Defect detection methods, defect detection devices and systems

Abstract

The defect detection method disclosed in this invention includes, for example, acquiring an initial image to be tested; acquiring a signal intensity distribution dataset of a target detection image corresponding to the initial image to be tested at a target rotation angle; determining first defect information of the target detection image based on the signal intensity distribution dataset; and determining defect feature information on the initial image to be tested based on the first defect information, the target rotation angle, and the signal intensity distribution dataset. The defect detection method disclosed in this invention can improve the accuracy of wafer defect detection.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a defect detection method, a defect detection device, and a defect detection system. Background Technology

[0002] In the process of inspecting wafers, especially patternless wafers, the acquired wafer image is usually divided into multiple inspection areas for analysis to determine the surface defects of the wafer. However, there may be some intermittent or inconspicuous defects on the wafer surface. Therefore, after splitting, it may be impossible to detect the defects in each inspection area, thus affecting the accuracy of wafer inspection. Summary of the Invention

[0003] Therefore, in order to overcome at least some of the defects and problems in the prior art, the present invention provides a defect detection method, a defect detection device, and a defect detection system, which have the characteristics of high detection accuracy.

[0004] Specifically, in one aspect, a defect detection method provided by an embodiment of the present invention includes, for example,: acquiring an initial image to be tested; acquiring a signal intensity distribution dataset of a target detection image corresponding to the initial image to be tested at a target rotation angle; determining first defect information of the target detection image based on the signal intensity distribution dataset; and determining defect feature information on the initial image to be tested based on the first defect information, the target rotation angle, and the signal intensity distribution dataset.

[0005] In one embodiment, the signal intensity distribution dataset includes multiple intensity distribution data. Obtaining the signal intensity distribution dataset of the target detection image corresponding to the initial test image at the target rotation angle includes: obtaining the target detection image corresponding to the initial test image at the target rotation angle; analyzing the pixels of the target detection image along a preset direction to obtain the multiple intensity distribution data, wherein the multiple intensity distribution data corresponds one-to-one with the pixels of the target detection image in the preset direction.

[0006] In one embodiment, the step of analyzing the pixels of the target detection image along a preset direction to obtain the plurality of intensity distribution data, wherein the plurality of intensity distribution data correspond one-to-one with the pixels of the target detection image in the preset direction, includes: obtaining the pixel values ​​of all pixels in the target row of the target detection image; calculating the intensity data of the intensity distribution data corresponding to the target row based on the pixel values; and using the row position of the target row as the position data of the intensity distribution data corresponding to the target row.

[0007] In one embodiment, determining the first defect information of the target detection image based on the signal intensity distribution dataset includes: determining defect intensity distribution data corresponding to the defect location of the target detection image among the plurality of intensity distribution data; and using the location data of the defect intensity distribution data as the first defect information.

[0008] In one embodiment, determining the defect intensity distribution data corresponding to the defect location in the target detection image from the plurality of intensity distribution data includes: comparing a preset intensity threshold with the intensity data of the target intensity distribution data; and determining the target intensity distribution data as the defect intensity distribution data when the intensity data of the target intensity distribution data is greater than the preset intensity threshold.

[0009] In one embodiment, determining the defect intensity distribution data corresponding to the defect location in the target detection image from the plurality of intensity distribution data includes: recording the variation patterns of the plurality of intensity data in the signal intensity distribution dataset, and determining the defect intensity distribution data based on the variation patterns of the plurality of intensity data.

[0010] In one embodiment, recording the variation patterns of the plurality of intensity data in the signal intensity distribution dataset and determining the defect intensity distribution data based on the variation patterns of the plurality of intensity data includes: generating a signal intensity distribution map with the respective position data of the plurality of intensity distribution data as the abscissa and the intensity data as the ordinate; and determining the defect intensity distribution data based on the jitter amplitude of the ordinate obtained from the signal intensity distribution map.

[0011] In one embodiment, the defect feature information includes defect location and defect length. Determining the defect feature information on the initial image under test based on the first defect information, the target rotation angle, and the signal intensity distribution dataset includes: determining the defect length based on the variation law of the pixel value of the pixel corresponding to the location data in the defect intensity distribution data; and determining the defect location on the initial image under test based on the first defect information, the defect length, and the target rotation angle.

