the wafer is 0 degree angle of view, the viewing angle range of the top oblique angle may be (0 °, 90 °), and the viewing angle range of the bottom oblique angle may be (90 °, 180 °). By shooting the side image of the top oblique angle of the wafer, so that the image of the top of the wafer is included in the side image shot, the difference between the image of the defect and the image of the normal area of ​​the wafer can be highlighted, which is conducive to the detection module 2300 based on the statistical results obtained by the statistical module 2200 to detect the abnormality of the wafer profile.

[0092] In some embodiments of the present application, the image feature value of a pixel may be, for example, a color value (such as an RGB value or a grayscale value) of the pixel or a brightness value of the pixel. For example, due to a notch defect in the wafer, the wafer profile is abnormal. Figures 2 to 4 It can be seen that the image area corresponding to the wafer notch (such as Figure 4 The color or brightness of area B and area C of the notch is different from the color or brightness of other areas, the color of the image area corresponding to the notch is black, the color around the notch can be, for example, the color of the wafer body, and the brightness of the image area corresponding to the notch is lower than the brightness around the notch. The detection module 2300 can detect whether the wafer has defects based on the color value or brightness value of each pixel in the side image counted by the statistics module 2200.

[0093] In some embodiments of the present application, a notch for positioning is provided on the wafer, and the detection device 2000 further includes an image acquisition module (not shown), which is configured to: acquire an original side image, and remove the image area corresponding to the notch for positioning of the wafer in the original side image by cropping the original side image. In order to facilitate the detection device 2000 and / or other devices for the wafer to correct the position of the wafer, a notch for positioning is usually provided at a fixed position of the wafer so that the detection device 2000 and / or other devices for the wafer can position the wafer. The image acquisition module can reduce the influence of the notch for positioning on the detection of wafer contour anomalies by cropping the original side image and removing the image area corresponding to the notch for positioning.

[0094] For ease of understanding, the following is an exemplary description of a method for removing an image region corresponding to a notch used for positioning.

[0095] For example, the image acquisition module acquires the original side image in the following manner: detecting the position of the notch for positioning of the wafer through a predefined notch detection algorithm for positioning; starting from the position of the notch for positioning of the wafer, taking images around the wafer to obtain the original side image.

[0096] For example, Figure 5 is a schematic side view of a wafer according to another embodiment of the present application. The left region (region D) and the right region (region E) of the original side image of the wafer contain images of the notch used for positioning.

[0097] As an example, the image acquisition module can determine the horizontal length of the gap used for positioning in the original side image based on the approximate range of the actual width of the x-direction dimension (i.e., the horizontal length) of the gap used for positioning given by the detector, and the actual length represented by each pixel in the image captured by the device that captured the original side image. Since the original side image is captured starting from the position of the gap used for positioning, the image area corresponding to the gap used for positioning is located in the left area (also referred to as the starting shooting area) and the right area (also referred to as the ending shooting area) of the original side image, and the image acquisition module can cut from the left area and the right area to the image area whose width is at least the horizontal length of the gap determined for positioning.

[0098] As another example, the detection device can determine the size of the notch used for positioning in the x-direction (i.e., the lateral length) in the original side image based on the approximate range of the actual width of the notch used for positioning on the wafer given by the detector along the circumferential direction of the wafer, and the actual length represented by each pixel in the image captured by the device that captured the original side image. The detection device can determine the size of the notch used for positioning in the y-direction (i.e., the longitudinal length) in the original side image based on the approximate range of the length of the notch used for positioning on the wafer given by the detector along the radial direction of the wafer, the shooting angle, and the actual length represented by each pixel in the image captured by the device that captured the original side image. The detection device can determine the image area in the original side image corresponding to the notch used for positioning based on the determined lateral length and longitudinal length of the notch used for positioning in the original side image, and cut out the portion of the image area.

[0099] It should be understood that, without departing from the teachings of the present application, the predefined gap detection algorithm for positioning can be set according to the morphological features of the gap used for positioning, the image features around the gap used for positioning, etc., and the present application does not impose any restrictions on this.

