Microdefect detection method, device and equipment for CF substrate

Abstract

The invention provides a microdefect detection method, a device and equipment for a CF substrate. The detection method comprises the steps of: acquiring a gray-scale image of the CF substrate; detecting the gray-scale value of the gray-scale image's to-be-detected point; acquiring a corresponding normal gray-scale value range of the to-be-detected point in a color area; judging whether the gray-scale value of the to-be-detected point is in the corresponding normal gray-scale range; if the gray-scale value of the to-be-detected point is not in the corresponding normal gray-scale range, judging the to-be-detected point an abnormal point. The microdefect detection method, device and equipment for the CF substrate provided by the invention can conduct adaptive defect detection on different color areas of the CF substrate distinctively, thus improving the defect detection rate of the CF substrate, and effectively solving the problem of undetected defects.

Description

technical field [0001] The invention relates to the technical field of optical detection, in particular to a microscopic defect detection method, device and equipment of a CF substrate. Background technique [0002] At present, automatic optical inspection (AOI) equipment is usually used in the manufacture of display panels to detect microscopic defects of CF (color filter, color filter, color film) substrates. Microscopic defects include white defects and black defects. The types of white defects include photoresist loss, whitening, etc., and the types of black defects include particles, photoresist residues, foreign matter residues, and backsmear. Since the defects of the CF substrate directly affect the display effect of the display panel, AOI inspection is very important for the quality monitoring of the CF substrate. If the AOI inspection cannot timely stop the CF substrate with defects, it may lead to a large number of abnormal CF substrate flow. In the later stage, i...

AI Technical Summary

Problems solved by technology

However, the existing parameter settings cannot detect microscopic abnormal defects with regular shapes (such as Figure 3-5 As shown), because when such defects are compared with the surrounding reference pixels, there is no difference or a small difference in gray scale, and the set threshold cannot be reached, and the AOI detection is judged to be normal

Although the AOI defect detection rate can be improved by lowering the threshold value, when the threshold value drops to a certain level, a large area of ​​false positives will occur, and the normal area will be detected as a defect

In addition, such defects cannot be detected by adjusting the brightness of the light source and comparing the distance value, thus affecting the quality of the product

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