Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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An effective classification method for TFT-LCD film defect images using intensity distribution and shape analysis

명암도 분포 및 형태 분석을 이용한 효과적인 TFT-LCD 필름 결함 영상 분류 기법

  • 노충호 (한국외국어대학교 산업경영공학과) ;
  • 이석룡 (한국외국어대학교 산업경영공학부) ;
  • 조문신 ((주) 카사테크)
  • Received : 2009.07.01
  • Accepted : 2010.04.19
  • Published : 2010.08.31
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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.

TFT-LCD 생산 과정에서 발생하는 결함을 정확하게 분류하여 결함 유형에 따라 폐기, 사용가능 등의 의사결정을 적절하게 내리는 것은 수율 증가 및 생산성 향상에 필수적인 요소이다. 본 논문에서는 TFT-LCD 생산 라인에서 획득한 결함 영상에 대하여 명암도 분포(intensity distribution) 및 결함 영상의 형태 특징(shape feature)을 분석하여 효과적으로 필름 결함 유형을 분류하는 기법을 제시한다. 본 연구에서는 먼저 필름 결함 영상을 결함 영역과 결함이 아닌 배경 영역으로 이진화하고, 결함 영역에서 결함의 선형성(linearity), 명암도 분포를 고려한 형태 특징 등의 여러 가지 특징을 분석하여 기준 영상(referential image) 데이터베이스를 구축하였으며, 분류하고자 하는 결함 영상과 데이터베이스에 저장된 기준 영상과의 매칭 비용 함수(matching cost function)를 정의하여 적절히 매칭시킴으로써 결함의 유형을 결정하였다. 제시한 기법의 성능을 검증하기 위하여 실제 TFT-LCD 생산 라인에서 획득한 결함 영상들을 대상으로 분류 실험을 수행하였으며, 실험 결과 생산 라인에서 이용할 수 있을 정도의 상당한 수준의 분류 정확도를 달성하였음을 보여주었다.

Keywords

References

  1. 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.
  2. 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. https://doi.org/10.1080/00207540410001716480
  3. 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. https://doi.org/10.1080/00207540500140732
  4. 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 https://doi.org/10.1080/00207540600622464
  5. 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 https://doi.org/10.4028/www.scientific.net/KEM.270-273.808
  6. 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.
  7. 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
  8. 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 https://doi.org/10.4028/www.scientific.net/KEM.270-273.233
  9. 박종성, 정규원, 강찬구, "비젼 시스템을 이용한 LCD용 편광 필름의 결함 검사에 관한 연구," 산업과학기술연구 논문집, 17권(2003), pp.47-54.
  10. 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. https://doi.org/10.1109/34.49050
  11. 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. https://doi.org/10.1109/34.250843
  12. 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 https://doi.org/10.1016/j.cie.2008.01.015
  13. 김성진, 노충호, 이석룡, "영상 분석에 기초한 실시간 필름 결함 검출." 데이터베이스 연구, 2008.
  14. 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.
  15. Belongie S. and Malik J., "Matching with Shape Contexts," IEEE Workshop on Content- based Access of Image and Video Libraries(CBAIVL'00), 2000.
  16. Joncker R. and Volgenant A., "A shortest augmenting path algorithm for dense and sparse linear assignment problem," Computing, Vol.38, 1987, pp. 325-340. https://doi.org/10.1007/BF02278710