Visual detection method for solar silicon wafer

Abstract

The invention discloses a solar silicon wafer visual detection method, and particularly relates to the technical field of silicon wafer detection. The method comprises the following steps: acquiring optical images of different incident angles, synchronously acquiring infrared transmission images, performing normalization processing, edge enhancement filtering and self-adaptive threshold segmentation on the acquired multi-angle images, extracting suspected crack areas, and respectively calculating a gray gradient change rate and a stress abnormal degree, so as to obtain a suspected crack area; calculating a crack credibility score according to the gray gradient change rate and the stress abnormal degree through a weighted scoring method, and comparing the crack credibility score with a preset crack detection threshold value to distinguish real hidden cracks and stress pseudo defects; if the credibility score is lower than the threshold value, secondary sampling is carried out by adjusting the illumination angle, and the judgment result is further optimized; according to the method, the false rejection rate caused by lattice stress pseudo defect misjudgment is effectively reduced, meanwhile, missing detection of real subfissures is avoided, and the accuracy, stability and production yield of silicon wafer defect detection are improved, so that the long-term reliability of a photovoltaic module is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of silicon wafer detection, and in particular to a solar silicon wafer visual detection method. Background Art

[0002] With the rapid development of the photovoltaic industry, the quality of solar silicon wafers, as the core material of solar cells, directly affects the photoelectric conversion efficiency and service life of the cells. During the silicon wafer manufacturing process, various defects such as cracks, hidden cracks, missing corners, surface contamination, and uneven etching may occur. Therefore, efficient and accurate visual inspection of silicon wafers has become a key link to improve product quality and reduce production costs.

[0003] Traditional silicon wafer inspection methods mainly include manual visual inspection, laser scattering inspection, ultrasonic inspection and machine vision inspection. Among them, manual visual inspection is inefficient and easily affected by subjective factors, while laser scattering inspection and ultrasonic inspection have high accuracy but slow inspection speed, which is difficult to meet the high-speed inspection requirements of modern production lines. Therefore, automated inspection methods based on machine vision have gradually become mainstream.

[0004] The prior art has the following deficiencies: During the visual inspection of solar silicon wafers, there may be confusion between hidden cracks and lattice stress-induced pseudo defects. Hidden cracks refer to tiny cracks inside the silicon wafer, which may not immediately affect the battery performance, but may expand into serious cracks during subsequent battery packaging, welding or long-term use, causing battery failure. Lattice stress-induced pseudo defects are caused by thermal or mechanical stress on the silicon wafer during the manufacturing process, which causes local lattice deformation or refractive index changes, and appear as crack-like structures under specific lighting conditions.

[0005] Current visual inspection systems mainly rely on deep learning or image processing technology to detect cracks. However, since hidden cracks and lattice stress pseudo defects may be highly similar in visual features (such as morphology and edge features), the inspection system is prone to misjudgment. If the system mistakenly identifies lattice stress pseudo defects as hidden cracks, it may lead to an increase in false rejection rate and affect production yield; if the system fails to accurately identify real hidden cracks, it may release potential defective products, ultimately affecting the long-term reliability of the components. Summary of the invention

[0006] The purpose of the present invention is to provide a solar silicon wafer visual inspection method to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a solar silicon wafer visual inspection method, comprising: A multi-angle lighting device is used to illuminate the solar silicon wafer to generate multiple optical images at different incident angles, obtain the original detection images of the silicon wafer at multiple lighting angles, and simultaneously collect infrared transmission images; The collected multi-angle images are normalized, the image features are enhanced through edge enhancement filtering, and the image is binarized using an adaptive threshold method to extract the suspected crack area on the silicon wafer surface; The grayscale gradient change rate of the suspected crack area is calculated under multiple illumination angles, and the stress anomaly degree of the suspected crack area is calculated through infrared transmission data based on the stress distribution inside the silicon wafer; Combined with the calculation results of the grayscale gradient change rate and the degree of stress anomaly, a weighted scoring method is used to generate a crack credibility score, which is compared with the crack detection threshold. If the crack credibility score is higher than the crack detection threshold, it is judged as a hidden crack defect, and the defect location and characteristics are recorded; if the crack credibility score is lower than the crack detection threshold, the illumination angle is adjusted for secondary sampling to further confirm the defect type.