[0012] In one embodiment, obtaining the target detection image of the initial image to be tested at the target rotation angle includes: rotating the initial image to be tested stepwise according to a preset angle gradient until the total rotation angle reaches the rated angle, thereby obtaining multiple rotated images, wherein each rotation at the preset angle gradient yields one rotated image, and the target rotation angle is the angle between the rotated image and the initial image to be tested; and preprocessing the multiple rotated images to obtain multiple target detection images at multiple target rotation angles.

[0013] On the other hand, another embodiment of the present invention provides a defect detection device, comprising an image acquisition module for acquiring an initial image to be tested; a dataset acquisition module for acquiring a signal intensity distribution dataset of a target detection image corresponding to the initial image to be tested at a target rotation angle; a first information acquisition module for determining first defect information of the target detection image based on the signal intensity distribution dataset; and a second information acquisition module for determining defect feature information on the initial image to be tested based on the first defect information, the target rotation angle, and the signal intensity distribution dataset.

[0014] On the other hand, another embodiment of the present invention provides a defect detection system, including a processor and a memory connected to the processor, the memory storing instructions executed by the processor, and the instructions causing the processor to perform operations to perform the defect detection method as described in any of the foregoing embodiments.

[0015] On the other hand, another embodiment of the present invention provides a defect detection system, comprising: a processor; and an image acquisition device connected to the processor; wherein the image acquisition device is used to acquire an initial image of a wafer under test and send the initial image to the processor, and the processor is used to: acquire the initial image; acquire a signal intensity distribution dataset of a target detection image corresponding to the initial image under test at a target rotation angle; determine first defect information of the target detection image based on the signal intensity distribution dataset; and determine defect feature information on the initial image under test based on the first defect information, the target rotation angle, and the signal intensity distribution dataset.

[0016] On the other hand, another embodiment of the present invention provides a computer-readable medium storing computer-readable instructions, the computer-readable instructions including instructions for performing the defect detection method as described in any of the preceding statements.

[0017] As can be seen from the above, the above embodiments of the present invention can achieve one or more of the following beneficial effects: by rotating the initial image to be tested, the signal intensity distribution dataset of the image under different rotation angles (i.e., different target detection images) can be obtained. After rotation, the strong signals of the defect location can be concentrated in the same row or column. Therefore, by comparing and analyzing the intensity distribution data in the signal intensity distribution dataset, the first defect information of the defect in the target detection image can be determined. Based on the first defect information of the defect in the target detection image, the actual position of the defect in the initial image to be tested can be deduced in reverse. The steps are simple and the results are highly accurate. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a defect detection method according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of a target detection image in one embodiment of the present invention.

[0021] Figure 3 This is a signal intensity distribution map corresponding to a target detection image in one embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of a defect detection device provided in one embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of a defect detection system provided in one embodiment of the present invention.

[0024] Figure 6 A schematic diagram of the structure of a computer-readable medium provided for one embodiment of the invention.

[0025] Figure 7 This is a schematic diagram of a defect detection system provided in another embodiment of the present invention. Detailed Implementation

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It should also be noted that the division of multiple embodiments in this invention is only for the convenience of description and should not constitute a special limitation. Features in various embodiments can be combined and referenced in each other without contradiction.

[0030] [First Embodiment]

[0031] The first embodiment of the present invention provides a defect detection method, such as... Figure 1 The defect detection method provided in one embodiment of the present invention includes, for example, steps S11 to S17.

[0032] S11: Obtain the initial image to be tested;

[0033] S13: Obtain the signal intensity distribution dataset of the target detection image corresponding to the initial image to be tested at the target rotation angle;

[0034] S15: Determine the first defect information of the target detection image based on the signal intensity distribution dataset;

[0035] S17: Determine the defect feature information on the initial image to be tested based on the first defect information, the target rotation angle, and the signal intensity distribution dataset.

[0036] Among them, the above-mentioned defect detection method is performed, for example, in... Figure 7The defect detection system 300 shown can be used to detect defects on wafers, especially intermittent or inconspicuous defects on unpatterned wafers. The defect detection system 300 includes, for example, a processor 302 and an image acquisition device 301 connected to the processor 302. The image acquisition device 301 is used to acquire an image of the wafer under test and send the image to the processor 302. The image acquisition device 301 is, for example, a camera, video camera, or other device with image acquisition capabilities. The processor 302 is used to execute the defect detection method described in steps S13 to S17 based on the image of the wafer under test transmitted from the image acquisition device 301. Before step S13, preprocessing such as segmentation of the image of the wafer under test may be performed. The processor 302 is, for example, a computer, processing chip, or device capable of executing steps S13 to S17.

