Int. J. Mol. Sci. 2011, 12(9), 5762-5781; doi:10.3390/ijms12095762
Article

Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

1,* email and 2email
Received: 5 July 2011; in revised form: 8 August 2011 / Accepted: 16 August 2011 / Published: 9 September 2011
(This article belongs to the Special Issue Liquid Crystals 2011)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.
Keywords: thin film transistor liquid crystal display; array process; defect detection; machine learning; support vector data description
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MDPI and ACS Style

Liu, Y.-H.; Chen, Y.-J. Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description. Int. J. Mol. Sci. 2011, 12, 5762-5781.

AMA Style

Liu Y-H, Chen Y-J. Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description. International Journal of Molecular Sciences. 2011; 12(9):5762-5781.

Chicago/Turabian Style

Liu, Yi-Hung; Chen, Yan-Jen. 2011. "Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description." Int. J. Mol. Sci. 12, no. 9: 5762-5781.

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