[0100] It should be understood that, without departing from the teaching of the present application, the image acquisition module may also lock the image area of ​​the notch used for positioning in other ways, and the present application does not impose any limitation on this.

[0101] In some embodiments of the present application, the image acquisition module is further used to grayscale the original side image; and obtain the side image according to the grayscale processed original side image. The original side image of the wafer may be a cropped image or an uncropped image, which is not limited in the present application.

[0102] As an example, the image acquisition module may perform grayscale processing on the original side image by methods such as component method, maximum value method, average value method, weighted average method, etc. Among them, if the image acquisition module adopts the component method, the brightness of the three RGB components of the pixels in the original side image may be used as the grayscale value of the pixels respectively to obtain three candidate images, and select one candidate image as the original side image after grayscale processing as needed. If the image acquisition module adopts the maximum value method, the maximum value of the brightness of the three RGB components of the pixels in the original side image may be used as the grayscale value of the pixels, thereby obtaining the original side image after grayscale processing. If the image acquisition module adopts the average value method, the average value of the brightness of the three RGB components of the pixels in the original side image may be used as the grayscale value of the pixels, thereby obtaining the original side image after grayscale processing. If the image acquisition module adopts the weighted average method, the three components may be weighted averaged with different weights according to the importance of each component of the three RGB components of the pixel or other indicators, thereby obtaining the original side image after grayscale processing. For example, the image acquisition module cuts off Figure 5 After the original side image shown in the figure is used to locate the gap area image, the cropped original side image is gray-scale processed to obtain the original side image after gray-scale processing as shown in the figure. Figure 6 As shown. Figure 6 It can be seen that the areas where the gaps other than the gap used for positioning are located (area F) in the original side image after grayscale processing are different from the image features of other areas. The statistical module 2200 and the detection module 2300 are coupled to Figure 6 Conduct defect detection.

[0103] It should be understood that, without departing from the teaching of the present application, the grayscale processing of the original side image may be performed in other ways, and the present application does not limit this.

[0104] Optionally, the image acquisition module may obtain the side image according to the original side image after grayscale processing, for example, including: binarizing the original side image after grayscale processing to obtain the side image of the wafer. Figure 6 After the original side image after grayscale processing is binarized, the side image is obtained. Figure 7 As shown, the area where the gap is located is area G.

[0105] As an example, the process of binarization processing by the image acquisition module may include: obtaining a predetermined threshold (for example, the median of 0 to 255 is 127), and for each pixel in the original side image after grayscale processing, in response to the grayscale value of the pixel being less than or equal to the threshold, changing the grayscale value of the pixel to 0 (i.e., black); in response to the grayscale value of the pixel being greater than the threshold, changing the grayscale value of the pixel to 255 (i.e., white).

[0106] As another example, the process of binarization processing by the image acquisition module may include: determining the grayscale average value of each pixel in the pixel array; for each pixel in the original side image after grayscale processing, in response to the grayscale value of the pixel being less than or equal to the grayscale average value, changing the grayscale value of the pixel to 0 (i.e., black); in response to the grayscale value of the pixel being greater than the grayscale average value, changing the grayscale value of the pixel to 255 (i.e., white).

[0107] It should be understood that, without departing from the teaching of the present application, the image acquisition module may select other methods to perform binarization processing on the grayscale processed image, and the present application does not impose any limitation on this.

[0108] In some embodiments of the present application, the statistical module 2200 is configured to: respectively calculate the sum or mean of the image feature values ​​of pixels in each row and / or column of the pixel array of the side image to obtain the image feature value of each row and / or column of the pixel array.

[0109] Optionally, the detection module is configured to: determine that there is an abnormality in the contour of the wafer in response to the statistical result indicating that there are abnormal values ​​in the image feature values ​​of the row and / or column.

