Defect size determination method and device, computer equipment, readable storage medium and program product
By comparing the bare crystal defect image with the wafer standard image, determining the defect pixel set and calculating the defect area, the accuracy and efficiency problems of defect size detection in the prior art are solved, and the intuitive presentation of the defect distribution of semiconductor wafers is achieved. Improvement of statistical analysis.
Patent Information
- Application Number
- CN202411433298.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2025-02-14
AI Technical Summary
The prior art is difficult to accurately and efficiently detect defect sizes on semiconductor wafers, especially in the case of a large number of defects, and cannot intuitively present defect distribution, affecting statistical analysis.
By acquiring bare crystal defect images and their corresponding defect information, comparing these images with pre-stored wafer standard images, determining the defect pixel set, and calculating the defect area and target defect pixel set based on the coordinate information and grayscale values, finally Accurately calculate defect size.
Accurate size confirmation of a large number of defects is achieved, defect distribution can be visually presented, and the accuracy and efficiency of defect statistical analysis are improved.
Smart Images
Figure CN119444677A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the semiconductor field, and in particular to a defect size determination method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art
[0002] In the modern semiconductor manufacturing process, defect detection on wafers is crucial because these defects directly affect the performance and reliability of bare wafers. However, even for defects of the same type, different defect sizes will have different effects on bare wafer performance, and the causes of defects of different sizes are also different. Therefore, being able to accurately measure the size of defects is particularly important for evaluating the lethality of defects and investigating the causes of defects.
[0003] Currently, existing technologies usually rely on scanning electron microscopy (SEM) to confirm defects one by one, but this method cannot confirm the size of a large number of defects and cannot intuitively present the distribution of defects of various sizes on the bare crystal, thus affecting the statistical analysis of defects. Summary of the invention
[0004] Based on this, it is necessary to provide a defect size determination method, device, computer equipment, computer readable storage medium and computer program product that can accurately calculate the defect size in response to the above technical problems.
[0005] In a first aspect, the present application provides a method for determining a defect size, the method comprising:
[0006] Acquire each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value;
[0007] Comparing each of the bare die defect images with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each of the defect images;
[0008] Calculating the defect area according to the coordinate information and grayscale value corresponding to each of the bare die defect images;
[0009] Comparing the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set;
[0010] The defect size is calculated according to each of the target defective pixel sets.
[0011] In one embodiment, comparing each of the bare die defect images with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each of the defect images includes:
[0012] Obtaining each reference image from the wafer standard image according to coordinate information corresponding to each of the bare die defect images;
[0013] The grayscale values of each reference image and each bare die defect image are compared to obtain each defect pixel set.
[0014] In one embodiment, the step of comparing the grayscale values of each reference image with each bare die defect image to obtain each defect pixel set includes:
[0015] Subtracting the grayscale value corresponding to each pixel point in each of the reference images from the grayscale value corresponding to each pixel point in each of the bare die defect images to obtain a grayscale difference;
[0016] When the grayscale difference is greater than a preset threshold, determining a pixel point in the bare die defect image as a target pixel point;
[0017] Based on the target pixel point, the defective pixel set is obtained.
[0018] According to the above-mentioned defect size determination method, the step of comparing the grayscale change values of each defect area and each defect pixel set to determine each target defect pixel set includes:
[0019] Calculating a first grayscale change value of each pixel in each defective area and a second grayscale change value of each pixel in each defective pixel set;
[0020] The pixel points in each of the defective pixel sets whose second grayscale change value is consistent with the first grayscale change value are taken as the target defective pixel set.
[0021] In one embodiment, the calculating of the first grayscale change value of each pixel in each defective area and the second grayscale change value of each pixel in each defective pixel set includes:
[0022] Determine a first central pixel point of each of the defective regions and a second central pixel point of each of the defective pixel sets;
[0023] Calculating the first grayscale change value based on the grayscale values of each of the first central pixels and other pixels in the defect area;
[0024] The second grayscale variation value is calculated based on the grayscale values of each of the second central pixels and other pixels in each of the defective pixel sets.
[0025] In one embodiment, after acquiring each bare die defect image, the method further includes:
[0026] The bare die defect image is preprocessed; the preprocessing includes graying.
