Micro-scratch filtering method, device and equipment in wafer defect detection and storage medium

Abstract

The invention discloses a micro-scratch filtering method and device in wafer defect detection, equipment and a storage medium, and the method comprises the steps: extracting the frequency spectrum characteristics of a wafer image, carrying out the direction characteristic analysis based on the frequency spectrum characteristics, and carrying out the calculation to obtain the center frequency of a target region suspected to have micro-scratches; constructing a Gabor filter according to the center frequency, and reinforcing the target area by using the Gabor filter; respectively carrying out gray integration along the direction vector and the vertical direction vector of the linear defect of the strengthened target area, and calculating the strength attribute of the target area; and when the strength attribute is higher than a preset threshold value, marking the defect of the target area as a micro-scratch and filtering out the micro-scratch. According to the method, the micro scratches in the image can be accurately positioned and filtered out, and the influence of the micro scratches on the accuracy of wafer defect detection is prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wafer detection, in particular to a micro-scratch filtering method and device in wafer defect detection, equipment and storage medium. BACKGROUND

[0002] Wafer defect detection is a crucial link in semiconductor manufacturing. Wafer defect detection technology helps to improve the yield of chip manufacturing, improve production efficiency and reduce production cost. Wafer is prone to cause various defects such as particles, scratches, cracks, contamination, pits and protrusions during production. These defects will affect the function and performance of the chip, and even cause the chip to fail. Therefore, it is necessary to effectively detect and analyze the wafer defects.

[0003] However, during the processing of the wafer, the cutting and grinding processes will form a specific direction of the scratch-like defect (hereinafter referred to as micro-scratch, SCH) on the wafer surface. SCH is characterized by a strip-shaped defect with a strong gray value on the image, which has similar characteristics with the scratch defect, and the defect will not affect the subsequent processing process and will not reduce the final wafer yield. However, the current mainstream defect detection equipment will be detected as a scratch or other fatal defect, which will reduce the wafer yield. SUMMARY

[0004] Therefore, the present application provides a micro-scratch filtering method, device, equipment and storage medium in wafer defect detection to solve the problem that the existing wafer detection cannot effectively identify micro-scratches.

[0005] To solve the above technical problems, one technical solution adopted by the present application is to provide a micro-scratch filtering method in wafer defect detection, which comprises: extracting the frequency spectrum features of the wafer image, and performing direction feature analysis based on the frequency spectrum features to calculate the center frequency of the target area suspected of having micro-scratches; constructing a Gabor filter according to the center frequency, and strengthening the target area using the Gabor filter; respectively performing gray integral along the direction vector and the perpendicular direction vector of the linear defect of the strengthened target area to calculate the intensity attribute of the target area; when the intensity attribute is higher than a preset threshold, the defect of the target area is marked as a micro-scratch and filtered out.

[0006] As a further improvement of this application, the spectral features of the wafer image are extracted, and directional feature analysis is performed based on the spectral features to calculate the center frequency of the target area suspected of having micro-scratches. This includes: performing a Fourier transform on the wafer image to obtain a spectrum; converting the spectrum into a two-dimensional image in polar coordinates, and integrating the two-dimensional image in polar coordinates along the polar axis to obtain a one-dimensional signal; calculating the gray mean and gray variance of the one-dimensional signal, and using the gray mean and gray variance to calculate a dynamic threshold; using the dynamic threshold to segment and calculate the frequency components of the spectrum to obtain the center frequency; and marking the area with the center frequency within a preset range as the target area.

[0007] As a further improvement to this application, the process of converting the spectrum diagram into a two-dimensional diagram in polar coordinates is represented as follows:

[0008]

[0009] Where r represents the polar radius, θ represents the polar angle, and (x,y) represents the two-dimensional coordinates of the spectrum.