[0110] Optionally, the detection module 2300 may also be configured to: after detecting an abnormal value, determine whether there is an abnormality in the outline of the wafer in combination with the distribution of the abnormal value. For example, for the original side image after cutting, since the image area of ​​the notch used for positioning has been cut out in advance, if the detection module 2300 detects an abnormal value, it determines that there is an abnormality in the outline of the wafer. For the original side image that has not been cut, since the side image contains an image area of ​​the notch used for positioning, the detection module 2300 can detect the abnormal value and determine the distribution area of ​​the abnormal value. For example, in response to the number of abnormal value distribution areas being less than or equal to N, the detection module 2300 determines that the outline of the wafer is normal; in response to the number of abnormal value distribution areas being greater than N, it determines that the outline of the wafer is abnormal. Among them, the value of N can be set according to the shooting method of the original side image. For example, if the same part in the image of the starting shooting area and the image of the ending shooting area of ​​the original side image is eliminated, N is equal to 1. If the same part in the image of the starting shooting area and the image of the ending shooting area of ​​the original side image is not eliminated, the value of N is determined according to the starting shooting position. For example, if the original side image is shot from the gap used for positioning, N=2; if the original side image is shot from a position other than the gap used for positioning, N=1.

[0111] As an example, the statistical module 2200 obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each row of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the row as the image feature value of the row. If there is a defect in the wafer, the image feature value of the pixel corresponding to the defect in the pixel array is different from the image feature value of the pixels around the pixel. By counting the feature values ​​of each row of pixels, if the image feature value of a row is different from the image feature value of the rows around it, it can be determined whether the pixels in the row include the pixels corresponding to the defect according to the difference between the image feature value of the row and the image feature value of the rows around it. For example, after counting the image feature values ​​of each row, if the detection module 2300 determines that the image feature difference between the i-th row and the i+1-th row is extremely large, for example, greater than the first threshold, it can be determined that the i-th row and the i+1-th row are the boundary of the wafer, not a defect; if it is determined that the image feature difference between the i-th row and the i+1-th row is less than the first threshold, it can be determined that the outline of the wafer may be abnormal.

[0112] It should be understood that, without departing from the teachings of the present application, the first threshold value can be determined according to the processing method of the original side image in the process of the image acquisition module obtaining the side image from the original side image, and the number of columns of the pixel array of the side image. For example, if the image acquisition module performs grayscale processing and binarization processing on the original side image to obtain the side image, and the number of columns of the pixel array is a, then the first threshold value can be, for example, equal to 255*a*T, and T can be, for example, any value between (70%, 100%), for example, 75%, 80%, 85%, 90% and 95%, etc. The present application does not limit the first threshold value.

[0113] As another example, the statistical module 2200 obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each column of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the column as the image feature value of the column. Taking the notch defect as an example, if there is no notch in the wafer, the image feature values ​​of each column in the pixel array of the side image are basically the same. If there is a notch in the wafer, usually, the notch of the wafer extends from the edge of the wafer to the inside of the wafer, so that the image feature values ​​of individual pixels in multiple rows of pixels in the side image of the wafer have a large difference from other pixels in the row. By statistically analyzing the image feature values ​​of each column of pixels in the pixel array to obtain the image feature value of the column, and comparing the image feature values ​​of each column to determine the area where the notch is located, it is possible to reduce the situation where the area where the notch is located is misidentified due to different image feature values ​​of pixels in different columns in the same row due to noise, etc. For example, the detection module 2300 sums the image feature values ​​of the pixels in each column of the pixel array of the side image, and the change trend of the obtained image feature values ​​of each column is as follows: Figure 8 As shown, Figure 8 In , the horizontal axis is the number of columns in the pixel array (j), and the vertical axis is the image feature value of the column (F). Figure 8 It can be seen that there is an abnormal value at j=K, and the detection module 2300 can determine that there is a gap in the wafer corresponding to the side image and the wafer contour is abnormal.

[0114] It should be understood that, without departing from the teachings of the present application, different methods for determining whether there are outliers can be configured for the detection module 2300, for example, by using an outlier detection algorithm to detect whether there are outliers in the image feature values ​​of each column, and the present application does not impose any restrictions on this.

