CN113034474A - Test method for wafer map of OLED display - Google Patents
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Abstract
The invention discloses a testing method of an OLED display wafer map. In the invention, a black and white industrial gigabit network camera with the model number of Mantag-917B is adopted, and the resolution ratio of 900 ten thousand pixels is obtained. According to the field of view of a wafer imaging area, the width of the sensor device and the imaging requirements, the width of the sensor device is about 3mm, and the size of the sensor device is 12.8mm, so that a 4-time telecentric magnifying lens and an industrial telecentric lens with a C-shaped interface are selected for shooting; after the contour of the pattern of the internal wafer is subjected to affine transformation through various processing modes, different threshold segmentation and morphological processing are carried out on the cut region, and then defect detection and defect marking of point defects, linear defects, planar defects, large-area pollution defects and unfilled corner defects are realized.
Description
Technical Field
The invention belongs to the technical field of display detection, and particularly relates to a method for testing a wafer map of an OLED display.
Background
The wafer is easily affected by mechanical tools, manufacturing techniques, environmental factors, etc. during the manufacturing process, and the surface of the wafer is easily damaged after the wafer processing is completed. The defects greatly influence the physical and chemical properties of the wafer, and the traditional method for manually acquiring and analyzing the SEM image is low in efficiency and occupies a large amount of manual resources, the method is low in efficiency in increasing industrial production and consumes a large amount of manpower, and the method is gradually applied to surface defect detection along with the progress of computer vision and deep learning of various researches.
However, the common detection method does not carry out multi-pattern preprocessing on the shot image, so that the subsequent detection image is blurred, and the detection result has no accuracy.
Disclosure of Invention
The invention aims to: in order to solve the above-mentioned problems, a method for testing a wafer map of an OLED display is provided.
The technical scheme adopted by the invention is as follows: a testing method of a wafer map of an OLED display comprises the following steps:
s1: the method comprises the steps of firstly acquiring a wafer image, acquiring a high-quality wafer image with less noise interference through specific hardware equipment, and improving the defect extraction efficiency of a later-stage image processing algorithm; then, the industrial personal computer is controlled to collect, transmit and store the images of the wafer; the image acquisition, transmission and storage of the wafer are finished under the control of an industrial personal computer;
s2: performing median filtering image denoising processing on the preliminary image obtained in the step S1; moving a median filter to an overlapping range part which is overlapped with a detection image, firstly sorting pixel values in the range, generally selecting odd median filter templates according to the sorting from small to large, reserving the sorted middle number, and replacing the pixel value of a central point in the range by the middle number; if the template is an even template, calculating the average value of two numbers of the sorted middle pixels, then keeping the average value, and replacing the central pixel value in the range;
s3: performing enhancement processing on the image subjected to denoising in the step S2, wherein r represents the gray value of the image to be processed in an image histogram, and the value interval of r is set as [0, L-1] in L digital images with different gray levels]By h (r)k)＝nkRepresenting a discrete function in the sense of a pixel grey value r of the kth levelkThe total number of the pixel numbers of (2) is nk(ii) a Representing the total number of pixels of an image as NallNormalized gray level probability value is expressed as p (r)k) To represent the gray level r in an imagekThe probability of occurrence is expressed as P (r)k)＝nk/NallK is 0,1,2,3,.., L-1; then, after calculating the probability density conversion function based on the original image, the gray scale of the converted image is distributed uniformly in all gray scale levels, and the contrast of the original image can be improved
S4: carrying out binarization operation on the image, setting the pixel gray level in a certain threshold value range as a specific value, and setting the other part of the pixel gray level as another different value; the image after binarization is represented by T (x, y), the binarization threshold value is represented by T, and T is a certain gray value between [0, L-1 ]; the binarization processing algorithm is expressed as: t (x, y) ═ 0, f (x, y) < T; t (x, y) is 1, f (x, y) is more than or equal to T;
