[논문]Automatic TFT-LCD Mura Inspection Based on Studentized Residuals in Regression Analysis

Chuang, Yu-Chiang; Fan, Shu-Kai S.

[국내논문] Automatic TFT-LCD Mura Inspection Based on Studentized Residuals in Regression Analysis 원문보기

Industrial engineering & management systems : an international journal, v.8 no.3, 2009년, pp.148 - 154

Chuang, Yu-Chiang (Department of Industrial Engineering and Management Yuan Ze University) , Fan, Shu-Kai S. (Department of Industrial Engineering and Management Yuan Ze University)

Abstract ▼ AI-Helper

In recent days, large-sized flat-panel display (FPD) has been increasingly applied to computer monitors and TVs. Mura defects, appearing as low contrast or non-uniform brightness region, sometimes occur in manufacturing of the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD). Implementation of automatic Mura inspection methods is necessary for TFT-LCD production. Various existing Mura detection methods based on regression diagnostics, surface fitting and data transformation have been presented with good performance. This paper proposes an efficient Mura detection method that is based on a regression diagnostics using studentized residuals for automatic Mura inspection of FPD. The input image is estimated by a linear model and then the studentized residuals are calculated for filtering Mura regions. After image dilation, the proposed threshold is determined for detecting the non-uniform brightness region in TFT-LCD by means of monitoring the every pixel in the image. The experimental results obtained from several test images are used to illustrate the effectiveness and efficiency of the proposed method for Mura detection.

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가설 설정

The idea of the proposed threshold is inspired from statistical process control (SPC) in quality control (QC), and the threshold is set equal to μ + n*σ, where n is the constant to be pre-specified by the user.

제안 방법

Those defects are not easy to be detected by human inspection or simple threshold method due to the human subjectivity and non-uniform illumination conditions. In this paper, the automatic inspection method is proposed for detecting the Mura defect in TFT-LCD.

이론/모형

The effectiveness is evaluated by visualizing the outcomes in comparison; the efficiency is evaluated by the computation time required. The estimated image is constructed by the linear regression model for calculating studentized residuals, and Mura regions and Mura pixels are extracted by two proposed threshold methods. As a practical guidance, the suggested threshold value and threshold parameter n for the studentized residuals is 2.

성능/효과

3. The Mura regions in the merged binary image are extended by processing morphological region dilation to ensure that the non-Mura regions contain no Mura pixels.
Table 2 lists the resulting images for different threshold parameter n on normal images. First, the normal image (without Mura defects) is examined, and the best result is that there is no Mura pixel found. The best result is obtained when n is set equal to 3.
This paper has presented a Mura detection method that is based on regression diagnostics using studentized residuals for automatic Mura inspection of FPD, and the experimental results show that the proposed method is very effective as well as efficient. The effectiveness is evaluated by visualizing the outcomes in comparison; the efficiency is evaluated by the computation time required.

후속연구

5, respectively, for the Mura pixel detection to achieve robust inspection outcomes. For further research, large-scale industrial images and other Mura types should be included to further validate the effectiveness of the proposed method. Alternative regression diagnostics particularly designed for ring Mura defects deserve a future study.

참고문헌 (8)

Gonzalez, R. C. and Woodes, R. E. (2002), Digital image Processing, 2nd edition, Prentice Hall
Lee, J. Y. and Yoo, S. I. (2004), Automatic detection of region-mura defect in TFT-LCD, IEICE Transactions on Information and Systems, E87-D(10), 2371-2378
Lu, C. J. and Tsai, D. M. (2005), Automatic Defect Inspection for LCDs Using Singular Value Decomposition. The International Journal of Advanced Manufacturing Technology, 25, 53-61

상세보기
Lu, C. J. and Tsai, D. M. (2008), Independent component analysis-based defect detection in patterned liquid crystal display surfaces, Image and Vision Computing, 26, 955-970

상세보기
Montgomery, D. C. and Peck, E. A. (1992), Introduction to Linear Regression Analysis, 2nd edition, Wiley-Interscience
Pratt, W. K., Sawkar S. S., and O'reilly, K. (1998), Automatic blemish detection in liquid crystal flat panel displays, IS&T/SPIE Symposium on electronic imaging: Science and Technology, 3306, San Jose, California
VESA (2001), Flat Panel Display Measurements. Standard Ver. 2.0. June 1
Wang, Z. Y. and Ma, L. (2006) Implementation of regionmura detection based on recursive polynomial-surface fitting algorithm. Proceedings of the 2nd Asia International Symposium on Mechatronics, Hong Kong, China, 1-6

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[국내논문] Automatic TFT-LCD Mura Inspection Based on Studentized Residuals in Regression Analysis 원문보기

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[국내논문] Automatic TFT-LCD Mura Inspection Based on Studentized Residuals in Regression Analysis 원문보기

Abstract ▼ AI-Helper

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가설 설정

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참고문헌 (8)

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원문 보기

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