[0010] As a further improvement to this application, the calculation process of the dynamic threshold is expressed as follows:

[0011]

[0012] th = mean + coeff * std;

[0013] Where mean represents the grayscale mean, std represents the grayscale variance, th represents the dynamic threshold, and gray i represents the grayscale value of the i-th pixel, n represents the number of pixels, and coeff represents the preset noise estimation parameters.

[0014] As a further improvement to this application, the calculation process for the center frequency is expressed as follows:

[0015]

[0016] Among them, angle resolution ImageHeight represents the angular resolution of a pixel, while imageHeight represents the polar coordinate angle value of a pixel in polar coordinates. valid This represents the polar coordinate angle value of a pixel in a one-dimensional signal whose gray value is greater than the dynamic threshold and whose polar coordinate angle is within the range of 0° to 180°. center The center frequency is represented by m, which represents the number of pixels in the one-dimensional signal whose gray value is greater than the dynamic threshold and whose polar coordinate angle is in the range of 0° to 180°.

[0017] As a further improvement to this application, the Gabor filter is expressed as:

[0018]

[0019] x ′ = xcos(θ) + ysin(θ);

[0020] y ′ = -xsin(θ) + ycos(θ);

[0021] in, Let exp() represent the filter, exp() represent the exponential function, and (x,y) represent the pixel coordinates of a pixel in the wafer image. ′ ,y ′ ) represents the rotated coordinates, λ represents the sine wave wavelength, θ represents the direction angle set according to the SCH center frequency, σ represents the standard deviation of the Gaussian function, and γ represents the spatial aspect ratio. This indicates a phase shift.

[0022] As a further improvement to this application, the calculation process for the intensity attribute is expressed as follows:

[0023]

[0024] Where SCHIntensity represents the intensity attribute, SCHH represents the maximum grayscale integral value, and SCHL represents the minimum grayscale integral value.

[0025] To address the aforementioned technical problems, another technical solution adopted in this application is: providing a micro-scratch filtering device for wafer defect detection, comprising: an extraction module for extracting spectral features of a wafer image and performing directional feature analysis based on the spectral features to calculate the center frequency of a target area suspected of having micro-scratch; an enhancement module for constructing a Gabor filter based on the center frequency and enhancing the target area using the Gabor filter; a calculation module for performing grayscale integration along the direction vector and vertical direction vector of the linear defect in the enhanced target area to calculate the intensity attribute of the target area; and a filtering module for marking the defect in the target area as a micro-scratch and filtering it out when the intensity attribute is higher than a preset threshold.

[0026] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide a computer device, the computer device including a processor and a memory coupled to the processor, the memory storing program instructions, and when the program instructions are executed by the processor, causing the processor to perform the steps of the micro-scratch filtering method in wafer defect detection as described in any of the above claims.

[0027] To solve the above-mentioned technical problems, another technical solution adopted in this application is to provide a storage medium storing program instructions capable of implementing the micro-scratch filtering method in wafer defect detection as described above.

[0028] The beneficial effects of this application are as follows: The micro-scratch filtering method in wafer defect detection converts the wafer image into a spectrum image, identifies and judges the spectrum image to determine whether there are defect areas on the wafer suspected of micro-scratch, and then calculates the intensity attributes of the defect areas suspected of micro-scratch. Combined with a preset threshold and intensity attributes, it confirms whether the defect area is a micro-scratch. It makes full use of frequency domain feature extraction and directional enhancement filtering, which solves the problem of difficulty in distinguishing micro-scratch from real scratch defects and high false detection rate in the prior art, and filters out micro-scratch to avoid affecting the yield detection rate of wafers. Attached Figure Description

[0029] Figure 1 This is a schematic flowchart of a micro-scratch removal method in wafer defect detection according to an embodiment of the present invention;

[0030] Figure 2 This is the original image of the micro-scratches in the micro-scratch removal method for wafer defect detection according to an embodiment of the present invention;