[0027] In one of the embodiments, before extracting each defect pixel set from a pre-stored wafer standard image according to the coordinate information corresponding to each bare die defect image, the method further includes:
[0028] Establishing a virtual map according to the defect information corresponding to each of the bare die defect images;
[0029] According to the coordinate information corresponding to each of the bare die defect images, the images are marked and displayed on the virtual map.
[0030] In a second aspect, the present application also provides a defect size determination device, the device comprising:
[0031] An acquisition module, used to acquire each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value;
[0032] A defect set determination module, used for comparing each of the bare die defect images with a pre-stored wafer standard image, to obtain a defect pixel set corresponding to each of the defect images;
[0033] A defect area determination module, used to determine the defect area according to the coordinate information and grayscale value corresponding to each of the bare crystal defect images;
[0034] A comparison module, used for comparing the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set;
[0035] The size calculation module is used to calculate the defect size according to each target defect pixel set.
[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.
[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.
[0038] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.
[0039] The above-mentioned defect size determination method, device, computer equipment, computer-readable storage medium and computer program product first compare each bare crystal defect image with a pre-stored wafer standard image to obtain a defect pixel set, and then obtain the defect area based on the coordinate information and grayscale value corresponding to the bare crystal defect image. Thereafter, the defect area and the grayscale change value of the defect pixel set are compared, which can effectively identify the edge area and avoid missed detection, so as to accurately determine the target defect pixel set, and then accurately calculate the defect size based on the target defect pixel set. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0041] Figure 1 A schematic diagram of a field defect scanning machine identifying defects in one embodiment;
[0042] Figure 2 A schematic diagram of identifying defects of a platform machine in one embodiment;
[0043] Figure 3 is a schematic diagram of actual defects in an embodiment;
[0044] Figure 4 A schematic diagram of a defect size determination method in one embodiment;
[0045] Figure 5 A diagram of an application environment of a defect size determination method in one embodiment;
[0046] Figure 6 A schematic diagram of a flow chart of a defect size determination method in another embodiment;
[0047] Figure 7 is a structural block diagram of a defect size determination device in one embodiment;
[0048] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0050] During the semiconductor manufacturing process, wafers are prone to various defects due to various factors such as material defects, particle contamination, equipment problems, and process parameter fluctuations. These defects directly affect the performance and reliability of the chip, so accurate and efficient defect detection is crucial.
[0051] At present, bright field defect scanning equipment is used to perform die-to-die comparison on each die (bare crystal) on the wafer under the scanning lens to identify and locate defects. The minimum comparison unit is pixel, and each pixel has a grayscale value of 0 to 255. The machine compares the grayscale values of pixels at corresponding positions, and when the grayscale difference is greater than or equal to the preset threshold (for example, 80), the area is marked as a defect. The size of the defect is calculated by accumulating the number of pixels identified as defects, thereby achieving accurate detection and positioning of defects on the wafer. Among them, die-to-die comparison refers to comparing the images or data of two adjacent or identically positioned dies on the same wafer.
[0052] Exemplary, combined Figure 1 As shown, Figure 1 The figure is a schematic diagram of a defect recognition machine in a bright field in one embodiment. The defect scanning machine compares the grayscale difference between Die B and the left and right Dies A and Die C on the wafer to find out the area with grayscale difference and identify it as a defect. In a bright field defect scanning machine, assume that the grayscale values of the pixels in a defective area on Die B are 20, 60, and 80 in the center, middle, and edge, respectively, and the grayscale differences with the background are 80, 40, and 20, respectively. The grayscale value of the noise is 80, and the grayscale difference with the background is 20. To avoid misjudging noise as a defect, the grayscale difference threshold set by the machine needs to be greater than 20. However, if the set threshold is too large (such as set to 20), although the noise can be filtered, the grayscale difference in the defect edge area is also less than the set value, so the machine cannot recognize the area, resulting in the measured defect size being smaller than the actual size.
[0053] Further, combined with Figure 2 , Figure 2 This is a schematic diagram of a machine identifying defects in an embodiment. The machine identifies a part of the defects based on the set grayscale difference parameters, but due to the limitation of the grayscale difference setting, only the central area of the defect is identified. This area has the largest grayscale difference. Figure 3 , Figure 3 Schematic diagram of an actual defect in an embodiment, the actual defect area is larger than the defect recognized by the machine, and includes more peripheral pixels. Since the grayscale difference of the peripheral pixels gradually decreases and approaches the grayscale difference threshold set by the machine, it is not recognized by the machine.
