Automated inspection of micro-defect recognition system for color filter. (July 2015)

• Record Type: Journal Article
• Authors: Jeffrey Kuo, Chung-Feng; Peng, Kai-Ching; Wu, Han-Cheng; Wang, Ching-Chin
• Is Part Of: Optics and lasers in engineering. Volume 70(2015)
• Page Start: 6
• Page End: 17
• Publication Date: 2015-07
• Subjects: Adaptive template matching -- Image subtraction -- Control limit law -- Back-propagation neural network -- Minimum distance classifier; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2015.01.009
• Languages: English
• ISSNs: 0143-8166

Abstract

Abstract: This study focused on micro-defect recognition and classification in color filters. First, six types of defects were examined, namely grain, black matrix hole (BMH), indium tin oxide (ITO) defect, missing edge and shape (MES), highlights, and particle. Orthogonal projection was applied to locate each pixel in a test image. Then, an image comparison was performed to mark similar blocks on the test image. The block that best resembled the template was chosen as the new template (or matching adaptive template). Afterwards, image subtraction was applied to subtract the pixels at the same location in each block of the test image from the matching adaptive template. The control limit law employed logic operation to separate the defect from the background region. The complete defect structure was obtained by the morphology method. Next, feature values, including defect gray value, red, green, and blue (RGB) color components, and aspect ratio were obtained as the classifier input. The experimental results showed that defect recognition could be completed as fast as 0.154 s using the proposed recognition system and software. In micro-defect classification, back-propagation neural network (BPNN) and minimum distance classifier (MDC) served as the defect classification decision theories for the five acquired feature values. To validate the proposed system, this study used 41 defects as training samples, and treated the feature values of 307 test samples as the BPNN classifier inputs. The total recognition rate was 93.7%. When an MDC was used, the total recognition rate was 96.8%, indicating that the MDC method is feasible in applying automatic optical inspection technology to classify micro-defects of color filters. The proposed system is proven to successfully improve the production yield and lower costs. Highlights: Inspected grain, black matrix hole, indium tin oxide defect, missing edge and shape, highlights, and particle. Orthogonal projection is used to record the coordinates of various pixels of the testing image. Defect grayscale, red, green, and blue color components, and aspect ratio were acquired as the classifier input. Back propagation neural network and minimum distance classifier served as the defect classification decision theories.