CN115290663B - Mini LED wafer appearance defect detection method based on optical detection - Google Patents
Mini LED wafer appearance defect detection method based on optical detection Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G06T7/0004—Industrial image inspection
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30148—Semiconductor; IC; Wafer
Abstract
The invention relates to the technical field of defect detection, in particular to a Mini LED wafer appearance defect detection method based on optical detection, which is used for testing the existence of flaws, defects or stains, wherein the method divides a wafer into a crystal grain region and an amorphous grain region based on the rectangular characteristics of crystal grains, detects the defect regions in the crystal grain region and the amorphous grain region based on texture information and gray level distribution, optimizes the threshold value of Hough line detection according to the number of the detected lines in the defect regions so as to detect effective lines, reduces redundant line information, and then confirms the defect type of each defect region according to the position of the effective lines and the position of the circle center of the wafer, so that the automatic detection ensures the detection accuracy of the defect type on the premise of rapidness and simplicity.
Description
Technical Field
The invention relates to the technical field of defect detection, in particular to a Mini LED wafer appearance defect detection method based on optical detection.
Background
With the development of display technology of intelligent equipment, the resolution of a screen is higher and higher, and a Mini LED array device comes along. The wafer is the core part of the LED, the quality of the wafer seriously affects various photoelectric parameters of the LED, and meanwhile, the Mini LED is reduced in size and more complex in process, so that the requirement for appearance quality detection of the Mini LED is further improved.
At present, in order to reduce the influence of the appearance defects of the wafer on the subsequent processing, the defects are usually detected in a manual visual inspection or sampling inspection mode, the detection efficiency is low, the cost is high, and the defects such as fine scratches on the wafer are easily missed to be detected, so that the defect result is inaccurate.
Disclosure of Invention
In order to solve the above technical problems, the present invention aims to provide a method for detecting a Mini LED wafer appearance defect based on optical detection, which adopts the following technical scheme:
acquiring RGB images of the surface of a wafer to obtain corresponding gray level images, carrying out corner point detection on the gray level images to obtain corner points of crystal grains in the wafer, calculating the distance between adjacent corner points, and dividing the gray level images into crystal grain areas and non-crystal grain areas according to the difference between the distance and a standard distance required by production;
dividing the grain area into a normal grain area and a defective grain area according to the difference of texture information between each grain area and the template grain area; taking four corner points and a central point in the defect grain region as characteristic points, and comparing the gray value difference between each pixel point and the characteristic points in the defect grain region to obtain a sub-defect region in each defect grain region; integrating the adjacent and continuous sub-defect regions into an integrated sub-region according to the gray distribution of the sub-defect regions to obtain a plurality of first sub-regions, wherein the first sub-regions comprise the sub-defect regions and the integrated sub-regions; detecting a second sub-region in each non-grain region by using a region growing algorithm;
carrying out Hough line detection on the gray level image to obtain the total number of straight lines in the wafer, counting the straight lines in each first sub-area and each second sub-area to obtain the total number of the straight lines, and adjusting a threshold value in the Hough line detection according to the difference value of the number of the straight lines between the total number of the straight lines and the total number of the straight lines to obtain a new threshold value; and performing line detection by using the new threshold to obtain a plurality of target lines, and judging the defect types of the first sub-area and the second sub-area based on the positions of the target lines.
Further, the method for dividing the normal grain region and the defective grain region includes:
respectively acquiring the gray level co-occurrence matrix of each crystal grain region and the template crystal grain region, and calculating the entropy value of each gray level co-occurrence matrix; calculating the similarity between the corresponding grain region and the template grain region according to the entropy difference between the grain region and the template grain region;
setting a similarity threshold, confirming that the corresponding crystal grain region is a normal crystal grain region when the similarity degree is greater than or equal to the similarity threshold, and confirming that the corresponding crystal grain region is a defect crystal grain region when the similarity degree is less than the similarity threshold;
wherein, the calculation formula of the similarity degree is as follows:
wherein, the first and the second end of the pipe are connected with each other,
in order to be of a similar degree,
is the entropy value of the region of the grain,
entropy values of the template grain regions.
Further, the method for obtaining the sub-defect region in each defect grain region by comparing the gray value difference between each pixel point and the feature point in the defect grain region includes:
calculating gray value difference values between the current pixel point and each feature point respectively, and adding the gray value difference values to obtain the gray value difference sum between the current pixel point and the feature points;
calculating the gray value difference sum of each pixel point in the current defect grain region, setting a gray value difference threshold, and when the gray value difference sum is greater than or equal to the gray value difference threshold, considering the corresponding pixel point as a defect pixel point; and the area formed by all the defective pixel points in the current defective grain area is the sub-defective area.