[0037] The signal strength distribution dataset in step S13 includes multiple strength distribution data, each of which includes location data and intensity data. Step S13 specifically includes, for example, steps S131 and S132.

[0038] S131: Obtain the target detection image corresponding to the initial image to be tested at the target rotation angle;

[0039] S132: The multiple intensity distribution data are obtained by analyzing the pixels of the target detection image row by row, and the multiple intensity distribution data correspond one-to-one with the pixels of each row of the target detection image.

[0040] Specifically, in step S132, the direction along a preset direction is, for example, the horizontal direction, i.e., step S132a: analyzing each pixel of the target detection image row by row to obtain the plurality of intensity distribution data, wherein each plurality of intensity distribution data corresponds one-to-one with each pixel in each row of the target detection image. Alternatively, in step S132, the direction along a preset direction is, for example, the vertical direction, i.e., step S132b: analyzing each pixel of the target detection image column by column to obtain the plurality of intensity distribution data, wherein each plurality of intensity distribution data corresponds one-to-one with each pixel in each column of the target detection image.

[0041] Reference Figure 2 This is a schematic diagram of a target detection image obtained in step S131 of one embodiment. The target rotation angle can be any value from 0° to 360° (inclusive). When the target rotation angle is 0° or 360°, it indicates that the initial test image has not rotated. Since the target rotation angle can be multiple different angles, step S131 corresponds to obtaining multiple target detection images under multiple target rotation angles. Each target detection image corresponds to a signal intensity distribution dataset; that is, the initial test image corresponds to a signal intensity distribution dataset under each target rotation angle.

[0042] Specifically, step S131 includes, for example, steps S1311 and S1312.

[0043] S1311: Rotate the initial image to be tested step by step according to a preset angle gradient until the total angle of rotation reaches the rated angle to obtain multiple rotated images, wherein each rotation of the preset angle gradient yields one rotated image, and the target rotation angle is the angle between the rotated image and the initial image to be tested.

[0044] S1312: Preprocess the multiple rotated images respectively to obtain multiple target detection images at multiple target rotation angles.

[0045] In step S1311, the preset angle gradient is, for example, 1°, and the rated angle is, for example, 90°. The initial image to be tested is rotated by 1° each time to obtain a rotated image, requiring a total of 90 rotations to obtain 90 rotated images. The angle between the rotated image obtained from the first rotation and the initial image to be tested is 1°, the angle between the rotated image obtained from the second rotation and the initial image to be tested is 2°, and so on, with the angle between the rotated image obtained from the nth rotation and the initial image to be tested being n°. Of course, the preset angle gradient and rated angle can be set to other values.

[0046] In step S1312, multiple rotated images are preprocessed. Specifically, this involves forming the bounding rectangle of the rotated image at the corresponding target rotation angle, and filling the portion within the bounding rectangle excluding the rotated image to obtain the target detection image corresponding to the rotated image. Filling refers to assigning a standard value to all pixels within the region. This standard value could be, for example, the average gray value of all pixels in the initial test image, the average gray value of noise points in the initial test image, or a value determined empirically, so that the filled portion of the target detection image is approximately the same as the initial test image. (Refer to...) Figure 2 The target detection image contains a tilted rectangular region, which is the rotated image. The outer rectangular region of the rotated image is filled to form the target detection image.

[0047] Taking a target detection image obtained in step S131 as an example, if the target detection image includes M rows * N columns of sub-pixels, then M intensity distribution data can be obtained through step S132a, and N intensity distribution data can be obtained through step S132b. Step S132a specifically includes, for example, performing steps S132a1 to S132a3 for each target row.

[0048] S132a1: Obtain the pixel values ​​of all pixels in the target row of the target detection image;

[0049] S132a2: Calculate the intensity data of the intensity distribution data corresponding to the target row based on the pixel value;

[0050] S132a3: The position data of the intensity distribution data corresponding to the target row is the row position of the target row.