[0031] Figure 3 This is a micro-scratch spectrum diagram in the micro-scratch filtering method for wafer defect detection according to an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of a two-dimensional polar coordinate system of the micro-scratch filtering method in wafer defect detection according to an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the polar coordinate system two-dimensional diagram of the micro-scratch filtering method in wafer defect detection according to an embodiment of the present invention compressed to one dimension;

[0034] Figure 6 This is a schematic diagram of the micro-scratch removal method in wafer defect detection according to an embodiment of the present invention before micro-scratch strengthening;

[0035] Figure 7 This is a schematic diagram of the micro-scratch strengthening process in the micro-scratch filtering method for wafer defect detection according to an embodiment of the present invention;

[0036] Figure 8 This is a schematic diagram of the functional modules of the micro-scratch filtering device in wafer defect detection according to an embodiment of the present invention;

[0037] Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0038] Figure 10 This is a schematic diagram of the structure of the storage medium according to an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0040] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0041] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] Figure 1 This is a schematic flowchart of a micro-scratch removal method for wafer defect detection according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, the method of the present invention is not necessarily identical. Figure 1 The illustrated process sequence is limited. It should be noted that micro-scratches are typically uniformly distributed on the wafer surface, and within a ring-shaped region, they usually have equal center angles that fluctuate within a certain range. Therefore, by extracting spectral features from the wafer image and then enhancing the directional characteristics of suspected micro-scratches, it is possible to effectively distinguish whether suspected micro-scratches are actual defects. For example... Figure 1As shown, the micro-scratch removal method in wafer defect detection includes the following steps:

[0043] Step S1: Extract the spectral features of the wafer image, and perform directional feature analysis based on the spectral features to calculate the center frequency of the target area suspected of having micro-scratches.

[0044] Specifically, during wafer inspection, the wafer image can be pre-acquired using a camera. Before extracting spectral features, the wafer image needs to be converted to grayscale and then preprocessed to remove random noise and normalize the grayscale values ​​in the image, avoiding the influence of brightness differences on the spectral intensity. After obtaining the spectral features, directional feature analysis is performed on the spectral features to confirm whether there are target areas suspected of being micro-scratches in the wafer image, and to obtain the center frequency of each target area.

[0045] Furthermore, step S1 specifically includes:

[0046] 1. Perform a Fourier transform on the wafer image to obtain the spectrum.

[0047] Specifically, such as Figure 2 and Figure 3 As shown, Figure 2 The original image with minor scratches is shown. Figure 3 The spectrum of micro-scratches is shown. In this embodiment, after preprocessing the wafer image, a fast Fourier transform is performed on the preprocessed wafer image to transform the wafer image from the spatial domain to the frequency domain. Then, centering processing is performed to move the low-frequency components to the center of the spectrum for easier observation. Finally, a logarithmic transform is performed to enhance the visualization effect of the low-frequency components, and the spectrum is obtained.

[0048] 2. Convert the spectrum diagram into a two-dimensional diagram in polar coordinates, and integrate the two-dimensional diagram in polar coordinates along the polar axis to obtain a one-dimensional signal.

[0049] Specifically, such as Figure 4 As shown, after obtaining the spectrum, it is converted from Cartesian coordinates to polar coordinates to facilitate the analysis of the periodic characteristics of the angular distribution. Specifically, a polar coordinate system is constructed using the origin of the center point of the spectrum, and then an interpolation algorithm (such as bilinear interpolation) is used to map the spectrum to the polar coordinate system. Figure 5 As shown, after obtaining the two-dimensional graph in polar coordinates, the graph is integrated along the polar axis to compress it to one dimension, thus obtaining a one-dimensional signal, thereby increasing the frequency intensity of the micro-scratches.

[0050] It should be noted that the process of converting a spectrum diagram into a two-dimensional diagram in polar coordinates is represented as follows:

[0051]

[0052] Where r represents the polar radius, θ represents the polar angle, and (x,y) represents the two-dimensional coordinates of the spectrum.