Further, the method for integrating the adjacent and continuous sub-defect regions into an integrated sub-region according to the gray distribution of the sub-defect regions comprises:
and respectively calculating the average gray value of each sub-defect area in two adjacent sub-defect areas, and integrating the two adjacent sub-defect areas into an integrated sub-area when the difference value between the average gray values of the two sub-defect areas is smaller than a difference threshold value.
Further, the method for adjusting the threshold in the hough line detection according to the difference between the total number of the straight lines and the total number of the straight lines to obtain a new threshold includes:
setting a quantity difference threshold, and when the quantity difference of the straight lines is greater than the quantity difference threshold, obtaining a new threshold by using a calculation formula for increasing the threshold, wherein the calculation formula for increasing the threshold is as follows:
wherein, in the step (A),
in order to be the new threshold value, the threshold value is set,
is the threshold value before adjustment.
Further, the method for judging the defect type of the first sub-area and the second sub-area based on the position of the target straight line includes:
excluding target straight lines corresponding to edge straight lines in the crystal grain region where the first sub-region is located and the non-crystal grain region where the second sub-region is located, and when no residual target straight line exists in the first sub-region or the second sub-region, determining that the defect type corresponding to the first sub-region or the second sub-region is area defects such as particle dust, pollutants and the like; when the first sub-area or the second sub-area has the residual target straight line, determining that the defect type corresponding to the first sub-area or the second sub-area is a scratch defect or a slip line defect.
Further, the method for judging the scratch defect or the slip line defect includes:
obtaining the center of a wafer circle through Hough circle detection, and respectively calculating the Euclidean distance between a target straight line in the first sub-area or the second sub-area and the center of the wafer circle by using the distance from a point to a straight line; and setting a distance threshold, and when the Euclidean distance is greater than or equal to the distance threshold, determining that the target straight line in the first sub-area or the second sub-area is a slip line defect, otherwise, determining that the target straight line is a scratch defect.
The invention has the following beneficial effects: the wafer is divided into a crystal grain area and an amorphous grain area based on the rectangular characteristics of the crystal grains, the defect areas in the crystal grain area and the amorphous grain area are detected based on texture information and gray distribution, the threshold value of Hough line detection is optimized according to the number of lines detected in the defect areas to detect effective lines and reduce redundant line information, and then the defect type of each defect area is confirmed according to the position of the effective lines and the circle center position of the wafer, so that the detection accuracy of the defect type is ensured on the premise of rapidness and simplicity in automatic detection, and the Mini LED wafer appearance defect detection method based on optical detection can also test the existence of flaws or dirt.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic view of a wafer with particle defects (a), a schematic view of a wafer with contaminants (b), and a schematic view of a wafer with slip line defects (C), according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a method for detecting appearance defects of a Mini LED wafer based on optical inspection according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the structure, the features and the functions of a method for detecting the appearance defects of a Mini LED wafer based on optical inspection according to the present invention will be provided with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention aims at the following specific scenes: and detecting the defect types of the Mini LED wafer, wherein the defect types comprise area defects such as particles and pollutants and linear defects such as scratches and slip lines, and the wafer schematic diagram (a) with particle defects, the wafer schematic diagram (b) with pollutants and the wafer schematic diagram (C) with slip line defects are shown in FIG. 1.
The following specifically describes a specific scheme of the method for detecting the appearance defects of the Mini LED wafer based on optical detection, with reference to the accompanying drawings.
Referring to fig. 2, a flowchart illustrating steps of a method for detecting the appearance defects of a Mini LED wafer based on optical inspection according to an embodiment of the present invention is shown, wherein the method includes the following steps:
step S001, collecting RGB images of the surface of the wafer to obtain corresponding gray level images, carrying out corner detection on the gray level images to obtain corners of crystal grains in the wafer, calculating the distance between adjacent corners, and dividing the gray level images into a crystal grain area and an amorphous grain area according to the difference between the distance and a standard distance required by production.
Specifically, a high-resolution camera is used for collecting the surface RGB image of the wafer in a fixed light source mode, and the surface RGB image is subjected to graying processing by a weighted graying method to obtain a corresponding grayscale image.