s5: using some characteristics with invariance of the graph for positioning, and extracting an interested region; common features include position, size, contour, other features of the target, etc., and by using these features, the moving state, position and direction of the target are not limited, and the target can be easily identified and automatically found, so that the region of interest can be extracted;
s6: performing feature extraction on the image processed in the step S5, at this time, performing feature extraction on the contour features, then calculating a direction vector of the contour template, then performing affine transformation on the contour template, calculating a direction vector of each point of the searched image, and finally obtaining a contour matching result after calculating the distance similarity;
s7: then, different opening and closing operations and filling operations are required to be carried out on the threshold segmentation parts with different characteristics inside and outside the dots of each characteristic; because the internal defect and the external defect of the dot are possibly overlapped or the boundaries are connected, the internal defect and the external defect need to be subjected to union operation; therefore, for the divided 5 regions, the defect regions are respectively extracted, and then the union is made to extract the total defect region;
s8: for the defect inside the circle, because the circle part followed by the affine is the outline, the inside of the outline needs to be filled into an area mode, and then the circle part is cut off from the preprocessed graph; the acquisition environment is not excessively interfered by the outside, so that the simplest gray threshold segmentation is adopted on the premise of ensuring that the region of interest can be segmented, the speed is high, and the time is saved; then, the divided whole area is changed into a single independent area, and according to the actual defect identification standard, an area with the area within a certain range is selected, and the area of the selected area is the defect area;
s9: for the circle external defect, firstly, the external area needs to be cut off from the preprocessed graph; because 2 pixels are enlarged when the circle template is established, the circle is firstly reduced by 2 pixels; then, calculating the set theoretical difference of the two regions by using difference, namely subtracting the region in the sub region from the region in the main region, defining the result region as the main region, and deleting all points in the sub region; deleting the circle region from the outer contour region;
s10: when the detection in step S9 is completed, merging, filling the inside of the large area part of the overlapped defect area, and finally extracting the total defect area; and recording the coordinates of the defective wafer, marking the central position of the defective area with a plus sign, and finally obtaining a detection result.
In a preferred embodiment, in step S1, the specific hardware device includes a light source, a lens, and a camera; and selecting an imaging scheme after the equipment is selected, and obtaining a digital image after the wafer is imaged.
In a preferred embodiment, in step S3, the whole may be divided into a plurality of parts, or the parts may be superimposed into a whole; if the image presents dark tones, the low gray level of the histogram presents dense distribution; otherwise, if the image presents bright tone, the high gray level of the histogram presents dense distribution; if the image has a high contrast, the gray values of the image are equally distributed in all gray levels.
In a preferred embodiment, in step S9, regarding the point defect outside the circle, the threshold segmentation is performed by local mean and standard deviation analysis; depending on the brightness, input image pixels with high or low local standard deviation and local brightness or darkness can be selected; thus, regions may be segmented over a background that is not uniform, noisy, or has non-uniform illumination.
In a preferred embodiment, in step S9, regarding the block defects outside the circle, the threshold segmentation is performed by using a gray threshold segmentation method; and similarly, obtaining the divided regions, and screening according to the size characteristics of the block areas to obtain area regions in a certain range, namely defect regions.
In a preferred embodiment, in step S9, regarding the defect of the outer line of the circle, the threshold segmentation is performed by a gray threshold segmentation method; and similarly, obtaining the divided regions, and obtaining area regions within a certain range, namely defect regions, according to the actual linear defect identification standard through morphological operation.
In a preferred embodiment, in step S9, regarding the defect on the outer surface of the circle, the threshold segmentation is performed by a gray threshold segmentation method; similar to other defect extraction modes, the method selects an area region in a certain range according to the identification standard of the actual planar defect through morphological operation repairing and screening to extract the defect.