[0008] Preferably, the multi-angle lighting device comprises a plurality of adjustable angle LED light source arrays, and the incident angle range of the LED light source array is , where: low angle incident light For enhanced visibility of fine surface scratches and shallow cracks; medium angle incident light Used to provide uniform illumination, suitable for overall defect detection; high angle incident light Used to enhance the optical contrast of deep cracks and hidden crack areas.

[0009] Preferably, the grayscale gradient change rate of the suspected crack area is calculated respectively under multiple illumination angles, and the grayscale gradient change rate is obtained by: The original input image is an RGB color image, which is converted to a grayscale image I(x,y). The Sobel operator is used to calculate the horizontal and vertical grayscale gradients of the image using two 3×3 convolution kernels: Horizontal gradient: ; Vertical gradient: ; Perform convolution calculation on the image and calculate and : ; ; In the formula, Represents the convolution operation; is the gradient in the X direction; is the gradient in the Y direction, combined with and , calculate the gradient magnitude of each pixel: ; Where: G(x,y) represents the total gradient size of the pixel. Under multiple lighting angles, the gradient change rate of the crack area is estimated by the gradient change of adjacent pixels: ;in: Indicates the grayscale gradient change rate.

[0010] Preferably, according to the stress distribution inside the silicon wafer, the degree of stress anomaly in the suspected crack area is calculated through infrared transmission data, specifically including: collecting a polarized infrared transmission image IIR(x, y), where IIR(x, y) represents the intensity of polarized infrared transmission light at the image coordinate (x, y), normalizing the image to obtain a normalized image , which is defined as: ;in, and are the minimum and maximum grayscale values ​​of the original image respectively; Due to the birefringence effect of polarized light caused by internal stress in the silicon wafer, the phase delay at each pixel is calculated: ; Where: δ(x,y) is the phase delay, d(x,y) is the local thickness of the silicon wafer; Δn(x,y) is the refractive index change, which is proportional to the stress; λ is the wavelength of infrared light, and the stress σ(x,y) and the phase delay δ(x,y) satisfy: ;in, is the photoelastic coefficient of the silicon wafer; Perform Fourier transform on the polarized infrared image to identify the stress concentration area F(u,v). The low-frequency component represents the uniform stress distribution, and the high-frequency component represents the stress mutation area. Extract high-frequency stress anomaly areas through bandpass filtering: ; where H(u,v) is a bandpass filter: ; , is the low-frequency and high-frequency cutoff value; perform inverse Fourier transform to restore the stress abnormal area , calculate the standard deviation of stress anomaly distribution : ;in: is the number of pixels in the suspected crack area; is the average stress value of the region; Calculate the rate of change of stress : ; Calculate the stress anomaly value , the expression is: ;in: is the weighting coefficient, satisfying .

[0011] Preferably, based on the real-time factor, combined with the calculation results of the gray gradient change rate and the stress anomaly degree, a weighted scoring method is used to generate a crack credibility score, specifically including: The grayscale gradient change rate and stress anomaly value are normalized to be between [0, 1], and the crack credibility score is calculated based on the normalized grayscale gradient change rate and stress anomaly value.

[0012] Preferably, the illumination angle is adjusted to perform secondary sampling to further confirm the defect type, specifically including: To enhance the visibility of cracks or defective areas, a dynamic lighting angle adjustment method is used: ;in: is the adjusted lighting angle, is the original sampling illumination angle; Adjust the step size for the angle; At the new light angle Next, recalculate the grayscale gradient change rate: ;in: is the normalized gray gradient change rate at the new angle; , The grayscale gradient in the X and Y directions calculated by the Sobel operator. Preferably, after adjusting the illumination angle, polarized infrared transmission detection is used to calculate the stress anomaly degree at the new angle: ;in: is the normalized stress anomaly degree under the new illumination angle, is the stress anomaly degree calculated at the new angle; It is still a global normalization parameter. Preferably, the crack credibility score is recalculated based on the result of the secondary sampling, and the new crack credibility score is compared with the crack detection threshold. If the new crack credibility score is lower than the crack detection threshold, it is determined to be a non-crack area.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention effectively solves the problem in the prior art that it is difficult to distinguish between hidden cracks and lattice stress-induced pseudo-defects by integrating multi-angle optical imaging and polarized infrared transmission analysis. By adopting a multi-angle lighting system, optical imaging at different incident angles enhances the visibility of different types of cracks, while infrared transmission imaging further provides internal stress information of silicon wafers, thereby improving the accuracy of hidden crack detection. The present invention extracts suspected crack areas on the surface of silicon wafers through normalization processing, edge enhancement filtering, and adaptive threshold segmentation, and calculates the grayscale gradient change rate at multiple lighting angles, calculates the degree of stress anomaly in combination with infrared transmission data, generates a crack credibility score through a weighted scoring method, and performs secondary sampling optimization judgment based on a dynamic lighting adjustment mechanism.