[0053] 3. Calculate the mean and variance of the gray level of the one-dimensional signal, and use the mean and variance of the gray level to calculate the dynamic threshold.

[0054] Specifically, after obtaining the one-dimensional signal, the gray-level mean and gray-level variance values ​​of all pixels in the one-dimensional signal are calculated. Then, a dynamic threshold is designed using the gray-level mean and gray-level variance values. This dynamic threshold can be automatically adjusted according to the gray-level mean and gray-level variance values, thereby adaptively adjusting the segmentation boundary. For example, if the gray-level value of the target feature (such as a defect) in the signal is significantly higher than the background noise, the dynamic threshold can effectively segment the target feature.

[0055] It should be noted that the calculation process for the dynamic threshold is expressed as follows:

[0056]

[0057] th = mean + coeff * std;

[0058] Where mean represents the grayscale mean, std represents the grayscale variance, th represents the dynamic threshold, and gray i This represents the grayscale value of the i-th pixel, n represents the number of pixels, and coeff represents the preset noise estimation parameter. The smaller this parameter is, the more sensitive the segmentation effect is to noise. It is usually set to 10.

[0059] 4. The frequency components of the spectrum are segmented and calculated using a dynamic threshold to obtain the center frequency.

[0060] Specifically, after obtaining the dynamic threshold, the frequency components of the spectrum are binarized using the dynamic threshold, retaining the significant frequency components, and then the center frequency is obtained by weighted averaging using the significant frequency components.

[0061] It should be noted that the calculation process for the center frequency is expressed as follows:

[0062]

[0063] Among them, angle resolution ImageHeight represents the angular resolution of a pixel, while imageHeight represents the polar coordinate angle value of a pixel in polar coordinates. valid This represents the polar coordinate angle value of a pixel in a one-dimensional signal whose gray value is greater than the dynamic threshold and whose polar coordinate angle is within the range of 0° to 180°. centerThe center frequency is represented by , and m represents the number of pixels in the one-dimensional signal whose grayscale value is greater than the dynamic threshold and whose polar coordinate angle is between 0° and 180°. It should be noted that the grayscale distribution from 0° to 180° overlaps with the grayscale distribution from 180° to 360°; therefore, in this embodiment, i is taken as . <imageHeight / 2。

[0064] 5. Mark the area with the center frequency within the preset range as the target area.

[0065] It's important to understand that in wafer surface defect detection, the center frequency is a key parameter extracted through frequency domain analysis. It exhibits defect direction characteristics and frequency domain energy concentration. The center frequency reflects the dominant angle of micro-scratches distributed on the wafer surface. Micro-scratches are typically caused by wafer fabrication processes (such as dicing and grinding), and they possess periodicity and directional consistency. Their linear defects manifest in the frequency domain as spectral components perpendicular to the defect direction (e.g., ...). Figure 3 (As shown). The center frequency corresponds to the direction of the most concentrated energy in the frequency domain; that is, the spectral components of the micro-scratches have significant intensity peaks near this direction. Micro-scratches are usually uniformly distributed and their direction fluctuates around the center angle, and their center frequencies tend to be consistent statistically. In contrast, real scratches have random directions and irregular shapes, and their frequency domain energy distribution has no significant directionality. Therefore, in this embodiment, by calculating the center frequency, if the center frequency is significantly higher than the preset normal range, it is determined that there is a suspected micro-scratch defect in that area, and the area is marked as the target area to facilitate further subsequent determination.

[0066] Step S2: Construct a Gabor filter based on the center frequency and use the Gabor filter to enhance the target region.

