Machine Vision Applied to Glass Cover Chip Defect Detection and Classification in the Image Sensor Packing Process

• Main Authors: KAUNG-SHING WANG, 王國興
• Other Authors: Chung-Feng Jeffrey Kuo
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/31993294189052875234

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 100 === This study focused on the glass cover chip defect detection and classification in the image sensor packing process. Glass cover chip defect detection can be divided into two parts, namely, the non-photoresist zone detection and photoresist zone detection. Defects in the non-photoresist zone include dark spots, solution residuals and scratches, and the photoresist zone has defects including photoresist deformity and chipping. Regarding the non-photoresist zone, this study used the smooth filtering approach to retain the image light source distribution and considerably reduce defect signals to generate a smooth image. Then, by subtracting it with the original image, the light source distribution was eliminated, while the defect image was retained for defect detection using binarization and morphology. The results confirmed that the detection process can be successfully applied in getting the image defects of solution residuals, dark spots and scratches. The comparison of the detected defect dimensions and the original image defects suggested that the process has very good effects on low contrast defects, such as solution residuals. Regarding high contrast defects such as dark spots, the amplification of defects can be apparently highlighted. Therefore, this study determined the high contrast defects using the minimum rectangular regions, and used the image zooming to enhance the contrast for image re-segmentation. The binary defect image in dimensions similar to the original defect image sizes was obtained for micro glass cover chip defect detection. The test on real standard samples found that it can detect the dark spot of minimum area at 2.5μm, which met the industrial standards of the glass cover chip defect sized at 5μm. This stud also extracted the image characteristics of the detected defects. The main characteristics of capturing included the average grey scale value, area, circularity, gray-scale uniformity. The decision-making tree J48 algorithm was used for recognition and the overall recognition rate reached 98.34%. Finally, regarding the photoresist zone parts, the adaptive template mask approach, coupled with the Otsu approach, the intersection computing, and the difference set computing, was used to realize the ideal photoresist deformity and chipping defect detection effects. The decision making tree J48 algorithm was used for recognition, and achieved a recognition rate of 100%. In this experiment, an image of pixel sized 2592*1944 was processed. The non-photoresist zone detection time was 0.98 sec, the photoresist zone detection time was 0.81 sec, and the total processing time was 1.79 sec. Thus, the time consuming problem of manual detection was solved. The systematic defect detection approach could be fully applied in the detection and classification of glass cover chip defects in the image sensing component packaging process to improve product yield.