Browse > Article

An effective classification method for TFT-LCD film defect images using intensity distribution and shape analysis  

Noh, Chung-Ho (한국외국어대학교 산업경영공학과)
Lee, Seok-Lyong (한국외국어대학교 산업경영공학부)
Zo, Moon-Shin ((주) 카사테크)
Publication Information
Journal of Korea Multimedia Society / v.13, no.8, 2010 , pp. 1115-1127 More about this Journal
Abstract
In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.
Keywords
TFT-LCD; defect classification; defect inspection; image analysis; shape feature;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lu. C.J. and Tsai. D.M., "Defect inspection of patterned thin film transistor-liquid crystal display panels using a fast sub-image-based singular value decomposition," IJPR, Vol.42, No.20, 2004, pp. 4331-4351.   DOI   ScienceOn
2 Ryu J.S., Oh J.H., Kim J.G., Koo T.M., and Park K.H., "TFT-LCD Panel Blob-Mura Inspection using the correlation of wavelet coefficients," IEEE TENCON 2004.
3 J.Y. Lee and S.I. Yoo, "Automatic Detection of Region-Mura Defect in TFT-LCD," IEICE TRANS. INF & SYSE, Vol. E87-D. No.10, 2004, pp.2371-2378
4 J.H. Oh, D.M. Kwak, K.B. Lee, Y.C. Song, D.H. Choi, and K.H. Park, "Line Defect Detection in TFT-LCD Using Directional Filter Bank and Adaptive Multilevel Thresholding," Key Engineering Materials, Vol.270-273, 2004, pp.233-238   DOI
5 Tsunashimahigashi, Kouhoku-ku, and Yokohama, "Hybrid Inspection System for LCD Color Filter Panels," Proc. of 10th Int. Conf. on Instrumentation and measurement Technology, 1994, pp. 689-692.
6 Tsai D.M and Hung C.Y., "Automatic defect inspection of patterned thin film transistorliquid crystal display (TFT-LCD) panels using one-dimensional Fourier reconstruction and wavelet decomposition," IJPR, Vol.43, No.21, 2005, pp.4589-4607.   DOI   ScienceOn
7 D.M. Tsai, S.T. Chuang, and Y.H. Tseng, "One-dimensional-based defect inspection of multiple patterned TFT -LCD panels using Fourier image reconstruction," IJPR, Vol.45, No.6 (2007), pp.1297-1321   DOI   ScienceOn
8 Joncker R. and Volgenant A., "A shortest augmenting path algorithm for dense and sparse linear assignment problem," Computing, Vol.38, 1987, pp. 325-340.   DOI
9 R. M. Haralick, K. Shanmugam, and I. Dinstein. "Textural Features for Image Classification." IEEE Trans. on Systems, Man and Cybernetics, SMC, Vol.3, No.6(1973), pp.610-620.
10 Belongie S. and Malik J., "Matching with Shape Contexts," IEEE Workshop on Content- based Access of Image and Video Libraries(CBAIVL'00), 2000.
11 박종성, 정규원, 강찬구, "비젼 시스템을 이용한 LCD용 편광 필름의 결함 검사에 관한 연구," 산업과학기술연구 논문집, 17권(2003), pp.47-54.
12 김성진, 노충호, 이석룡, "영상 분석에 기초한 실시간 필름 결함 검출." 데이터베이스 연구, 2008.
13 T. Pavlidis and Y. T. Liow, "Integrating Region Growing and Edge Detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.12(1990), pp.225-233.   DOI   ScienceOn
14 C. Chu and J.K. Aggarwal, "The Integration of Image Segmentation Maps Using Region and Edge Information," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.15(1993), pp.1241-1252.   DOI   ScienceOn
15 Y.G. Yoon, S.L. Lee, C.W. Chung, and S.H. Kim, "An Effective Defect Inspection System for Polarized Film Images Using Image Segmentation and Template Matching Techniques," Computers & Industrial Engineering, Vol.55 Issue 3, October 2008, pp. 567-583   DOI   ScienceOn
16 Kim W.S., Kwak D.M., Song Y.C., Choi D.H., and Park K. H., "Detection of Spot-Type Defects on Liquid Crystal Display Modules," Key Engineering Materials, Vols. 270-273, 2004, pp. 808-813   DOI