Online Visual Inspectionsystem for OLED Defects

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Abstract:

The OLED defects generated in the manufacturing process restrict the development of OLED industry, machine vision based automatic OLED-inspection equipment can rapidly detect these defects and help to improve the OLED manufacturing process. The OLED images have the features of repeating texture background, uneven overall brightness of the image and the defects without obvious edge. In addition, an uncertain change in light and position of the inspection system increases the difficulty of the detection. Therefore, we propose the method to detect defects which take advance of the human eye characteristics of the Gabor filter and the unsupervised and fast segmentation features of the Fuzzy C-Means FCM algorithm. Through the combined 2-step segmentation, most OLED defects can be detected. The experimental tests are performed to validate the effectiveness of the proposed method. The result of the experiment shows that this method works well which can meet the requirements of robustness, automation of the fast and reliable of an online inspection system.

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3153-3158

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] C Y Li, B Wei. Thin Film encapsulation of OLED Displays with Organic-Inorganic Composition Film, 2008 Electronic Components and Technology Conference, 1820-1824.

DOI: 10.1109/ectc.2008.4550228

Google Scholar

[2] Su-Hwan Kim, Jee-Hyun Kim, Shin-Won Kang. Nondestructive defect inspection for LCDs using optical coherence tomography, Displays, 2011, 32, p.325–329.

DOI: 10.1016/j.displa.2011.04.002

Google Scholar

[3] K.N. Choi, J.Y. Lee, S.I. Yoo. Area-mura detection in TFT-LCD panel, in: Proceedings of SPIE, Vision Geometry XII, vol. 5300, 2004, p.151–158.

DOI: 10.1117/12.525557

Google Scholar

[4] S. Baek, W. Kim, T. Koo, I. Choi, K. Park. Inspection of defect on LCD panel using polynomial approximation, TENCON 2004, in: IEEE Region 10 Conference A, 2004, p.235–238.

DOI: 10.1109/tencon.2004.1414400

Google Scholar

[5] Lee J, Yoo s. Automatic detection of region-Mura defect in TFT-LCD[J], IEICE Transactions on Information and Systems, 2006, 87(10) , pp.2371-2378.

Google Scholar

[6] Lu, C.J., Tsai, D.M., 2005. Automatic defect inspection for LCDs using singular value decomposition, Internet, J. Adv. Manuf. Technol. 25, 53–61.

DOI: 10.1007/s00170-003-1832-6

Google Scholar

[7] Lu, C.J., Tsai, D.M., 2008. Independent component analysis-based defect detection in patterned liquid crystal display surfaces, Image Vis. Computer. 26, 955–970.

DOI: 10.1016/j.imavis.2007.10.007

Google Scholar

[8] JIANG B, WANG C, LIU H. Liquid crystal display surface uniformity defect inspection using analysis of variance and exponentially weighted moving average techniques, International Journal of Production research, 2005, 43(1) , pp.67-80.

DOI: 10.1080/00207540412331285832

Google Scholar

[9] GABOR D. Theory of communication, J. IEE, 1946, 93(26), 429-457.

Google Scholar

[10] Runping Han, Lingmin Zhang. Fabric Defect Detection Method Based on Gabor Filter Mask, Global Congress on Intelligent Systems, 2009, pp.184-188.

DOI: 10.1109/gcis.2009.356

Google Scholar

[11] Miin-Shen Yang , Hsu-Shen Tsai. A Gaussian kernel-based fuzzy c -means algorithm with a spatial bias correction, Pattern Recognition Letters, 2008, 29, p.1713–172.

DOI: 10.1016/j.patrec.2008.04.016

Google Scholar