[0051] For example, for M rows * N rows of pixels in an object detection image, let G be the pixel value of the pixel located in the i-th row and j-th column. ij When step S132a analyzes the first row as the target row, the pixel values ​​of N pixels in the first row are obtained through step S132a1, for example, denoted as G. 11 G 12 G 13 G 1j ...G 1N When step S132a analyzes the target row with the i-th row, the pixel values ​​of the N pixels in the i-th row are obtained through step S132a1, for example, denoted as G. i1 G i2 G i3 G ij ...G iN When step S132a analyzes the target row with row M, the pixel values ​​of the N pixels in row M are obtained through step S132a1, for example, denoted as G. M1 G M2 G M3 G Mj ...G MN Taking the i-th row as the target row as an example, step S132a2 can be achieved by adjusting G. i1 To G iN The intensity data A in the i-th row can be obtained by calculating the mean, sum, or standard deviation. i Taking the mean as an example, then A i =(G i1 +G i2 +G i3 +......+G ij +G iN ) / N. Taking the summation as an example, then A i =(G i1 +G i2 + G i3 +......+G ij +G iN Step S132a3 determines the row position i of the target row as position data. That is, after analyzing the target row using row i in step S13a2, the intensity distribution data corresponding to the i-th row is obtained as (i, A). iThe target detection image signal intensity distribution dataset is A = [(1, A1), (2, A2), (3, A3) ... (i, A...). i )......(M, A M )).

[0052] Step S132b specifically includes performing steps S132b1 to S132b3 with each column as the target column.

[0053] S132b1: Obtain the pixel values ​​of all pixels in the target column of the target detection image;

[0054] S132b2: Calculate the intensity data of the intensity distribution data corresponding to the target column based on the pixel values;

[0055] S132b3: The position data of the intensity distribution data corresponding to the target column is the column position of the target column.

[0056] For example, for M rows * N rows of pixels in an object detection image, let G be the pixel value of the pixel located in the i-th row and j-th column. ij When step S132b analyzes the first column as the target column, the pixel values ​​of the M pixels in the first column are obtained through step S132b1, for example, denoted as G. 11 G 21 G 31 G i1 ...G M1 When step S132b analyzes the target column (j-th column), the pixel values ​​of the M pixels in the j-th column are obtained through step S132b1, for example, denoted as G... 1j G 2j G 3j G ij ...G Mj When step S132b analyzes the Nth column as the target row, the pixel values ​​of the M pixels in the Nth column are obtained through step S132b1, for example, denoted as G. 1N G 2N G 3N G iN ...G MN Taking column j as the target column as an example, step S132b2 can be achieved by modifying G... 1j To G Mj The intensity data B in column j can be obtained by calculating the mean, sum, or standard deviation. j Taking the mean as an example, then B i =(G 1j +G 2j +G 3j+......+G ij +G Mj ) / N. Taking the summation as an example, then B i =(G 1j +G 2j +G 3j +......+G ij +G Mj Step S132b3 determines the column position j of the target row as positional data. That is, after analyzing column j as the target column in step S133, the intensity distribution data corresponding to column j is obtained as (j, B). i The target detection image signal intensity distribution dataset B = [(1, B1), (2, B2), (3, B3) ... (j, B1)] is given. j )......(N, B N )).

[0057] The signal intensity distribution dataset for a target detection image can be arbitrarily selected from either dataset A or dataset B. For ease of explanation, the signal intensity distribution dataset in the following embodiments will be illustrated using dataset A obtained in steps S131 and S132a as an example.

[0058] Further, step S15 specifically includes step S151: determining the defect intensity distribution data corresponding to the defect location of the target detection image among the plurality of intensity distribution data; and step S152: using the location data of the defect intensity distribution data as the first defect information.

[0059] In one embodiment, step S151 specifically includes step S1511: comparing a preset intensity threshold with the intensity data of the target intensity distribution data; when the intensity data of the target intensity distribution data is greater than the preset intensity threshold, determining the target intensity distribution data as the defect intensity distribution data.

[0060] Specifically, taking step S151, where each intensity distribution data point in multiple intensity distribution data points is considered the target intensity distribution data, and taking the signal intensity distribution dataset as dataset A, then each intensity data point in dataset A is compared with a preset intensity threshold, i.e., A1 to A... M Compared with a preset intensity threshold, if the intensity data A corresponding to the (i+x)th row of the target detection image... i+x If the intensity is greater than a preset intensity threshold, then the intensity distribution data (i+x, A) is determined. i+xThe data represents the defect intensity distribution. The location data i+x is used as the first defect information, indicating that there is a defect in the i+x row of the target detection image. In other words, a certain defect position on the initial image to be tested is located in the i+x row of the target detection image under the target rotation angle.