[0014] 2. The present invention has higher detection robustness and reliability. Multi-angle illumination avoids false detection or missed detection due to the limitations of single-angle imaging. Infrared transmission analysis makes up for the problem of internal defects that cannot be identified by traditional visual detection. The combined calculation of the grayscale gradient change rate and the degree of stress anomaly makes crack identification more accurate. In addition, the present invention adopts intelligent dynamic illumination adjustment to automatically optimize the detection angle when the crack credibility score is close to the threshold, thereby improving the accuracy of defect identification and reducing the false rejection rate and missed detection rate, thereby improving the quality control level of solar silicon wafers and the long-term reliability of photovoltaic modules, which is of great significance to the efficient production and cost control of the photovoltaic industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] For examples, see Figure 1 As shown, the solar silicon wafer visual inspection method described in this embodiment includes: A multi-angle lighting device is used to illuminate the solar silicon wafer to generate multiple optical images at different incident angles, obtain the original detection images of the silicon wafer at multiple lighting angles, and simultaneously collect infrared transmission images; The collected multi-angle images are normalized, the image features are enhanced through edge enhancement filtering, and the image is binarized using an adaptive threshold method to extract the suspected crack area on the silicon wafer surface; The grayscale gradient change rate of the suspected crack area is calculated under multiple illumination angles, and the stress anomaly degree of the suspected crack area is calculated through infrared transmission data based on the stress distribution inside the silicon wafer; Combined with the calculation results of the grayscale gradient change rate and the degree of stress anomaly, a weighted scoring method is used to generate a crack credibility score, which is compared with the crack detection threshold. If the crack credibility score is higher than the crack detection threshold, it is judged as a hidden crack defect, and the defect location and characteristics are recorded; if the crack credibility score is lower than the crack detection threshold, the illumination angle is adjusted for secondary sampling to further confirm the defect type.

[0019] For the detection of hidden cracks and defects in solar silicon wafers, a combination of multi-angle lighting + infrared transmission imaging is used to improve the accuracy and robustness of detection. Specifically, the multi-angle lighting system includes the following components: Multiple adjustable angle LED light source arrays: configured above the silicon wafer to illuminate the silicon wafer surface at different angles; Programmable light control unit: dynamically adjusts the brightness and incident angle of the light source according to the reflective characteristics of the silicon wafer surface to optimize the defect imaging effect; Principle of multi-angle optical imaging: Low-angle incident light (10°~30°): enhances the visibility of fine surface scratches and shallow cracks; medium-angle incident light (30°~60°): provides uniform illumination, suitable for overall defect detection; high-angle incident light (60°~90°): enhances the optical contrast of deep cracks and hidden crack areas. The silicon wafer surface images under different lighting angles are collected by a high-resolution industrial camera and stored as multi-channel data. The specific process is as follows: Synchronously control the light source and camera to collect original images in sequence at each lighting angle; Automatic image calibration ensures spatial alignment of multi-angle images to avoid detection deviations caused by equipment errors; Preliminary defect area extraction uses edge enhancement processing and adaptive contrast adjustment to improve the visibility of defect features. Synchronous acquisition of infrared transmission images: Infrared transmission testing is an important innovation of this method, which can be used to detect subsurface cracks or abnormal stress areas. Short-wave infrared (SWIR, 900-1700nm) or medium-wave infrared (MWIR, 3-5μm) light sources are used to transmit silicon wafers to obtain internal defect information; The transmission image is recorded by an infrared camera and fused with the visible light image for analysis; Infrared image preprocessing: denoising, contrast enhancement, edge detection to extract stress concentration areas and hidden crack information. Fusion of optical images and infrared images for joint analysis improves detection accuracy; use of infrared transmission data to correct misjudgment of optical images and reduce interference caused by surface texture or contamination; use of threshold comparison + stress analysis method to ensure accurate distinction between hidden cracks and false defects.