Because the wafer is composed of a plurality of crystal grains, and the shape of each crystal grain is rectangular, the crystal grains in the wafer are detected based on the rectangular outline characteristics of the crystal grains, and the method comprises the following steps: firstly, edge detection is carried out on a gray level image to obtain an edge contour of a wafer and an edge line of each grain, then corner detection is carried out on the edge line to obtain a plurality of corners in the gray level image, the distance between every two adjacent corners is respectively calculated, the standard distance between any two adjacent corners in 4 corners of the grain is obtained based on the grain size required by production, when the distance is equal to the standard distance, the corresponding two corners belong to the corners on the same grain, and similarly, the 4 corners on each grain are detected, and the region enclosed by the 4 corners is a grain region, otherwise, the region is an amorphous grain region.
It should be noted that, when there are no 4 corner points in a certain area, it indicates that there is a missing corner in the die corresponding to the area, otherwise, it indicates that the area is a blank area at the edge of the wafer, and therefore the non-die area includes a defective area corresponding to the missing corner in the die and a blank area on the wafer except for the die area.
Step S002, dividing the grain region into a normal grain region and a defect grain region according to the texture information difference between each grain region and the template grain region; taking four corner points and a central point in the defect grain area as characteristic points, and comparing gray value difference between each pixel point and the characteristic points in the defect grain area to obtain a sub-defect area in each defect grain area; integrating adjacent and continuous sub-defect regions into an integrated sub-region according to the gray distribution of the sub-defect regions to obtain a plurality of first sub-regions, wherein the first sub-regions comprise the sub-defect regions and the integrated sub-regions; a second sub-region in each non-grain region is detected using a region growing algorithm.
Specifically, each crystal grain region is respectively matched with a template crystal grain region to detect whether the crystal grain region has defects, and the specific method comprises the following steps: respectively obtaining gray level co-occurrence matrixes of each crystal grain region and each template crystal grain region, calculating the entropy value of each gray level co-occurrence matrix, wherein the entropy value represents texture information of each region, and further obtaining the entropy value corresponding to each crystal grain region and the entropy value of each template crystal grain region; calculating the similarity between the corresponding grain region and the template grain region according to the entropy difference between the grain region and the template grain region, wherein the calculation formula of the similarity is as follows:
wherein the content of the first and second substances,
in order to be of a similar degree,
is the entropy value of the region of the grain,
entropy values of the template grain regions.
The similarity degree between each crystal grain region and the template crystal grain region can be obtained by utilizing the calculation formula of the similarity degree, then a similarity degree threshold value is set, when the similarity degree is greater than or equal to the similarity degree threshold value, the crystal grain region is considered to have no defect and is a normal crystal grain region, otherwise, when the similarity degree is less than the similarity degree threshold value, the crystal grain region is considered to have the defect and is a defect crystal grain region.
Further, the defective crystal grain regions are further analyzed, and the sub-defect region in each defective crystal grain region is detected according to the gray distribution in the defective crystal grain regions, specifically: taking 4 angular points of the current defect grain region and the central point of the region as characteristic points, calculating gray value difference values between the current pixel point and each characteristic point respectively, and adding the gray value difference values to obtain the gray value difference sum between the current pixel point and the characteristic points; calculating the gray value difference sum of each pixel point in the current defect grain region, setting a gray value difference threshold, when the gray value difference sum is greater than or equal to the gray value difference threshold, considering the corresponding pixel point as a defect pixel point, otherwise, when the gray value difference sum is less than the gray value difference threshold, considering the corresponding pixel point as a normal pixel point, detecting all defect pixel points in the current defect grain region based on the judgment method, and determining the region formed by all the defect pixel points as a sub-defect region of the current defect grain region.
The method includes the steps that the same defect possibly appears in adjacent crystal grain regions, namely one defect covers two crystal grains, adjacent and continuous sub-defect regions are combined based on gray value difference in the sub-defect regions, specifically, average gray values of each sub-defect region are respectively calculated for the two adjacent sub-defect regions, when the difference value between the average gray values of the two sub-defect regions is smaller than a difference threshold value, the two sub-defect regions belong to the same defect, the two sub-defect regions are integrated to obtain an integrated sub-region, and the non-adjacent sub-defect regions, the two adjacent sub-defect regions with the difference value not meeting the difference threshold value and the integrated sub-region are collectively called as a first sub-region to obtain a plurality of first sub-regions.
Further, detecting a defect area in each amorphous grain area based on the gray value of the pixel point specifically comprises: and (3) using a region growing algorithm, randomly selecting one pixel point in the current amorphous grain region as an initial seed point, growing based on the gray value of the initial seed point, wherein the growing rule is that when the gray value difference is smaller than a difference threshold value and the growing direction is in an eight-neighborhood direction, after the growth is finished, the region with large area is used as a normal region, and the region with small area is used as a defect region in the current amorphous grain region, namely a second sub-region.