[0061] The preset intensity threshold can be, for example, a fixed value preset based on actual experience, or it can be the product of a specific magnification of the intensity distribution data of the target intensity distribution data, for example, the specific magnification is set to... A certain intensity distribution data (i+x, A) i+x The corresponding preset intensity threshold is Another intensity distribution data (i+y, A) i+y The corresponding preset intensity threshold is Alternatively, a preset intensity threshold can be set, for example, as the difference between the maximum and average intensity values ​​in the signal intensity distribution dataset of the target image to be detected, i.e., Max[A] i ]-(A1+A2+A3+......+A M ) / M (where i is 1 to M). Alternatively, a preset intensity threshold can be defined as the ratio of the maximum intensity data to the minimum intensity data in the signal intensity distribution dataset corresponding to the target image to be detected, i.e., Max[A] / M. i ] / Min[A i (where i is 1 to M). This embodiment does not limit the specific value of the preset intensity threshold.

[0062] In another embodiment, step S15 specifically includes step S153: recording the variation patterns of the plurality of intensity data in the signal intensity distribution dataset, and determining the defect intensity distribution data based on the variation patterns of the plurality of intensity data. More specifically: generating a signal intensity distribution map using the respective position data of the plurality of intensity distribution data as the abscissa and the intensity data as the ordinate; determining the defect intensity distribution data based on the jitter amplitude of the ordinate obtained from the signal intensity distribution map.

[0063] Reference Figure 3This is a schematic diagram of the signal intensity distribution map generated in one embodiment of the present invention. It can be seen from the diagram that the jitter amplitude of the vertical axis is approximately 0.01, while the jitter amplitude at the horizontal axis position of 800 increases dramatically. It can be determined that the intensity distribution data at the horizontal axis 800 is defect intensity distribution data. Using its position data 800 as the first defect information, it indicates that a defect exists in the 800th row of the target detection image. In other words, a defect location on the initial image to be tested is located in the 800th row of the target detection image at the target rotation angle. By generating a signal intensity distribution map, the normal and abnormal data in the signal intensity distribution map can be read more intuitively, the defect location in the target detection image can be quickly determined, and the normal data obtained from the signal intensity distribution map can also be used as reference data in the defect detection process.

[0064] Furthermore, the defect feature information in step S17 includes the defect location and defect length. Step S17 specifically includes steps S171 and S172.

[0065] S171: Determine the defect length based on the variation pattern of pixel values ​​of the pixels corresponding to the location data of the defect intensity distribution data;

[0066] S172: Determine the position of the defect on the initial image to be tested based on the first defect information, the defect length, and the target rotation angle.

[0067] Specifically, as described in step S15 above, in step S15, the location data of the defect in the target detection image (i.e., the row or column position of the defect in the target detection image) has been obtained. In step S171, the pixel values ​​of all pixels in the row (or column) corresponding to the location data of the target detection image are taken, and their variation patterns are analyzed. For example, each pixel value can be compared with a pixel value threshold, and the number of pixel values ​​greater than the pixel value threshold can be counted. The defect length is determined based on the proportion of this number to the total number of pixels in the row (or column). In step S172, the angle and row position of the defect on the initial image to be tested can be determined based on the first defect information and the target rotation angle, thus obtaining the defect position. Combining the defect position and defect length, the defect can be marked in subsequent processing for reference by the wafer production line.

[0068] In this embodiment, by rotating the initial image to be tested, the signal intensity distribution dataset at different rotation angles (i.e., different target detection images) is obtained. After rotation, the strong signals at the defect location can be concentrated in the same row or column. Therefore, by comparing and analyzing the intensity distribution data in the signal intensity distribution dataset, the row or column position of the defect in the target detection image can be determined. Based on the position of the defect in the target detection image, the actual position of the defect in the initial image to be tested can be calculated in reverse. The steps are simple and the results are highly accurate.