[0054] In one embodiment, Figure 4As shown, a defect size determination method is provided. This embodiment takes the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0055] Step S402 , obtaining each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value.
[0056] The machine first scans each bare die on the wafer, and then obtains the defect image corresponding to each bare die. These images record the actual image information of the defects on the surface of the bare die.
[0057] At the same time, the machine records and obtains the defect information related to each bare die defect image, including coordinate information and grayscale value.
[0058] The coordinate information is used to indicate the specific location of the defect on the wafer, usually expressed as X and Y coordinates, to facilitate subsequent positioning and analysis.
[0059] The grayscale value is used to record the grayscale value of each pixel, that is, the brightness change from 0 (black) to 255 (white), which is used to further analyze the brightness and contrast characteristics of the defect.
[0060] After obtaining each bare die defect image and the defect information corresponding to each bare die defect image, the desktop computer sends each bare die defect image and the defect information corresponding to each bare die defect image to the system, and the system identifies each bare die defect image to accurately identify the defects and then accurately calculate the defect size.
[0061] Step S404 , comparing each bare die defect image with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each defect image.
[0062] Among them, the wafer standard image is sent to the system and saved in advance by the desktop machine. The wafer standard image represents a defect-free or ideal bare crystal state.
[0063] Optionally, each bare crystal has a fixed position on the wafer. Therefore, after obtaining the standard image of the wafer, the lower left corner of the bare crystal is used as the coordinate origin to confirm the coordinates of the upper, lower, left and right corners of the bare crystal in the machine and establish a coordinate system. This ensures that the same coordinate system can be used when comparing the defect positions scanned subsequently with the standard image.
[0064] Optionally, each bare die defect image is compared with a pre-stored wafer standard image pixel by pixel. During the comparison process, pixels at the same position of the bare die defect image and the standard image are compared one by one, and pixels with grayscale differences are identified. These pixels with grayscale differences are determined as defective pixel sets.
[0065] Step S406 , calculating the defect area according to the coordinate information and grayscale value corresponding to each bare die defect image.
[0066] Since the grayscale difference between the center, middle and edge of the defect gradually decreases during bright field or dark field defect scanning, the machine may not be able to identify defects in the edge area under the set grayscale difference threshold, which leads to inaccurate calculation of the defect size. Therefore, by calculating the defect area, the system in this embodiment can compare the grayscale changes in the entire defect area and the grayscale changes of the defective pixel set, rather than relying solely on a specific threshold. This helps to improve the recognition accuracy of defects when the grayscale difference at the edge decreases.
[0067] Optionally, the coordinate information corresponding to the defect image is used to map the actual position of each defect onto the overall image of the wafer, and then the grayscale value of the pixel is used to calculate the boundary of each defect area to form the defect area contour.
[0068] Optionally, a grayscale difference threshold may be set, and the grayscale value of each pixel is compared with the grayscale difference threshold to determine the boundary of each defective area, and further determine the defective area.
[0069] Step S408 , comparing the grayscale variation values of each defective region and each defective pixel set to determine each target defective pixel set.
[0070] In this step, the grayscale change value of each defective area is analyzed and compared with the pixels in the corresponding defective pixel set. By comparing the grayscale change of each pixel, the system identifies which pixels are actual defective points.
[0071] Optionally, the grayscale change value of each pixel in each defective area and the grayscale change value of each pixel in each defective pixel set are calculated respectively, and then the changes therebetween are compared to determine the target defective pixel set corresponding to each bare die defect image.
[0072] Optionally, the pixels with the same change conditions between the two can be taken as the target defective pixel set.
[0073] Optionally, if the difference between the changes of the two meets the pixel point of a preset value, the pixel point is taken as a target defective pixel set.
[0074] Step S410: Calculate defect sizes according to each target defect pixel set.
[0075] Optionally, the number of target defect pixels can be counted and converted to physical size. For example, if one pixel corresponds to 0.85 microns, the defect size can be obtained by multiplying the number of pixels by the physical size of each pixel.