S003, carrying out Hough line detection on the gray level image to obtain the total number of straight lines in the wafer, counting the straight lines in each first subregion and each second subregion to obtain the total number of the straight lines, and adjusting a threshold value in the Hough line detection according to a straight line number difference value between the total number of the straight lines and the total number of the straight lines to obtain a new threshold value; and performing line detection by using the new threshold to obtain a plurality of target lines, and judging the defect types of the first sub-area and the second sub-area based on the positions of the target lines.
Specifically, all straight lines in the gray level image are obtained by utilizing Hough straight line detection with an initially set threshold value, and the total number of the straight lines in the wafer is obtained; based on step S002, a plurality of defect regions in the wafer, that is, a plurality of first sub-regions and a plurality of second sub-regions, are obtained, and the number of straight lines in the defect regions is counted to obtain a total number of straight lines in the defect regions.
In the traditional Hough line detection, detection is performed based on a voting value, a line can be detected only when the voting value is larger than a threshold, an overlarge threshold can result in that the line cannot be detected, and an undersize threshold can detect a plurality of interference lines, so that selection of a proper threshold is particularly critical. Calculating the difference value of the number of straight lines between the total number of the straight lines and the total number of the straight lines, setting a number difference threshold value, and when the difference value of the number of the straight lines is larger than the number differenceWhen the threshold value is set, it means that the initially set threshold value is smaller, and the initially set threshold value needs to be increased to obtain a new threshold value, and the increasing calculation formula is as follows:
wherein, in the step (A),
in order to be the new threshold value, the threshold value is set,
is a threshold value before adjustment; when the difference in the number of straight lines is less than or equal to the number difference threshold, the initially set threshold is considered to be unadjusted.
Performing Hough line detection on the gray level image again based on the new threshold to obtain a plurality of target lines, and judging the defect types of the first sub-area and the second sub-area based on the positions of the target lines, wherein the method specifically comprises the following steps: known from prior knowledge, small defects such as particle dust are small in shape and cannot detect straight lines, and slip line defects and scratch defects are long in shape and can easily detect straight lines, so that target straight lines corresponding to straight lines at the edges of regions in a crystal grain region where a first sub-region is located and an amorphous grain region where a second sub-region is located are excluded, whether the positions of the remaining target straight lines are in the first sub-region or the second sub-region is compared, if no target straight line exists in the first sub-region or the second sub-region, it is indicated that the defects corresponding to the first sub-region or the second sub-region are area defects such as particle dust and pollutants, otherwise, if the first sub-region or the second sub-region contains the target straight lines, it is determined whether the defects corresponding to the first sub-region or the second sub-region are the scratch defects or the slip line defects according to the distance between the target straight lines and the circle center of the wafer: the slip lines are often located in blank areas at the edge of the wafer, and the slip lines are closer to the edge profile of the wafer, so that the center of the circle of the wafer is detected by a Hough circle detection method to obtain the position of the center of the circle of the wafer, euclidean distances between the center of the circle of the wafer and target straight lines are calculated by using a point-to-straight line distance formula for the target straight lines in the first sub-area or the second sub-area respectively, a distance threshold is set, and when the Euclidean distances are greater than or equal to the distance threshold, the target straight lines in the first sub-area or the second sub-area are considered as slip line defects, otherwise, the target straight lines are considered as scratch defects.
In summary, the embodiment of the present invention provides a method for detecting an appearance defect of a Mini LED wafer based on optical detection, the method divides a wafer into a grain region and an amorphous grain region based on rectangular features of grains, detects defect regions in the grain region and the amorphous grain region based on texture information and gray distribution, optimizes a threshold of hough line detection according to the number of lines detected in the defect regions to detect effective lines, reduces redundant line information, and then determines a defect type of each defect region according to a position of the effective line and a position of a center of a circle of the wafer, so that automatic detection ensures accuracy of the defect type on the premise of rapidness and simplicity.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit of the present invention are intended to be included therein.