[0067] It should be noted that the Gabor filter is represented as:

[0068]

[0069] x ′ = xcos(θ) + ysin(θ);

[0070] y ′ = -xsin(θ) + ycos(θ);

[0071] in, Let exp() represent the filter, exp() represent the exponential function, and (x,y) represent the pixel coordinates of a pixel in the wafer image. ′ ,y ′ ) represents the rotated coordinates, λ represents the sine wave wavelength, θ represents the direction angle set according to the SCH center frequency, σ represents the standard deviation of the Gaussian function, and γ represents the spatial aspect ratio. This indicates a phase shift.

[0072] Specifically, the intensity of micro-scratches in an image is unstable, which can affect subsequent attribute calculations. Therefore, it is necessary to enhance the micro-scratches to ensure the stability of subsequent attribute calculations. Thus, this embodiment constructs a matched Gabor filter based on the center frequency. This Gabor filter is only used to enhance the frequency domain components in the SCH direction and suppress noise in other directions, such as... Figure 6 and Figure 7 As shown, Figure 6 Images of micro-scratches without enhancement are shown. Figure 7 The image shows the enhanced version after micro-scratches.

[0073] Step S3: Perform grayscale integration along the direction vector and vertical direction vector of the linear defect in the strengthened target area to calculate the intensity attribute of the target area.

[0074] It should be noted that the calculation process for the intensity attribute is expressed as follows:

[0075]

[0076] Where SCHIntensity represents the intensity attribute, SCHH represents the maximum grayscale integral value, and SCHL represents the minimum grayscale integral value.

[0077] Specifically, micro-scratches are linear defects, with their intensity mainly distributed along their direction vector. By integrating the pixel grayscale values ​​of their direction vector and vertical direction vector, the maximum and minimum grayscale integral values ​​are obtained, respectively. The ratio of these values ​​represents the intensity attribute of the micro-scratch. The higher the intensity attribute, the greater the likelihood that it is a micro-scratch.

[0078] Step S4: When the intensity attribute is higher than the preset threshold, the defect in the target area is marked as a micro-scratch and filtered out.

[0079] The micro-scratch filtering method in wafer defect detection of this embodiment converts the wafer image into a spectrum image, identifies and judges the spectrum image to determine whether there are defect areas on the wafer suspected of micro-scratch, and then calculates the intensity attribute of the defect area suspected of micro-scratch. Combined with a preset threshold and intensity attribute, it confirms whether the defect area is a micro-scratch. It makes full use of frequency domain feature extraction and directional enhancement filtering, which solves the problem of difficulty in distinguishing micro-scratch from real scratch defects and high false detection rate in the prior art, and filters out micro-scratch to avoid affecting the wafer yield detection rate.

[0080] Figure 8 This is a schematic diagram of the functional modules of the micro-scratch filtering device in wafer defect detection according to an embodiment of the present invention. Figure 8 As shown, the micro-scratch removal device 20 for wafer defect detection includes: an extraction module 21, a strengthening module 22, a calculation module 23, and a filtering module 24.

[0081] Extraction module 21 is used to extract the spectral features of the wafer image and perform directional feature analysis based on the spectral features to calculate the center frequency of the target area suspected of having micro-scratches.

[0082] Enhancement module 22 is used to construct a Gabor filter based on the center frequency and enhance the target region using the Gabor filter;

[0083] Calculation module 23 is used to perform grayscale integration along the direction vector and vertical direction vector of the linear defect in the strengthened target area to calculate the intensity properties of the target area.

[0084] The filtering module 24 is used to mark defects in the target area as micro-scratches and filter them out when the intensity attribute is higher than a preset threshold.

[0085] Optionally, the extraction module 21 performs the operation of extracting the spectral features of the wafer image and performing directional feature analysis based on the spectral features to calculate the center frequency of the target area suspected of having micro-scratches. Specifically, this includes: performing a Fourier transform on the wafer image to obtain a spectrum; converting the spectrum into a two-dimensional image in polar coordinates and integrating the two-dimensional image in polar coordinates along the polar axis to obtain a one-dimensional signal; calculating the gray mean and gray variance of the one-dimensional signal and using the gray mean and gray variance to calculate a dynamic threshold; using the dynamic threshold to segment and calculate the frequency components of the spectrum to obtain the center frequency; and marking the area with the center frequency within a preset range as the target area.