[0076] In the above embodiment, each bare crystal defect image is first compared with a pre-stored wafer standard image to obtain a defect pixel set, and then the defect area is obtained based on the coordinate information and grayscale value corresponding to the bare crystal defect image. After that, the grayscale change value of the defect area and the defect pixel set is compared, which can effectively identify the edge area and avoid missed detection, so as to accurately determine the target defect pixel set, and then accurately calculate the defect size based on the target defect pixel set.
[0077] In one embodiment, the above-mentioned comparing each bare crystal defect image with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each defect image includes: obtaining each reference image from the wafer standard image according to the coordinate information corresponding to each bare crystal defect image; comparing the grayscale value of each reference image with that of each bare crystal defect image to obtain each defect pixel set.
[0078] The reference image refers to an image corresponding to or related to each defect image. For example, if the current defect image is an image of die B, then the reference image can be a standard image of die B, or a standard image of a die related to die B, such as die A and die C adjacent to die B, or any one of die A and die C.
[0079] First, based on the coordinate information of each bare die defect image, the reference image at the corresponding position is extracted from the pre-stored standard wafer image. These reference images represent the ideal state without defects and are used to compare and analyze defect images. The reference image is compared with the bare die defect image pixel by pixel in grayscale value, and the pixels whose grayscale difference exceeds the set threshold are extracted to form a defective pixel set.
[0080] In the above embodiment, the reference image is extracted from the standard wafer image through the coordinate information, and then the grayscale value is compared pixel by pixel, so that the defective pixel set can be effectively identified and generated.
[0081] Furthermore, in one embodiment, the above-mentioned comparison of the grayscale values of each reference image and each bare crystal defect image to obtain each defect pixel set includes: subtracting the grayscale value corresponding to each pixel point in each reference image from the grayscale value corresponding to each pixel point in each bare crystal defect image to obtain a grayscale difference; when the grayscale difference is greater than a preset threshold, determining the pixel point in the bare crystal defect image as a target pixel point; and obtaining a defect pixel set based on the target pixel point.
[0082] The preset threshold refers to a value used to distinguish normal pixels from defective pixels when performing grayscale value comparison.
[0083] The target pixel refers to a pixel that is identified as having a significant grayscale difference when comparing the reference image with the bare die image.
[0084] In this embodiment, the grayscale difference between the reference image and each bare crystal defect image is calculated one by one, and the grayscale difference is compared with a preset threshold. If the grayscale difference is greater than the preset threshold, the pixel point in the corresponding bare crystal image is used as the target pixel point, and a defective pixel set is obtained based on each target pixel point.
[0085] For example, the coordinate range of a certain area of the bare wafer defect image is (50, 100) to (150, 200), and there may be defects in this area. According to the coordinate information, a reference image with the same coordinate range of (50, 100) to (150, 200) is extracted from the standard wafer image, and the preset threshold is 80.
[0086] Compare the grayscale values of the defect image and the reference image pixel by pixel. For example. For pixel (60, 110), the grayscale value of the corresponding reference image is 200, the grayscale value of the defect image is 50, and the grayscale difference is |200-50|=150, which is greater than the preset threshold 80. Since the grayscale difference is greater than 80, the pixel is determined to be the target pixel. For pixel (65, 115), the grayscale value of the reference image is 195, the grayscale value of the defect image is 190, and the grayscale difference is |195-190|=5. Since the grayscale difference is less than 80, the pixel is determined to be a normal pixel.
[0087] In the above embodiment, by presetting the threshold, normal pixels and noise can be effectively filtered, and only those pixels with significant grayscale difference from the reference image are retained as target pixels. These target pixels are combined together to form a defective pixel set.
[0088] In an exemplary embodiment, the above-mentioned comparison of the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set includes: calculating the first grayscale change value of each pixel point in each defective area and the second grayscale change value of each pixel point in each defective pixel set; and taking the pixel points in each defective pixel set whose second grayscale change value is consistent with the first grayscale change value as the target defective pixel set.
[0089] The first grayscale change value is obtained based on the grayscale value change of each pixel in the defect area, and is obtained relative to the grayscale value change of each pixel inside the defect area. The second grayscale change value is obtained based on the grayscale value change of each pixel in the defect area, and is obtained relative to the grayscale value change of each pixel.
[0090] Optionally, a central pixel point may be set, and then the grayscale change values of other pixels are calculated in sequence based on the central pixel point.