In a preferred embodiment, in the step S6, the contour may be calculated and set a threshold S by using top-level search in the pyramidminPotential match bitThe match score of a position must be greater than a threshold sminAnd is a local maximum; after the latent position is preliminarily determined, the next tracking is needed, a pyramid image of a lower layer is found, and the operation is repeated until a target object is found at the lowest layer of the pyramid image; then, fitting the similarity measurement in the neighborhood of the local maximum value into a polynomial so that the local part of the polynomial reaches the minimum value, and obtaining a relatively accurate pose; and finally, adjusting the pose parameters by a least square method to obtain a more accurate final pose.
In a preferred embodiment, in step S1, an area CCD camera is selected as the capturing camera, and a black and white industrial gigabit camera with a model number of MantaG-917B is finally adopted according to the theoretical pixel size and the actual imaging effect, and has a resolution of 900 ten thousand pixels.
In a preferred embodiment, in step S1, a 4-fold telecentric magnifying lens and an industrial telecentric lens with a C-type interface are selected according to the field of view size of the wafer imaging area being about 3mm wide, the size of the sensor device being 12.8mm wide, and the imaging requirements.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, affine transformation is carried out on the outline of the pattern of the internal wafer in multiple processing modes, different threshold segmentation and morphological processing are carried out on the cut region, and then defect detection and defect marking of point defects, linear defects, planar defects, large-area pollution defects and unfilled corner defects are realized.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
a testing method of a wafer map of an OLED display comprises the following steps:
s1: the method comprises the steps of firstly acquiring a wafer image, acquiring a high-quality wafer image with less noise interference through specific hardware equipment, and improving the defect extraction efficiency of a later-stage image processing algorithm; then, the industrial personal computer is controlled to collect, transmit and store the images of the wafer; the image acquisition, transmission and storage of the wafer are finished under the control of an industrial personal computer; in step S1, the specific hardware device includes a light source, a lens, and a camera; selecting imaging schemes after the equipment is selected, and obtaining digital images after wafer imaging; in step S1, selecting an area array CCD camera as a collection camera, and finally adopting a black and white industrial gigabit network camera with a model of MantaG-917B, having a resolution of 900 ten thousand pixels, according to the theoretical pixel size and the actual imaging effect; in step S1, according to the field width of the wafer imaging area of about 3mm, the size width of the sensor device of 12.8mm, and the imaging requirements, a 4-fold telecentric magnifying lens and an industrial telecentric lens with a C-type interface are selected;
s2: performing median filtering image denoising processing on the preliminary image obtained in the step S1; moving a median filter to an overlapping range part which is overlapped with a detection image, firstly sorting pixel values in the range, generally selecting odd median filter templates according to the sorting from small to large, reserving the sorted middle number, and replacing the pixel value of a central point in the range by the middle number; if the template is an even template, calculating the average value of two numbers of the sorted middle pixels, then keeping the average value, and replacing the central pixel value in the range;
s3: performing enhancement processing on the image subjected to denoising in the step S2, wherein r represents the gray value of the image to be processed in an image histogram, and the value interval of r is set as[0,L-1]By h (r)k)＝nkRepresenting a discrete function in the sense of a pixel grey value r of the kth levelkThe total number of the pixel numbers of (2) is nk(ii) a Representing the total number of pixels of an image as NallNormalized gray level probability value is expressed as p (r)k) To represent the gray level r in an imagekThe probability of occurrence is expressed as P (r)k)＝nk/NallK is 0,1,2,3,.., L-1; then, after a probability density conversion function is calculated based on the original image, the gray scale of the converted image is distributed in a balanced and equal manner at all gray scale levels, and the contrast of the original image can be improved; in step S3, the whole may be divided into a plurality of parts, or the parts may be superimposed into a whole; if the image presents dark tones, the low gray level of the histogram presents dense distribution; otherwise, if the image presents bright tone, the high gray level of the histogram presents dense distribution; if the image has high contrast, the gray value of the image is evenly distributed in all gray levels
S4: carrying out binarization operation on the image, setting the pixel gray level in a certain threshold value range as a specific value, and setting the other part of the pixel gray level as another different value; the image after binarization is represented by T (x, y), the binarization threshold value is represented by T, and T is a certain gray value between [0, L-1 ]; the binarization processing algorithm is expressed as: t (x, y) ═ 0, f (x, y) < T; t (x, y) is 1, f (x, y) is more than or equal to T;