[0115] Optionally, in the process of detecting wafer contour anomalies based on the image feature values ​​of each column of the pixel array, the detection module 2300 is further configured to: after determining that the wafer contour is abnormal, determine the position of the wafer contour anomaly according to the column corresponding to the abnormal value. As described above, since the image feature values ​​of each column in the pixel array of the side image are substantially the same, the detection module 2300 can locate the position of the wafer contour anomaly according to the column corresponding to the detected abnormal value.

[0116] Optional. After determining the column corresponding to the abnormal value, the detection module 2300 determines the longitudinal length of the contour abnormal area according to the pixels with abnormal image characteristic values ​​in the column corresponding to the abnormal value; determines the defect depth of the wafer according to the determined longitudinal length; and determines that the wafer has a notch defect in response to the defect depth being within a preset abnormal depth range.

[0117] For example, if the detection module 2300 determines that the image feature values ​​of the j1th column to the j2th column of the side image are abnormal, the pixels with abnormal image feature values ​​in the pixels of the j1th column to the j2th column of the side image are determined, and the longitudinal length (i.e., the length in the y direction) of the abnormal road condition area is determined accordingly. For example, if the pixels with abnormal image feature values ​​are continuously distributed in the side image, the longitudinal length is equal to the difference in the number of rows of the first pixel with abnormal image feature value and the last pixel with abnormal image feature value from the top of the side image to the bottom of the side image. For example, if the pixels with abnormal image feature values ​​are abnormal image feature values ​​of the pixels of the i1th row to the i2th row, the longitudinal length can be determined to be i2-i1. If the pixels with abnormal image feature values ​​are distributed in segments in the side image, multiple segments are integrated. After the integration is completed, the segments corresponding to the defects in each integrated segment are determined according to the preset search rules, and the number of pixels in the segments corresponding to the defects is used as the longitudinal length. The integration method may be, for example, as follows: for each segment, if the number of pixels between adjacent segments is less than a preset pixel number threshold (i pre ), the adjacent segments are integrated into one segment. The preset search rule is determined according to the parameters of the shooting device of the side image. For example, the search rule indicates: max row and i min The segments between the rows are taken as the segments corresponding to the defects. max and i min For example, if the pixels with abnormal image characteristic values ​​in the j1th column are pixels in the i1th row to the i2th row, and pixels in the i3th row to the i4th row, in response to i3-i2<i pre , determine the longitudinal length is i4-i1, in response to i3-i2≥i pre , i2 and i1 are in [i min ,imax ], the longitudinal length is equal to i2-i1. After determining the longitudinal length, the detection module 2300 uses the product of the longitudinal length and the actual length represented by each pixel in the side image as the defect depth of the wafer in that column. The detection module 2300 obtains the maximum value of the defect depth of the wafer in each column, and determines whether the obtained maximum value is in the abnormal depth range. If it is determined to be, it is determined that the wafer has a notch defect. If it is determined not to be, it can be determined that the wafer has other defects. Among them, the method of distinguishing other defects can be defined according to the morphological characteristics of other defects, which are not listed here one by one.

[0118] It should be understood that, without departing from the teachings of the present application, the abnormal depth range can be determined based on wafer structural characteristics and process requirements, for example, the abnormal depth range can be (0, 0.1 mm), and the present application does not impose any restrictions on this.

[0119] As another example, the detection module 2300 obtains the image feature value of each pixel in the pixel array of the side image (for example, the gray value of the pixel after binarization), and for each row of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the row as the image feature value of the row; and for each column of pixels in the pixel array, calculates the sum or mean of the image feature values ​​of the pixels in the column as the image feature value of the column. The detection module 2300 can combine the pixel features of the rows and the pixel features of the columns of the pixel array of the side image to comprehensively determine whether the contour of the wafer is abnormal. For example, if the detection module 2300 determines that the wafer has defects based on the pixel features of the rows of the pixel array of the side image, or determines that the wafer has defects based on the pixel features of the columns of the pixel array of the side image, it determines that the wafer contour is abnormal. By detecting the abnormality of the wafer contour through multiple features, it can reduce the situation where the wafer with abnormal contour is misjudged as a wafer with normal contour, and reduce the probability of missed judgment.