[0091] Optionally, the grayscale change values of the central pixel and other adjacent pixels are calculated first, and then the other adjacent pixels are taken as the central pixel, and the grayscale change values of the other adjacent pixels are continued to be calculated until the grayscale change values of each pixel are obtained.
[0092] Optionally, the grayscale change values of other adjacent pixels and the central pixel may be directly calculated.
[0093] According to the above method, the first grayscale change value and the second grayscale change value of each pixel point are obtained respectively, and then the first grayscale change value is compared with the second grayscale change value, and the pixel points in each defective pixel set whose second grayscale change value is consistent with the first grayscale change value are taken as the target defective pixel set.
[0094] Since the defective pixel set is obtained by comparing the defective image with the standard image, the purpose of the comparison is to ensure that no possible defective area is missed, so the defective pixel set may include all pixels with grayscale differences, and even some noise that is not a real defect. Therefore, in this embodiment, the noise in the defective pixel set is filtered out by comparing the second grayscale change value to obtain the target defective pixel set.
[0095] Optionally, a change trend may be calculated based on the first grayscale change value and the second grayscale change value, and if the change trends of the two are consistent, a target defective pixel set is obtained.
[0096] In the above embodiment, by comparing the grayscale change values, the noise in the defective pixel set can be filtered to obtain a target defective pixel set that accurately represents the defects.
[0097] Furthermore, in one embodiment, the above-mentioned calculation of the first grayscale change value of each pixel point in each defective area and the second grayscale change value of each pixel point in each defective pixel set includes: determining the first central pixel point of each defective area and the second central pixel point of each defective pixel set; calculating the first grayscale change value based on the grayscale values of each first central pixel point and other pixel points in each defective area; calculating the second grayscale change value based on the grayscale values of each second central pixel point and other pixel points in each defective pixel set.
[0098] Optionally, the defective area and the pixel with the largest grayscale difference in the defective pixel set may be taken as the center to obtain a first central pixel point and a second central pixel point respectively.
[0099] In this embodiment, according to the method mentioned in the above embodiment, the grayscale change value of the central pixel point and other pixel points adjacent to the central pixel point is first calculated, and then the other adjacent pixel points are taken as the central pixel point, and the grayscale change value of the other adjacent pixel points is continued to be calculated until the grayscale change value of each pixel point is obtained, so as to obtain the first grayscale change value and the second grayscale change value.
[0100] Optionally, assuming that the center of a defective area has a larger grayscale difference and the edge area has a smaller grayscale difference. The defective area may only identify the central part with a significant grayscale difference, while missing the edge part. However, by analyzing the grayscale change trend of the defective area and the defective pixel set, it can be identified that the change trend of the edge part is consistent with the center, so that the edge area is also identified as part of the defect, and the noise in the curve pixel set is filtered out to obtain the target defective pixel set.
[0101] In the above embodiment, the edge areas that may be missed in the initial detection are identified through this progressive grayscale change value calculation method, and noise is eliminated through trend analysis, so as to finally obtain an accurate target defective pixel set.
[0102] In one embodiment, after acquiring each bare die defect image, the method further includes: preprocessing the bare die defect image; the preprocessing includes grayscale conversion.
[0103] In this embodiment, after acquiring each bare die defect image, the bare die defect image is further preprocessed to improve the accuracy and efficiency of defect detection.
[0104] Optionally, the preprocessing can be grayscale, which can greatly enhance the contrast and make the defect area more prominent. Grayscale refers to the process of converting a color image (usually containing three color channels of red, green and blue, namely RGB) into a grayscale image. The converted grayscale image no longer contains color information, and the value of each pixel represents the brightness of the pixel, namely the grayscale value, which is usually represented by an integer between 0 and 255. 0 represents completely black (lowest brightness), while 255 represents completely white (highest brightness).
[0105] In the above embodiments, performing the preprocessing operation can significantly improve the accuracy and efficiency of defect detection.
[0106] In one embodiment, before extracting each defect pixel set from a pre-stored wafer standard image according to the coordinate information corresponding to each bare crystal defect image, the above also includes: establishing a virtual map according to the defect information corresponding to each bare crystal defect image; marking and displaying on the virtual map according to the coordinate information corresponding to each bare crystal defect image.
[0107] First, a virtual map is created based on the defect information (such as coordinate information, size, shape, etc.) corresponding to each bare die defect image. The virtual map is equivalent to the mapping of the entire wafer or multiple bare dies, and can display the entire inspection area in two or three dimensions.