s5: using some characteristics with invariance of the graph for positioning, and extracting an interested region; common features include position, size, contour, other features of the target, etc., and by using these features, the moving state, position and direction of the target are not limited, and the target can be easily identified and automatically found, so that the region of interest can be extracted;
s6: performing feature extraction on the image processed in the step S5, at this time, performing feature extraction on the contour features, then calculating a direction vector of the contour template, then performing affine transformation on the contour template, calculating a direction vector of each point of the searched image, and finally obtaining a contour matching result after calculating the distance similarity; in step S6, a pyramid may be usedSearching the top layer of the tower, calculating the contour and setting a threshold value sminThe match score for a potential match location must be greater than a threshold sminAnd is a local maximum; after the latent position is preliminarily determined, the next tracking is needed, a pyramid image of a lower layer is found, and the operation is repeated until a target object is found at the lowest layer of the pyramid image; then, fitting the similarity measurement in the neighborhood of the local maximum value into a polynomial so that the local part of the polynomial reaches the minimum value, and obtaining a relatively accurate pose; finally, adjusting the pose parameters by a least square method to obtain a more accurate final pose;
s7: then, different opening and closing operations and filling operations are required to be carried out on the threshold segmentation parts with different characteristics inside and outside the dots of each characteristic; because the internal defect and the external defect of the dot are possibly overlapped or the boundaries are connected, the internal defect and the external defect need to be subjected to union operation; therefore, for the divided 5 regions, the defect regions are respectively extracted, and then the union is made to extract the total defect region;
s8: for the defect inside the circle, because the circle part followed by the affine is the outline, the inside of the outline needs to be filled into an area mode, and then the circle part is cut off from the preprocessed graph; the acquisition environment is not excessively interfered by the outside, so that the simplest gray threshold segmentation is adopted on the premise of ensuring that the region of interest can be segmented, the speed is high, and the time is saved; then, the divided whole area is changed into a single independent area, and according to the actual defect identification standard, an area with the area within a certain range is selected, and the area of the selected area is the defect area;
s9: for the circle external defect, firstly, the external area needs to be cut off from the preprocessed graph; because 2 pixels are enlarged when the circle template is established, the circle is firstly reduced by 2 pixels; then, calculating the set theoretical difference of the two regions by using difference, namely subtracting the region in the sub region from the region in the main region, defining the result region as the main region, and deleting all points in the sub region; deleting the circle region from the outer contour region; in step S9, regarding the defect of the point outside the circle, the threshold segmentation is performed by analyzing the local mean and the standard deviation; depending on the brightness, input image pixels with high or low local standard deviation and local brightness or darkness can be selected; thus, regions can be segmented over non-uniform, noisy, or non-uniform illumination backgrounds; in step S9, regarding the defect of the outside block of the circle, the threshold segmentation is performed by a gray threshold segmentation method; similarly, obtaining a segmented region, and screening according to the size characteristics of the block area to obtain an area region in a certain range, namely a defect region; in step S9, regarding the defect of the outer line of the circle, the threshold segmentation is performed by a gray threshold segmentation method; similarly, obtaining a segmented region, and obtaining an area region in a certain range, namely a defect region, according to the actual linear defect identification standard through morphological operation; in step S9, regarding the defect on the outer surface of the circle, the threshold segmentation is performed by a gray threshold segmentation method; similar to other defect extraction modes, in the same way, the area region in a certain range is selected according to the identification standard of the actual planar defect through morphological operation repairing and screening, and the defect is extracted;
s10: when the detection in step S9 is completed, merging, filling the inside of the large area part of the overlapped defect area, and finally extracting the total defect area; recording the coordinates of the defective wafer, marking the central position of the defective area with a plus sign, and finally obtaining a detection result; the method has the advantages that the contour of the pattern of the internal wafer is affine transformed through various processing modes, different threshold segmentation and morphological processing are carried out on the cut region, and then the defect detection and defect marking of point defects, linear defects, planar defects, large-area pollution defects and unfilled corner defects are realized.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A testing method of a wafer map of an OLED display is characterized in that: the testing method of the wafer map of the OLED display comprises the following steps of:
s1: the method comprises the steps of firstly acquiring a wafer image, acquiring a high-quality wafer image with less noise interference through specific hardware equipment, and improving the defect extraction efficiency of a later-stage image processing algorithm; then, the industrial personal computer is controlled to collect, transmit and store the images of the wafer; the image acquisition, transmission and storage of the wafer are finished under the control of an industrial personal computer;
s2: performing median filtering image denoising processing on the preliminary image obtained in the step S1; moving a median filter to an overlapping range part which is overlapped with a detection image, firstly sorting pixel values in the range, generally selecting odd median filter templates according to the sorting from small to large, reserving the sorted middle number, and replacing the pixel value of a central point in the range by the middle number; if the template is an even template, calculating the average value of two numbers of the sorted middle pixels, then keeping the average value, and replacing the central pixel value in the range;
s3: performing enhancement processing on the image subjected to denoising in the step S2, wherein r represents the gray value of the image to be processed in an image histogram, and the value interval of r is set as [0, L-1] in L digital images with different gray levels]By h (r)k)＝nkRepresenting a discrete function in the sense of a pixel grey value r of the kth levelkThe total number of the pixel numbers of (2) is nk(ii) a Representing the total number of pixels of an image as NallNormalized gray level probability value is expressed as p (r)k) To represent the gray level r in an imagekThe probability of occurrence is expressed as P (r)k)＝nk/NallK is 0,1,2,3,.., L-1; then, after a probability density conversion function is calculated based on the original image, the gray scale of the converted image is distributed in a balanced and equal manner at all gray scale levels, and the contrast of the original image can be improved;
s4: carrying out binarization operation on the image, setting the pixel gray level in a certain threshold value range as a specific value, and setting the other part of the pixel gray level as another different value; the image after binarization is represented by T (x, y), the binarization threshold value is represented by T, and T is a certain gray value between [0, L-1 ]; the binarization processing algorithm is expressed as: t (x, y) ═ 0, f (x, y) < T; t (x, y) is 1, f (x, y) is more than or equal to T;
s5: using some characteristics with invariance of the graph for positioning, and extracting an interested region; common features include position, size, contour, other features of the target, etc., and by using these features, the moving state, position and direction of the target are not limited, and the target can be easily identified and automatically found, so that the region of interest can be extracted;
s6: performing feature extraction on the image processed in the step S5, at this time, performing feature extraction on the contour features, then calculating a direction vector of the contour template, then performing affine transformation on the contour template, calculating a direction vector of each point of the searched image, and finally obtaining a contour matching result after calculating the distance similarity;
s7: then, different opening and closing operations and filling operations are required to be carried out on the threshold segmentation parts with different characteristics inside and outside the dots of each characteristic; because the internal defect and the external defect of the dot are possibly overlapped or the boundaries are connected, the internal defect and the external defect need to be subjected to union operation; therefore, for the divided 5 regions, the defect regions are respectively extracted, and then the union is made to extract the total defect region;
s8: for the defect inside the circle, because the circle part followed by the affine is the outline, the inside of the outline needs to be filled into an area mode, and then the circle part is cut off from the preprocessed graph; the acquisition environment is not excessively interfered by the outside, so that the simplest gray threshold segmentation is adopted on the premise of ensuring that the region of interest can be segmented, the speed is high, and the time is saved; then, the divided whole area is changed into a single independent area, and according to the actual defect identification standard, an area with the area within a certain range is selected, and the area of the selected area is the defect area;
s9: for the circle external defect, firstly, the external area needs to be cut off from the preprocessed graph; because 2 pixels are enlarged when the circle template is established, the circle is firstly reduced by 2 pixels; then, calculating the set theoretical difference of the two regions by using difference, namely subtracting the region in the sub region from the region in the main region, defining the result region as the main region, and deleting all points in the sub region; deleting the circle region from the outer contour region;
s10: when the detection in step S9 is completed, merging, filling the inside of the large area part of the overlapped defect area, and finally extracting the total defect area; and recording the coordinates of the defective wafer, marking the central position of the defective area with a plus sign, and finally obtaining a detection result.