[0069] [Second Embodiment]

[0070] A second embodiment of the present invention provides a defect detection device 100. (Refer to...) Figure 4 The present invention provides a defect detection device 100, which includes, for example, an image acquisition module 101, a dataset acquisition module 102, a first information acquisition module 103, and a second information acquisition module 104. The image acquisition module 101 is used to perform step S11 to acquire an initial image to be tested. The dataset acquisition module 102 is used, for example, to perform step S13 to acquire a signal intensity distribution dataset of the target detection image. The first information acquisition module 103 is used, for example, to perform step S17 to acquire first defect information, i.e., to determine the location information of the defect on the target detection image. The second information acquisition module 104 is used, for example, to perform step S17 to determine the specific location, length, and other feature information of the defect in the initial image to be tested.

[0071] For detailed procedures and specifics regarding the defect detection method, please refer to the description of the first embodiment above, which will not be repeated here. The technical effects of the defect detection device provided in the second embodiment of the present invention are the same as those of the defect detection method in the first embodiment above, and will not be repeated here.

[0072] [Third Embodiment]

[0073] Reference Figure 5 The third embodiment of the present invention provides a defect detection system 200, which includes, for example, a processor 201 and a memory 202 electrically connected to the processor 201. The memory 202 stores instructions executed by the processor 201, which cause the processor 201 to perform an operation to perform any of the defect detection methods in the first embodiment described above.

[0074] The functions and technical effects of the defect detection system 200 provided in the third embodiment of the present invention can be referred to the relevant description of the defect detection method in the first embodiment above, and will not be repeated here.

[0075] [Fourth Embodiment]

[0076] Reference Figure 6The fourth embodiment of the present invention provides a computer-readable medium 500, which stores computer-readable instructions 501, including instructions for performing any of the defect detection methods in the first embodiment.

[0077] The functions and technical effects of the computer-readable medium 500 provided in the fourth embodiment of the present invention can be referred to the technical effects of the defect detection method in the first embodiment mentioned above, and will not be repeated here.

[0078] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and / or methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units / modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0079] The units / modules described as separate components may or may not be physically separate. The components shown as units / modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units / modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] Furthermore, in the various embodiments of the present invention, the functional units / modules can be integrated into one processing unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated into one unit / module. The integrated unit / module described above can be implemented in hardware or in the form of hardware plus software functional units / modules.

[0081] The integrated unit / module implemented as a software functional unit / module described above can be stored in a computer-readable storage medium. The software functional unit, stored in a storage medium, includes several instructions to cause one or more processors of a computer device (which may be a personal computer, server, or network device, etc.) to execute some steps of the methods described in the various embodiments of the present invention.

[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A defect detection method, characterized in that, include: Obtain the initial image to be tested; Obtain the signal intensity distribution dataset of the target detection image corresponding to the initial image under test at the target rotation angle; The first defect information of the target detection image is determined based on the signal intensity distribution dataset; Determine the defect feature information on the initial image to be tested based on the first defect information, the target rotation angle, and the signal intensity distribution dataset; The signal intensity distribution dataset includes multiple intensity distribution data, each of which includes location data and intensity data. The step of obtaining the signal intensity distribution dataset of the target detection image corresponding to the initial test image at the target rotation angle includes: Obtain the target detection image corresponding to the initial image to be tested at the target rotation angle; Analyzing the pixels of the target detection image along a preset direction to obtain the plurality of intensity distribution data, wherein the plurality of intensity distribution data corresponds one-to-one with the pixels of the target detection image along the preset direction, including: obtaining the pixel values ​​of all pixels in the target row of the target detection image; Calculate the intensity data of the intensity distribution data corresponding to the target row based on the pixel values; The position of the target row is taken as the position data of the intensity distribution data corresponding to the target row.

2. The defect detection method as described in claim 1, characterized in that, Determining the first defect information of the target detection image based on the signal intensity distribution dataset includes: Determine the defect intensity distribution data corresponding to the defect location in the target detection image from among the plurality of intensity distribution data; The location data of the defect intensity distribution data is used as the first defect information.

3. The defect detection method as described in claim 2, characterized in that, The step of determining the defect intensity distribution data corresponding to the defect location in the target detection image from the plurality of intensity distribution data includes: Compare the preset intensity threshold with the intensity data of the target intensity distribution data; When the intensity data of the target intensity distribution data is greater than the preset intensity threshold, the target intensity distribution data is determined to be the defect intensity distribution data.