[0108] Based on the coordinate information of each bare die defect image, the system will accurately mark the location of the defect on the virtual map.
[0109] In the above embodiment, the purpose of establishing the virtual map is to visualize each defect information so as to observe and analyze the defect distribution more intuitively.
[0110] In an exemplary embodiment, in combination Figure 5 as well as Figure 6 As shown, Figure 5 This is an application scenario diagram of a defect size determination method in an embodiment. Figure 6 FIG. 1 is a schematic diagram of a defect size determination process in one embodiment.
[0111] The defect scanning machine obtains the standard image of the bare die on the wafer, takes the lower left corner of the bare die as the coordinate origin, confirms the coordinates of the four corners of the bare die in the machine, establishes a coordinate system, and inputs the standard image and coordinate system into the defect recognition system. When the defect scanning machine completes scanning the wafer, it imports the file containing all defect information (including the coordinates, size and grayscale of each defect, etc.) and the defect image into the defect recognition system.
[0112] The defect recognition system generates a virtual map based on the information in the file, and displays the defects on the virtual map in the form of red dots based on the coordinate information of each defect. The defect images correspond one-to-one to the red dots on the virtual map.
[0113] The defect recognition system pre-processes each defect image to make it secondary. Through the coordinates in the defect information corresponding to the defect image, the reference image of the defect image is obtained in the standard image, and both are decomposed into pixel sets in pixels. By comparing the grayscale values of the pixel sets of the defect image and the reference image, the pixel sets of defects and noise in the defect image are found.
[0114] The defect recognition system simulates the defect morphology by analyzing the X size / Y size and grayscale distribution in the defect information corresponding to each defect image; the defect recognition system finds the pixel set for each defect image as described above, and forms a simulated morphology with the pixel points in the pixel set whose grayscale difference with the background is greater than the program setting parameters. The grayscale value change sequence is compared with the pixel with the largest grayscale difference between the two as the center. If the grayscale value changes are consistent, it is the pixel set where the defect is located. The size of the pixel set is calculated and defined as the defect size.
[0115] Finally, the size is defined as the defect size corresponding to the defect image and written into a file. The file is transferred to the DMS system, and the engineer can confirm the defect size from the DMS system.
[0116] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0117] Based on the same inventive concept, the embodiment of the present application also provides a defect size determination device for implementing the defect size determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more defect size determination device embodiments provided below can refer to the limitations of the defect size determination method above, and will not be repeated here.
[0118] In an exemplary embodiment, Figure 7 As shown, a defect size determination device is provided, including: an acquisition module 710, a defect set determination module 720, a defect area determination module 730, a comparison module 740 and a size calculation module 750, wherein:
[0119] The acquisition module 710 is used to acquire each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value.
[0120] The defect set determination module 720 is used to compare each bare die defect image with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each defect image.
[0121] The defect area determination module 730 is used to determine the defect area according to the coordinate information and grayscale value corresponding to each bare die defect image.
[0122] The comparison module 740 is used to compare the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set.
[0123] The size calculation module 750 is used to calculate the defect size according to each target defect pixel set.
[0124] In one embodiment, the defect set determination module includes:
[0125] The reference image acquisition unit is used to obtain each reference image from the wafer standard image according to the coordinate information corresponding to each bare die defect image.
[0126] The grayscale comparison unit is used to compare the grayscale values of each reference image with each bare die defect image to obtain each defect pixel set.
[0127] In one embodiment, the grayscale contrast unit comprises:
[0128] The grayscale difference calculation subunit is used to subtract the grayscale value corresponding to each pixel point in each of the reference images from the grayscale value corresponding to each pixel point in each of the bare die defect images to obtain a grayscale difference.
[0129] The pixel determination subunit is used to determine the pixel point in the bare die defect image as the target pixel point when the grayscale difference is greater than a preset threshold.
[0130] The set acquisition subunit is used to obtain the defective pixel set based on the target pixel point.
[0131] In one embodiment, the comparison module includes:
[0132] The grayscale conversion unit is used to calculate the first grayscale change value of each pixel in each defective area and the second grayscale change value of each pixel in each defective pixel set.
[0133] The change comparison unit is used to take the pixel points whose second grayscale change value is consistent with the first grayscale change value in each of the defective pixel sets as the target defective pixel set.