[0020] This embodiment uses normalization, edge enhancement filtering, and adaptive threshold binarization to extract suspected crack areas on the surface of the silicon wafer from the collected multi-angle optical images and infrared transmission images, specifically: Eliminate uneven image contrast caused by lighting angle, brightness changes or device noise, and ensure image data consistency at different angles.

[0021] Calculate the average grayscale value of all collected images and normalize each image I(x,y) so that its grayscale value range is between [0,1]: ;in, and are the minimum and maximum grayscale values ​​of the original image, respectively. Edge enhancement filtering: highlight the edge features of the crack area and improve the accuracy of crack detection.

[0022] High-pass filtering to enhance crack edges: Laplacian filtering is used to calculate the second-order derivative and enhance the crack edge. This operation can strengthen the crack edge while reducing surface noise interference. Adaptive threshold binarization processing: Extract suspected crack areas on the surface of the silicon wafer and convert the image into a black and white binary image for subsequent analysis.

[0023] Local Adaptive Thresholding: Due to the uneven illumination on the surface of the silicon wafer, a local window adaptive method is used to calculate the binarization threshold: ;in, is the average gray value in the local window, σ(x,y) is the local standard deviation, and k is an adjustment parameter that controls the threshold sensitivity (usually 0.5~1.0). If Ienhance(x,y)>T(x,y), it is set to white (crack area); otherwise it is set to black (background). Final output result: Generate a binary crack image, in which the crack area is white and the background is black; extract the connected crack area, calculate the characteristic parameters such as crack length and width, and provide a basis for subsequent analysis; combine with infrared transmission images to further eliminate false defects and ensure the accuracy of detection.

[0024] The grayscale gradient change rate of the suspected crack area is calculated at multiple lighting angles. The grayscale gradient change rate is obtained as follows: The input original image is usually an RGB color image, which needs to be converted into a grayscale image I(x,y) first. The calculation expression is: I(x,y)=0.299R(x,y)+0.587G(x,y)+0.114B(x,y); where R, G, and B are the red, green, and blue channel values ​​of the image pixels, respectively.

[0025] The Sobel operator is used to calculate the grayscale gradient of the image in the horizontal (X direction) and vertical (Y direction), using the following two 3×3 convolution kernels: Horizontal gradient: ; Vertical gradient: ; Perform convolution calculation on the image and calculate and : ; ; In the formula, Represents the convolution operation; is the gradient in the X direction; is the gradient in the Y direction, combined with and , calculate the gradient magnitude of each pixel: ; Where: G(x,y) represents the total gradient size of the pixel point, which can be used to measure the grayscale change rate of the crack area. Under multiple illumination angles, the gradient change rate of the crack area is estimated by the gradient change of adjacent pixels: ;in: Represents the grayscale gradient change rate; calculates the difference in gradient values ​​of adjacent pixels to measure the degree of gradient mutation in the crack area.

[0026] According to the stress distribution inside the silicon wafer, the degree of stress anomaly in the suspected crack area is calculated through infrared transmission data, including: Polarized infrared imaging is used to detect internal stress in silicon wafers. Due to the photoelastic effect, silicon wafers will produce phase delay under the action of internal stress, affecting the transmission characteristics of polarized light.

[0027] Setting an infrared light source and a polarizing plate, and adjusting the polarization direction to enhance the contrast of the stress area; Collect polarized infrared transmission images IIR(x, y), where IIR(x, y) represents the intensity of polarized infrared transmission light at the image coordinate (x, y), which is affected by the internal stress distribution of the silicon wafer. To reduce the device response error, the image is normalized to obtain a normalized image. , which is defined as: ;in, and are the minimum and maximum grayscale values ​​of the original image, respectively. Due to the birefringence effect of polarized light caused by internal stress in the silicon wafer, the phase delay at each pixel is calculated: ; Where: δ(x,y) is the phase delay, d(x,y) is the local thickness of the silicon wafer; Δn(x,y) is the refractive index change, which is proportional to the stress; λ is the wavelength of infrared light (usually in the range of 900-1700nm). The stress σ(x,y) and the phase delay δ(x,y) satisfy: ;in, is the photoelastic coefficient of the silicon wafer.