Claims (5)
1. A Mini LED wafer appearance defect detection method based on optical detection is characterized by comprising the following steps:
acquiring RGB images of the surface of a wafer to obtain corresponding gray level images, carrying out corner point detection on the gray level images to obtain corner points of crystal grains in the wafer, calculating the distance between adjacent corner points, and dividing the gray level images into crystal grain areas and non-crystal grain areas according to the difference between the distance and a standard distance required by production;
dividing the grain area into a normal grain area and a defect grain area according to the difference of texture information between each grain area and the template grain area; taking four corner points and a central point in the defect grain region as characteristic points, and comparing the gray value difference between each pixel point and the characteristic points in the defect grain region to obtain a sub-defect region in each defect grain region; integrating the adjacent and continuous sub-defect regions into an integrated sub-region according to the gray distribution of the sub-defect regions to obtain a plurality of first sub-regions, wherein the first sub-regions comprise the sub-defect regions and the integrated sub-regions; detecting a second sub-region in each non-grain region by using a region growing algorithm;
performing Hough line detection on the gray level image to obtain the total number of straight lines in the wafer, counting the straight lines in each first sub-area and each second sub-area to obtain the total number of the straight lines, and adjusting the threshold value in the Hough line detection according to the difference value between the total number of the straight lines and the total number of the straight lines to obtain a new threshold value; performing line detection by using the new threshold to obtain a plurality of target lines, and judging the defect types of the first sub-area and the second sub-area based on the positions of the target lines;
the method for obtaining the sub-defect region in each defect grain region by comparing the gray value difference between each pixel point and the characteristic point in each defect grain region comprises the following steps:
calculating gray value difference values between the current pixel point and each feature point respectively, and adding the gray value difference values to obtain the gray value difference sum between the current pixel point and the feature points;
calculating the gray value difference sum of each pixel point in the current defect grain region, setting a gray value difference threshold, and when the gray value difference sum is greater than or equal to the gray value difference threshold, considering the corresponding pixel point as a defect pixel point; the region formed by all the defective pixel points in the current defective grain region is the sub-defective region;
the method for judging the defect types of the first sub-area and the second sub-area based on the position of the target straight line comprises the following steps:
excluding target straight lines corresponding to straight lines at the edges of the crystal grain region where the first sub-region is located and the non-crystal grain region where the second sub-region is located, and when no residual target straight line exists in the first sub-region or the second sub-region, determining that the defect type corresponding to the first sub-region or the second sub-region is the area defect of particle dust and pollutants; when the first sub-area or the second sub-area has the residual target straight line, determining that the defect type corresponding to the first sub-area or the second sub-area is a scratch defect or a slip line defect.
2. The method for detecting the appearance defects of the Mini LED wafer based on the optical detection as claimed in claim 1, wherein the method for dividing the normal crystal grain region and the defect crystal grain region comprises:
respectively obtaining the gray level co-occurrence matrix of each crystal grain region and the template crystal grain region, and calculating the entropy value of each gray level co-occurrence matrix; calculating the similarity between the corresponding grain region and the template grain region according to the entropy difference between the grain region and the template grain region;
setting a similarity threshold, confirming that the corresponding crystal grain region is a normal crystal grain region when the similarity degree is greater than or equal to the similarity threshold, and confirming that the corresponding crystal grain region is a defect crystal grain region when the similarity degree is less than the similarity threshold;
wherein, the calculation formula of the similarity degree is as follows:
wherein the content of the first and second substances,
in order to be of a similar degree,
is the entropy value of the region of the grain,
entropy values of the template grain regions.
3. The method as claimed in claim 1, wherein the method for integrating adjacent and continuous sub-defect regions into an integrated sub-region according to the gray distribution of the sub-defect regions comprises:
and respectively calculating the average gray value of each sub-defect area in two adjacent sub-defect areas, and integrating the two adjacent sub-defect areas into an integrated sub-area when the difference value between the average gray values of the two sub-defect areas is smaller than a difference threshold value.
4. The method for detecting the appearance defects of the Mini LED wafer based on the optical detection as claimed in claim 1, wherein the method for adjusting the threshold in the Hough line detection according to the difference between the total number of the straight lines and the total number of the straight lines to obtain a new threshold comprises:
setting a quantity difference threshold, and obtaining a new threshold by using a calculation formula for increasing the threshold when the quantity difference of the straight lines is greater than the quantity difference threshold, wherein the calculation formula for increasing the threshold is as follows:
wherein, in the step (A),
in order to be the new threshold value, the threshold value is set,
is the threshold value before adjustment.
5. The method for detecting the appearance defects of the Mini LED wafer based on the optical detection as claimed in claim 1, wherein the method for determining the scratch defects or the slip line defects comprises:
obtaining the center of a wafer circle through Hough circle detection, and respectively calculating the Euclidean distance between a target straight line in the first sub-area or the second sub-area and the center of the wafer circle by using the distance from a point to a straight line; and setting a distance threshold, and when the Euclidean distance is greater than or equal to the distance threshold, determining that the target straight line in the first sub-area or the second sub-area is a slip line defect, otherwise, determining that the target straight line is a scratch defect.
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