Detection of Spot-Type Defects on Liquid Crystal Display Modules

Abstract

In the manufacturing process of a LCM(Liquid Crystal Display Module), many spot-type defects can be occurred on the surface of LCM due to various physical factors. The existence and pattern of such defects are very important in determining whether the LCM is normal or not. To enhance the accuracy and reproducibility of LCD inspection, this paper introduces an automated inspection method using a computer vision technique. The LCM defects are classified into macro-defects and micro-defects. One is detected by using a macro-view area camera and the other by using six micro-view line cameras. An adaptive multilevel thresholding method using statistical characteristics of local block is proposed for a macro-view image while the detection method for a micro-view images composed of R, G, B sub-cells involves a pattern elimination technique using the pixel difference and adaptive multilevel thresholding. The proposed inspection system is tested using many real LCMs having different defects, and the resulting performance confirms the effectiveness of the proposed algorithm.

Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble

“…Zhang and Zhang (2005) fuzzy neural network to solve the difficulty in modeling muras. The works in (Kim, Kwak, Song, Choi, & Park, 2004;Lee & Yoo, 2004;Mori, Tanahashi, & Tsuji, 2004;Oh et al, 2004;Pratt, Sawkar, & O'Reilly, 1998;Ryu, Oh, Kim, Koo, & Park, 2004; were also proposed for mura detection. Lu and Tsai (2004) used the singular value decomposition method to reconstruct the defective images, by which four kinds of small defects on the surfaces of panels can be detected.…”

“…According to the types of the defects detected, the above approaches can be divided into three categories: (1) the first category is the defect detection for glass substrates (Tsai & Chao, 2005;Tsai et al, 2006), performed before the glass substrates enter the TFT-LCD manufacturing, (2) the second one detects the surface defects on TFT panels (Lu & Tsai, 2004), performed after the array process is complete, and (3) the last category inspects different types of mura defects (Baek et al, 2004;Zhang, Yan, Zhang, & Huang, 2006;Ryu et al, 2004;Lee & Yoo, 2004;Kim et al, 2004;Oh et al, 2004;Song et al, 2004;Mori et al, 2004;Pratt et al, 1998), performed in cell or module assembly processes.…”

Defect Inspection in Low-Contrast LCD Images Using Hough Transform-Based Nonstationary Line Detection

“…Then, a genetic algorithm was applied to extract the boundary of anomalous brightness region. Kim et al [8] studied the detection of spot-type defects in LCD panel surfaces. An adaptive multiple-level thresholding method based on the statistical characteristics of the local block is applied to segment the macroview defect from the background surface.…”

Adaptive Defect Detection Method based on Skewness of the Histogram in LCD Image

“…. 자동 검사 시스템에 적용하기 위한 기존의 대표적인 결함 검출 방법으로는 Gabor 필터를 이용하는 방법 [6] , Otsu 방법 [7] , 평균과 표준편차를 이용한 방법 방법으로는 STD 방법 [8] 과 순차적 STD 방법 [9] 이 있다. 이용한 평탄화 기법을 사용한다 [6] .…”