[0134] In one embodiment, the grayscale conversion unit includes:
[0135] The center determination subunit is used to determine a first center pixel point of each defective area and a second center pixel point of each defective pixel set.
[0136] The first calculation subunit is configured to calculate the first grayscale change value based on the grayscale values of each of the first central pixels and each of the other pixels in the defect area.
[0137] The second calculation subunit is used to calculate the second grayscale change value based on the grayscale values of each of the second central pixels and other pixels in each of the defective pixel sets.
[0138] In one embodiment, the above device further comprises:
[0139] A preprocessing module is used to preprocess the bare die defect image; wherein the preprocessing includes grayscale conversion.
[0140] In one embodiment, the above device further comprises:
[0141] The map establishing module is used to establish a virtual map according to the defect information corresponding to each of the bare crystal defect images.
[0142] The marking display module is used to mark and display on the virtual map according to the coordinate information corresponding to each of the bare die defect images.
[0143] Each module in the defect size determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store defect image data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a defect size determination method is implemented.
[0145] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0146] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.
[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.
[0148] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following steps of the method in any one of the above embodiments.
[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.
[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0151] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A method for determining defect size, characterized in that: The method comprises: Acquire each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value; Comparing each of the bare die defect images with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each of the defect images; Calculating the defect area according to the coordinate information and grayscale value corresponding to each of the bare die defect images; Comparing the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set; The defect size is calculated according to each of the target defective pixel sets.
2. The defect size determination method according to claim 1, characterized in that: The step of comparing each of the bare die defect images with a pre-stored wafer standard image to obtain a defect pixel set corresponding to each of the defect images includes: Obtaining each reference image from the wafer standard image according to coordinate information corresponding to each of the bare die defect images; The grayscale values of each reference image and each bare die defect image are compared to obtain each defect pixel set.
3. The defect size determination method according to claim 2, characterized in that: The step of comparing the grayscale values of each reference image with each bare die defect image to obtain each defect pixel set includes: Subtracting the grayscale value corresponding to each pixel point in each of the reference images from the grayscale value corresponding to each pixel point in each of the bare die defect images to obtain a grayscale difference; When the grayscale difference is greater than a preset threshold, determining a pixel point in the bare die defect image as a target pixel point; Based on the target pixel point, the defective pixel set is obtained.
4. The defect size determination method according to claim 1, characterized in that: The comparing the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set includes: Calculating a first grayscale change value of each pixel in each defective area and a second grayscale change value of each pixel in each defective pixel set; The pixel points in each of the defective pixel sets whose second grayscale change value is consistent with the first grayscale change value are taken as the target defective pixel set.
5. The method according to claim 4, characterized in that The calculating the first grayscale change value of each pixel point in each defective area and the second grayscale change value of each pixel point in each defective pixel set includes: Determine a first central pixel point of each of the defective regions and a second central pixel point of each of the defective pixel sets; Calculating the first grayscale change value based on the grayscale values of each of the first central pixels and other pixels in the defect area; The second grayscale variation value is calculated based on the grayscale values of each of the second central pixels and other pixels in each of the defective pixel sets.
6. The defect size determination method according to claim 1, characterized in that: After acquiring each bare die defect image, the method further includes: The bare die defect image is preprocessed; the preprocessing includes graying.
7. The defect size determination method according to claim 1, characterized in that: Before extracting each defect pixel set from a pre-stored wafer standard image according to the coordinate information corresponding to each bare die defect image, the method further includes: Establishing a virtual map according to the defect information corresponding to each of the bare die defect images; According to the coordinate information corresponding to each of the bare die defect images, the images are marked and displayed on the virtual map.
8. A defect size determination device, characterized in that: The device comprises: An acquisition module, used to acquire each bare die defect image and defect information corresponding to each bare die defect image; the defect information includes coordinate information and grayscale value; A defect set determination module, used for comparing each of the bare die defect images with a pre-stored wafer standard image, to obtain a defect pixel set corresponding to each of the defect images; A defect area determination module, used to determine the defect area according to the coordinate information and grayscale value corresponding to each of the bare crystal defect images; A comparison module, used for comparing the grayscale change values of each defective area and each defective pixel set to determine each target defective pixel set; The size calculation module is used to calculate the defect size according to each target defect pixel set.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.
11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.