4. The defect detection method as described in claim 2, characterized in that, The step of determining the defect intensity distribution data corresponding to the defect location in the target detection image from the plurality of intensity distribution data includes: Record the variation patterns of the multiple intensity data in the signal intensity distribution dataset; The defect intensity distribution data is determined based on the variation pattern of the multiple intensity data.

5. The defect detection method as described in claim 4, characterized in that, Determining the defect intensity distribution data based on the variation pattern of the multiple intensity data includes: A signal intensity distribution map is generated by using the location data of each of the multiple intensity distribution data as the horizontal axis and the intensity data as the vertical axis. The defect intensity distribution data is determined based on the jitter amplitude of the vertical axis obtained from the signal intensity distribution map.

6. The defect detection method as described in claim 2, characterized in that, The defect feature information includes the defect location and defect length. Determining the defect feature information on the initial test image based on the first defect information, the target rotation angle, and the signal intensity distribution dataset includes: The defect length is determined based on the variation pattern of pixel values ​​of the pixels corresponding to the location data in the defect intensity distribution data. The location of the defect on the initial image to be tested is determined based on the first defect information, the defect length, and the target rotation angle.

7. The defect detection method as described in claim 1, characterized in that, The step of obtaining the target detection image of the initial image to be tested at the target rotation angle includes: The initial image to be tested is rotated step by step according to a preset angle gradient until the total angle of rotation reaches the rated angle, thereby obtaining multiple rotated images. Each rotation of the preset angle gradient yields one rotated image, and the target rotation angle is the angle between the rotated image and the initial image to be tested. The multiple rotated images are preprocessed to obtain multiple target detection images at multiple target rotation angles.

8. A defect detection device, characterized in that, include: The image acquisition module is used to acquire the initial image to be tested; The dataset acquisition module is used to acquire the signal intensity distribution dataset of the target detection image corresponding to the initial test image at the target rotation angle. The signal intensity distribution dataset includes multiple intensity distribution data, and each intensity distribution data includes position data and intensity data. The first information acquisition module is used to determine the first defect information of the target detection image based on the signal intensity distribution dataset. The second information acquisition module is used to determine the defect feature information on the initial image to be tested based on the first defect information, the target rotation angle and the signal intensity distribution dataset; The step of obtaining the signal intensity distribution dataset of the target detection image corresponding to the initial test image at the target rotation angle includes: Obtain the target detection image corresponding to the initial image to be tested at the target rotation angle; Analyzing the pixels of the target detection image along a preset direction to obtain the plurality of intensity distribution data, wherein the plurality of intensity distribution data corresponds one-to-one with the pixels of the target detection image along the preset direction, including: obtaining the pixel values ​​of all pixels in the target row of the target detection image; Calculate the intensity data of the intensity distribution data corresponding to the target row based on the pixel values; The position of the target row is taken as the position data of the intensity distribution data corresponding to the target row.

9. A defect detection system, characterized in that, include: A processor and a memory connected to the processor, the memory storing instructions executed by the processor, the instructions causing the processor to perform operations to perform the defect detection method as described in any one of claims 1-7.

10. A defect detection system, characterized in that, include: processor; Image acquisition device, connected to the processor; The image acquisition device is used to acquire an image of the wafer under test and send the image to the processor. The processor is used to: acquire an initial image under test; acquire a signal intensity distribution dataset of the target detection image corresponding to the initial image under test at a target rotation angle; determine first defect information of the target detection image based on the signal intensity distribution dataset; and determine defect feature information on the initial image under test based on the first defect information, the target rotation angle, and the signal intensity distribution dataset. The signal intensity distribution dataset includes multiple intensity distribution data, each of which includes location data and intensity data. The step of obtaining the signal intensity distribution dataset of the target detection image corresponding to the initial test image at the target rotation angle includes: Obtain the target detection image corresponding to the initial image to be tested at the target rotation angle; Analyzing the pixels of the target detection image along a preset direction to obtain the plurality of intensity distribution data, wherein the plurality of intensity distribution data corresponds one-to-one with the pixels of the target detection image along the preset direction, including: obtaining the pixel values ​​of all pixels in the target row of the target detection image; Calculate the intensity data of the intensity distribution data corresponding to the target row based on the pixel values; The position of the target row is taken as the position data of the intensity distribution data corresponding to the target row.

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