2. The method according to claim 1, wherein the testing method comprises the following steps: in step S1, the specific hardware device includes a light source, a lens, and a camera; and selecting an imaging scheme after the equipment is selected, and obtaining a digital image after the wafer is imaged.
3. The method according to claim 1, wherein the testing method comprises the following steps: in step S3, the whole may be divided into a plurality of parts, or the parts may be superimposed into a whole; if the image presents dark tones, the low gray level of the histogram presents dense distribution; otherwise, if the image presents bright tone, the high gray level of the histogram presents dense distribution; if the image has a high contrast, the gray values of the image are equally distributed in all gray levels.
4. The method according to claim 1, wherein the testing method comprises the following steps: in step S9, regarding the point defect outside the circle, the threshold segmentation is performed by analyzing the local mean and the standard deviation; depending on the brightness, input image pixels with high or low local standard deviation and local brightness or darkness can be selected; thus, regions may be segmented over a background that is not uniform, noisy, or has non-uniform illumination.
5. The method according to claim 1, wherein the testing method comprises the following steps: in step S9, regarding the defect of the block outside the circle, the threshold segmentation is performed by using a gray threshold segmentation method; and similarly, obtaining the divided regions, and screening according to the size characteristics of the block areas to obtain area regions in a certain range, namely defect regions.
6. The method according to claim 1, wherein the testing method comprises the following steps: in step S9, regarding the defect of the outer line of the circle, the threshold segmentation is performed by a gray threshold segmentation method; and similarly, obtaining the divided regions, and obtaining area regions within a certain range, namely defect regions, according to the actual linear defect identification standard through morphological operation.
7. The method according to claim 1, wherein the testing method comprises the following steps: in step S9, regarding the defect on the outer surface of the circle, the threshold segmentation is performed by a gray threshold segmentation method; similar to other defect extraction modes, the method selects an area region in a certain range according to the identification standard of the actual planar defect through morphological operation repairing and screening to extract the defect.
8. The method according to claim 1, wherein the testing method comprises the following steps: in step S6, a threshold S may also be set by using top-level search in the pyramid to calculate the contourminThe match score for a potential match location must be greater than a threshold sminAnd is a local maximum; after the latent position is preliminarily determined, the next tracking is needed, a pyramid image of a lower layer is found, and the operation is repeated until a target object is found at the lowest layer of the pyramid image; then, fitting the similarity measurement in the neighborhood of the local maximum value into a polynomial so that the local part of the polynomial reaches the minimum value, and obtaining a relatively accurate pose; and finally, adjusting the pose parameters by a least square method to obtain a more accurate final pose.
9. The method according to claim 1, wherein the testing method comprises the following steps: in step S1, an area CCD camera is selected as the acquisition camera, and a black-and-white industrial gigabit-capable camera of a model of MantaG-917B with a resolution of 900 ten thousand pixels is finally used according to the theoretical pixel size and the actual imaging effect.
10. The method according to claim 1, wherein the testing method comprises the following steps: in step S1, a 4-fold telecentric magnifying lens and an industrial telecentric lens with a C-type interface are selected according to the field of view size of the wafer imaging area being about 3mm, the size width of the sensor device being 12.8mm, and the imaging requirements.
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