[0120] As an option, the detection module 2300 is further configured to: before determining whether the wafer profile is abnormal according to the statistical results, smooth the image feature values ​​of the rows and / or columns. For example, there may be noise points in the side image of the wafer. In order to reduce the influence of the noise points on the statistical image feature values ​​of the rows and / or columns, the statistical image feature values ​​of the rows and / or columns may be smoothed.

[0121] It should be understood that without departing from the teaching of the present application, a smoothing algorithm for row and / or column image feature values ​​may be selected as needed, and the present application does not impose any limitation on this.

[0122] It should be understood that, without departing from the teachings of the present application, the statistical module 2200 may also perform statistics on the image feature values ​​of pixels based on other rules, and adjust the way in which the detection module 2300 determines whether the contour of the wafer is abnormal based on the statistical rules. For example, the statistical module 2200 performs statistics on the rows and / or columns of the pixel array of a partial area of ​​the side image, and the detection module 2300 determines whether the contour is abnormal based on the statistical results. The present application does not impose any restrictions on this.

[0123] In some embodiments of the present application, the detection device 2000 may further include an alarm module (not shown) for feeding back an alarm message to the detector after determining that the wafer has an abnormal profile or after determining that the wafer has a notch defect. For example, the alarm module may feed back an alarm message to the detector by sending an alarm message, turning on an alarm ring, generating a test result, and sending an email to the detector, etc., and the present application does not limit this.

[0124] It is not difficult to find that this embodiment is a device embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details mentioned in the above method embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above method embodiment.

[0125] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0126] The embodiments of the present application also provide a detection device and a readable storage medium.

[0127] Fig.10 It is a block diagram of a detection device 3000 according to an embodiment of the present application. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0128] like Fig.10As shown, the detection device 3000 includes: one or more processors 3100, a memory 3200, and an interface (not shown) for connecting various components, including a high-speed interface and a low-speed interface. The memory 3200 can be used to store computer instructions. The processor 3100 can be used to communicate with the memory to execute computer instructions, thereby realizing the defect detection method mentioned in the above embodiment. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor 3100 can process instructions executed in the detection device 3000, including instructions stored in or on the memory 3200 to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors 3100 and / or multiple buses can be used together with multiple memories 3200 and multiple memories 3200. Similarly, multiple detection devices 3000 can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 3100 is taken as an example.

[0129] The memory 3200 is a readable storage medium provided in the present application, for example, a non-transitory computer-readable storage medium. The memory 3200 stores instructions executable by at least one processor 3100, so that at least one processor 3100 executes the defect detection method provided in the present application. The readable storage medium of the present application stores computer instructions, which are used to cause a computer to execute the defect detection method for a wafer provided in the above-mentioned embodiment of the present application.

[0130] The memory 3200 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 3100 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 3200, that is, implements the defect detection method for wafers in the above method implementation.

[0131] The memory 3200 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the detection device for quality control, etc. In addition, the memory 3200 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory 3200 may include a memory remotely arranged relative to the processor 3100, and these remote memories may be connected to the detection device 3000 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0132] The detection device 3000 may further include: an input device 3300 and an output device 3400. The processor 3100, the memory 3200, the input device 3300 and the output device 3400 may be connected via a bus or other means. Fig.10 The example of connecting through bus is taken in the following.

[0133] The input device 3300 can receive input digital or character information, and generate key signal input related to user settings and function control of the detection device for controlling quality, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 3400 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0134] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0136] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0137] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks, wide area networks, and the Internet.

[0138] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a server of a distributed system, or a server combined with a blockchain. The server may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The server may be a server of a distributed system, or a server combined with a blockchain. The server may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0139] According to the implementation mode of the present application, since the image feature values ​​of pixels in the abnormal area of ​​the wafer contour in the side image of the wafer are different from the image feature values ​​of pixels in the normal area, the detection device can analyze whether there is any abnormality in the wafer contour by statistically analyzing the image feature values ​​of the pixels in the side image of the wafer, thereby realizing automatic detection of wafer contour abnormalities, which improves detection efficiency and accuracy compared to manual detection.