[0086] Optionally, the process of converting a spectrum diagram into a two-dimensional diagram in polar coordinates is represented as follows:

[0087]

[0088] Where r represents the polar radius, θ represents the polar angle, and (x,y) represents the two-dimensional coordinates of the spectrum.

[0089] Optionally, the calculation process of the dynamic threshold is expressed as follows:

[0090]

[0091] th = mean + coeff * std;

[0092] Where mean represents the grayscale mean, std represents the grayscale variance, th represents the dynamic threshold, and gray i represents the grayscale value of the i-th pixel, n represents the number of pixels, and coeff represents the preset noise estimation parameters.

[0093] Optionally, the calculation process for the center frequency is expressed as follows:

[0094]

[0095] Among them, angle resolution ImageHeight represents the angular resolution of a pixel, while imageHeight represents the polar coordinate angle value of a pixel in polar coordinates. valid This represents the polar coordinate angle value of a pixel in a one-dimensional signal whose gray value is greater than the dynamic threshold and whose polar coordinate angle is within the range of 0° to 180°. center The center frequency is represented by m, which represents the number of pixels in the one-dimensional signal whose gray value is greater than the dynamic threshold and whose polar coordinate angle is in the range of 0° to 180°.

[0096] Alternatively, the Gabor filter can be represented as:

[0097]

[0098] x ′ = xcos(θ) + ysin(θ);

[0099] y ′ = -xsin(θ) + ycos(θ);

[0100] in, Let exp() represent the filter, exp() represent the exponential function, and (x,y) represent the pixel coordinates of a pixel in the wafer image. ′ ,y ′ ) represents the rotated coordinates, λ represents the sine wave wavelength, θ represents the direction angle set according to the SCH center frequency, σ represents the standard deviation of the Gaussian function, and γ represents the spatial aspect ratio. This indicates a phase shift.

[0101] Optionally, the calculation process for the intensity attribute is expressed as follows:

[0102]

[0103] Where SCHIntensity represents the intensity attribute, SCHH represents the maximum grayscale integral value, and SCHL represents the minimum grayscale integral value.

[0104] For further details regarding the implementation of the technical solutions for each module in the micro-scratch filtering device for wafer defect detection in the above embodiments, please refer to the description in the micro-scratch filtering method for wafer defect detection in the above embodiments, which will not be repeated here.

[0105] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0106] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Figure 9 As shown, the computer device 30 includes a processor 31 and a memory 32 coupled to the processor 31. The memory 32 stores program instructions. When the program instructions are executed by the processor 31, the processor 31 performs the steps of the micro-scratch filtering method in wafer defect detection as described in any of the above embodiments.

[0107] The processor 31 can also be referred to as a Central Processing Unit (CPU). The processor 31 may be an integrated circuit chip with signal processing capabilities. The processor 31 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0108] See Figure 10 , Figure 10 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present invention. The storage medium of this embodiment stores program instructions 41 capable of implementing the micro-scratch filtering method in wafer defect detection described above. These program instructions 41 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or computer devices such as computers, servers, mobile phones, and tablets.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed computer devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0110] Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for filtering out microscratches in wafer defect detection, the method comprising: It comprises: ​ extracting the spectral features of the wafer image, and performing directional feature analysis based on the spectral features to calculate the center frequency of the target region suspected of having micro scratches; constructing a Gabor filter according to the center frequency, and strengthening the target region using the Gabor filter; respectively performing gray integral along the direction vector and the vertical direction vector of the linear defect of the strengthened target region to calculate the intensity attribute of the target region; when the intensity attribute is higher than a preset threshold, marking the defect of the target region as a micro scratch and filtering it out.