[0028] The polarized infrared image is subjected to Fourier transform, which is used to extract the frequency characteristics of stress distribution and identify the stress concentration area F(u,v). The low-frequency component represents uniform stress distribution, and the high-frequency component represents the stress mutation area. Extract high-frequency stress anomaly areas through bandpass filtering: ; where H(u,v) is a bandpass filter: ; , is the low frequency and high frequency cutoff value. Perform inverse Fourier transform to restore the stress abnormal area , calculate the standard deviation of stress anomaly distribution : ;in: is the number of pixels in the suspected crack area; is the average stress value in the region.

[0029] Calculate the rate of change of stress : ;like If the stress is higher than the set threshold, it means that the stress in the area has changed suddenly and there may be cracks. Calculate the stress abnormality value , the expression is: ;in: is the weighting coefficient, which usually satisfies ;like If it is higher than the set threshold, the area is a stress abnormality area.

[0030] Combining the calculation results of gray gradient change rate and stress anomaly degree, a weighted scoring method is used to generate crack credibility score, including: The grayscale gradient change rate and stress anomaly value are normalized to be between [0, 1], and the crack credibility score is calculated based on the normalized grayscale gradient change rate and stress anomaly value.

[0031] For example, the present invention may use the following formula to calculate the crack credibility score, and the calculation expression is: ; In the formula, Score the crack credibility, represents the gray gradient change rate, is the stress anomaly value, is the weight coefficient of gray gradient change rate and stress anomaly value (which can be optimized based on experimental experience or machine learning), and Both are greater than 0.

[0032] The obtained crack credibility score is compared with the crack detection threshold. If the crack credibility score is higher than the crack detection threshold, it is judged as a hidden crack defect, and the defect location and characteristics are recorded; if the crack credibility score is lower than the crack detection threshold, the illumination angle is adjusted for secondary sampling to further confirm the defect type.

[0033] Adjust the illumination angle for secondary sampling to further confirm the defect type, including: To enhance the visibility of cracks or defective areas, a dynamic lighting angle adjustment method is used: ;in: is the adjusted lighting angle, is the original sampling illumination angle; Adjust the step size for the angle. If the crack credibility score is low, increase the incident angle to improve the shadow contrast of the crack area; if stress anomalies are detected but no obvious cracks are present, reduce the illumination angle to reduce surface reflection interference. At the new light angle Next, recalculate the grayscale gradient change rate: ;in: is the normalized gray gradient change rate at the new angle; , is the grayscale gradient in the X and Y directions calculated by the Sobel operator (under the new illumination angle). If the area increases, it means that the area may be a crack; If it decreases, it may be noise or surface texture changes. After adjusting the illumination angle, polarized infrared transmission detection is used to calculate the degree of stress anomaly at the new angle: ;in: is the normalized stress anomaly degree under the new illumination angle, is the stress anomaly degree calculated at the new angle; is still a global normalization parameter. If it increases significantly, it may be a hidden crack; There is no obvious change, which may be normal stress distribution. According to the results of the secondary sampling, the crack credibility score is recalculated and compared with the crack detection threshold. If the new crack credibility score is lower than the crack detection threshold, it is judged as a non-crack area, which may be a stress pseudo defect or a normal texture.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0036] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects associated with each other are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the context. A person of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this article can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0037] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A solar silicon wafer visual inspection method, characterized in that: include: A multi-angle lighting device is used to illuminate the solar silicon wafer to generate multiple optical images at different incident angles, obtain the original detection images of the silicon wafer at multiple lighting angles, and simultaneously collect infrared transmission images; The collected multi-angle images are normalized, the image features are enhanced through edge enhancement filtering, and the image is binarized using an adaptive threshold method to extract the suspected crack area on the silicon wafer surface; The grayscale gradient change rate of the suspected crack area is calculated under multiple illumination angles, and the stress anomaly degree of the suspected crack area is calculated through infrared transmission data based on the stress distribution inside the silicon wafer. The weighted scoring method is used to generate a crack credibility score based on the calculation results of the grayscale gradient change rate and the stress anomaly degree, and the score is compared with the crack detection threshold. If the crack credibility score is higher than the crack detection threshold, it is determined to be a hidden crack defect, and the defect location and characteristics are recorded. If the crack credibility score is lower than the crack detection threshold, the illumination angle is adjusted for secondary sampling to further confirm the defect type.