[0140] The above description is only an implementation method of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of protection involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the technical concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for detecting a wafer, characterized in that: include: Determining image feature values ​​of pixels of the side image of the wafer; Taking statistics on image feature values ​​of pixels of the side image; as well as Determine whether the profile of the wafer is abnormal based on the statistical results, wherein, in response to the statistical result indicating that an abnormal value exists in the image feature value of the row and / or column of the pixel array of the side image, it is determined that the profile of the wafer is abnormal; as well as The position of the wafer contour abnormality is determined according to the column corresponding to the abnormal value.

2. The method according to claim 1, wherein: The counting of the image feature values ​​of the side image comprises: The sum or mean of the image characteristic values ​​of the pixels in each row and / or column of the pixel array of the side image is calculated respectively to obtain the image characteristic value of each row and / or column of the pixel array.

3. The method according to claim 1, wherein: The method further comprises: Determine the longitudinal length of the contour abnormal area according to the pixels with abnormal image characteristic values ​​in the column corresponding to the abnormal value; Determining a defect depth of the wafer according to the determined longitudinal length; and In response to the defect depth being within a preset abnormal depth range, it is determined that a notch defect exists in the wafer.

4. The method according to claim 2, wherein: Before determining whether the profile of the wafer is abnormal according to the statistical results, the method further includes: The image feature values ​​of the rows and / or columns are smoothed.

5. The method according to any one of claims 1 to 4, wherein: Before determining the image feature values ​​of the pixels of the side image of the wafer, the method further includes: Acquiring an original side image of the wafer; Performing grayscale processing on the original side image; and The side image is obtained according to the original side image after grayscale processing.

6. The method according to claim 5, wherein: The obtaining of the side image according to the original side image after the grayscale processing comprises: The original side image after the grayscale processing is binarized to obtain the side image.

7. The method according to claim 5, wherein: The wafer is provided with a notch for positioning, and the method further comprises: The image area corresponding to the notch used for positioning in the original side image is removed by cropping the original side image.

8. The method according to any one of claims 1 to 4, wherein: The image characteristic value includes a color value or a brightness value.

9. A wafer detection device, characterized in that: include: A determination module, used to determine image feature values ​​of pixels of the side image of the wafer; A statistical module, used for collecting statistics on image feature values ​​of pixels of the side image; as well as a detection module, configured to determine whether the contour of the wafer is abnormal according to the statistical result, wherein in response to the statistical result indicating that there are abnormal values ​​in the image feature values ​​of the rows and / or columns of the pixel array of the side image, it is determined that the contour of the wafer is abnormal; And determining the position of the wafer contour abnormality according to the column corresponding to the abnormal value.

10. The device according to claim 9, wherein: The statistics module is configured to: The sum or mean of the image characteristic values ​​of the pixels in each row and / or column of the pixel array of the side image is calculated respectively to obtain the image characteristic value of each row and / or column of the pixel array.

11. The device according to claim 9, wherein: The detection module is further configured to: Determine the longitudinal length of the contour abnormal area according to the pixels with abnormal image characteristic values ​​in the column corresponding to the abnormal value; Determining a defect depth of the wafer according to the determined longitudinal length; as well as In response to the defect depth being within a preset abnormal depth range, it is determined that a notch defect exists in the wafer.

12. The device according to claim 9, wherein: The detection device also includes: The alarm module is used to feedback alarm information in response to an abnormality in the outline of the wafer or a notch defect in the wafer.

13. A detection device, characterized in that: include: Memory, for storing computer instructions; as well as A processor, configured to communicate with the memory to execute the computer instructions, thereby implementing the detection method according to any one of claims 1 to 8.

14. A readable storage medium, characterized in that: The readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the detection method according to any one of claims 1 to 8 is implemented.

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