2. The wafer defect detection micro-scratch filter removal method of claim 1, wherein, The extraction of the spectral features of the wafer image, and the calculation of the center frequency of the target region suspected of having micro scratches based on the spectral features and directional feature analysis, comprises: performing Fourier transform on the wafer image to obtain a frequency spectrum; convert the frequency spectrum into a polar coordinate system two-dimensional graph, and integrate the polar coordinate system two-dimensional graph in the polar axis direction to obtain a one-dimensional signal; calculate the gray mean and gray variance of the one-dimensional signal, and use the gray mean and the gray variance to calculate a dynamic threshold; use the dynamic threshold to segment and calculate the frequency components of the frequency spectrum to obtain the center frequency; regions with the center frequency within a preset range are marked as the target region.

3. The wafer defect detection micro-scratch filter removal method of claim 2, wherein, The process of converting the frequency spectrum into a polar coordinate system two-dimensional graph is represented as: where r represents the polar coordinate radius, θ represents the polar coordinate angle, and (x, y) represents the two-dimensional coordinates of the frequency spectrum.

4. The wafer defect detection micro-scratch filter removal method of claim 2, wherein, The calculation process of the dynamic threshold is represented as: th = mean + coeff * std; wherein mean represents the mean value of the gray scale, std represents the variance value of the gray scale, th represents the dynamic threshold value, gray i represents the gray scale value of the i-th pixel, n represents the number of pixels, and coeff represents a preset noise estimation parameter.

5. The wafer defect detection micro-scratch filter removal method of claim 4, wherein, The calculation process of the center frequency is represented as: wherein angle resolution represents the angle resolution of the pixel, imageHeight represents the polar coordinate angle value of the pixel in the polar coordinate system, pixel valid represents the polar coordinate angle value of the pixel whose gray value is greater than the dynamic threshold and whose polar coordinate angle is in the interval range of 0° to 180° in the one-dimensional signal, fre center represents the center frequency, m represents the number of pixels whose gray value is greater than the dynamic threshold and whose polar coordinate angle is in the interval range of 0° to 180° in the one-dimensional signal.

6. The wafer defect detection microscratch filter method of claim 1, wherein, The Gabor filter is represented as: x ′ = x cos(θ) + y sin(θ); y ′ = -x sin(θ) + y cos(θ); wherein, represents a filter, exp() represents an exponential function, (x, y) represents pixel coordinates of a pixel in a wafer image, (x ′ ,y ′ ) represents a coordinate after rotation, λ represents a sine wave wavelength, θ represents a direction angle set according to a SCH center frequency, σ represents a Gaussian function standard deviation, γ represents a spatial transverse axis ratio, represents a phase shift.

7. The wafer defect detection microscratch filter method of claim 1, wherein, The calculation process of the intensity attribute is represented as: where SCHIntensity represents the intensity attribute, SCHH represents the maximum gray integral value, and SCHL represents the minimum gray integral value.

8. A micro-scratch filtering device for wafer defect detection, characterized in that, It comprises: an extraction module for extracting the spectral features of the wafer image, and performing directional feature analysis based on the spectral features to calculate the center frequency of the target region suspected of having micro scratches; a strengthening module for constructing a Gabor filter according to the center frequency, and strengthening the target region using the Gabor filter; a calculation module for respectively performing gray integral along the direction vector and the vertical direction vector of the linear defect of the strengthened target region to calculate the intensity attribute of the target region; a filtering module for marking the defect of the target region as a micro scratch and filtering it out when the intensity attribute is higher than a preset threshold.

9. A computer device, comprising: The computer device comprises a processor and a memory coupled to the processor, the memory storing program instructions, the program instructions being executed by the processor to perform the steps of the wafer defect detection micro scratch filtering method according to any one of claims 1-7.

10. A storage medium, characterized by The memory stores program instructions capable of implementing the wafer defect detection micro scratch filtering method according to any one of claims 1-7.

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