2. The solar silicon wafer visual inspection method according to claim 1, characterized in that: The multi-angle lighting device comprises a plurality of adjustable angle LED light source arrays, the incident angle range of the LED light source array is , where: low angle incident light For enhanced visibility of fine surface scratches and shallow cracks; medium angle incident light Used to provide uniform illumination, suitable for overall defect detection; high angle incident light Used to enhance the optical contrast of deep cracks and hidden crack areas.

3. The solar silicon wafer visual inspection method according to claim 2, characterized in that: The grayscale gradient change rate of the suspected crack area is calculated at multiple lighting angles. The grayscale gradient change rate is obtained as follows: The original input image is an RGB color image, which is converted to a grayscale image I(x,y). The Sobel operator is used to calculate the horizontal and vertical grayscale gradients of the image using two 3×3 convolution kernels: Horizontal gradient: ; Vertical gradient: ; Perform convolution calculation on the image and calculate and : ; ; In the formula, Represents the convolution operation; is the gradient in the X direction; is the gradient in the Y direction, combined with and , calculate the gradient magnitude of each pixel: ; Where: G(x,y) represents the total gradient size of the pixel. Under multiple lighting angles, the gradient change rate of the crack area is estimated by the gradient change of adjacent pixels: ;in: Indicates the grayscale gradient change rate.

4. The solar silicon wafer visual inspection method according to claim 3, characterized in that: According to the stress distribution inside the silicon wafer, the degree of stress anomaly in the suspected crack area is calculated through infrared transmission data, which specifically includes: collecting polarized infrared transmission images IIR(x, y), where IIR(x, y) represents the intensity of polarized infrared transmission light at the image coordinates (x, y), normalizing the image, and obtaining a normalized image , which is defined as: ;in, and are the minimum and maximum grayscale values ​​of the original image respectively; Due to the birefringence effect of polarized light caused by internal stress in the silicon wafer, the phase delay at each pixel is calculated: ; Where: δ(x,y) is the phase delay, d(x,y) is the local thickness of the silicon wafer; Δn(x,y) is the refractive index change, which is proportional to the stress; λ is the wavelength of infrared light, and the stress σ(x,y) and the phase delay δ(x,y) satisfy: ;in, is the photoelastic coefficient of the silicon wafer; Perform Fourier transform on the polarized infrared image to identify the stress concentration area F(u,v). The low-frequency component represents the uniform stress distribution, and the high-frequency component represents the stress mutation area. Extract high-frequency stress anomaly areas through bandpass filtering: ; where H(u,v) is a bandpass filter: ; , is the low-frequency and high-frequency cutoff value; perform inverse Fourier transform to restore the stress abnormal area , calculate the standard deviation of stress anomaly distribution : ;in: is the number of pixels in the suspected crack area; is the average stress value of the region; Calculate the rate of change of stress : ; Calculate the stress anomaly value , the expression is: ;in: is the weighting coefficient, satisfying .

5. The solar silicon wafer visual inspection method according to claim 4, characterized in that: According to the real-time factor, combined with the calculation results of the gray gradient change rate and the stress anomaly degree, a weighted scoring method is used to generate the crack credibility score, which includes: The grayscale gradient change rate and stress anomaly value are normalized to be between [0, 1], and the crack credibility score is calculated based on the normalized grayscale gradient change rate and stress anomaly value.

6. The solar silicon wafer visual inspection method according to claim 5, characterized in that: Adjust the illumination angle for secondary sampling to further confirm the defect type, including: To enhance the visibility of cracks or defective areas, a dynamic lighting angle adjustment method is used: ;in: is the adjusted lighting angle, is the original sampling illumination angle; Adjust the step size for the angle; At the new light angle Next, recalculate the grayscale gradient change rate: ;in: is the normalized grayscale gradient change rate at the new angle; , It is the grayscale gradient in X and Y directions calculated by Sobel operator.

7. The solar silicon wafer visual inspection method according to claim 6, characterized in that: After adjusting the illumination angle, polarized infrared transmission detection is used to calculate the degree of stress anomaly at the new angle: ;in: is the normalized stress anomaly degree under the new illumination angle, is the stress anomaly degree calculated at the new angle; It is still a global normalization parameter.

8. The solar silicon wafer visual inspection method according to claim 7, characterized in that: According to the result of secondary sampling, the crack credibility score is recalculated, and the new crack credibility score is compared with the crack detection threshold. If the new crack credibility score is lower than the crack detection threshold, it is determined to be a non-crack area.

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