Liu Yi-Hung, Chen Yan-Jen
Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 320, Taiwan.
Int J Mol Sci. 2011;12(9):5762-81. doi: 10.3390/ijms12095762. Epub 2011 Sep 9.
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.
缺陷检测被认为是提高薄膜晶体管液晶显示器（TFT-LCD）制造中面板良品率的有效方法。在本研究中，我们聚焦于阵列工艺，因为它是TFT-LCD制造中的首个关键工艺。阵列工艺中会出现各种缺陷，其中一些可能会对LCD面板造成严重损坏。因此，如何设计一种能够从LCD面板表面捕获的图像中稳健地检测缺陷的方法变得至关重要。此前，支持向量数据描述（SVDD）已成功应用于LCD缺陷检测。然而，其泛化性能有限。在本文中，我们提出了一种新颖的单类机器学习方法，称为拟共形核SVDD（QK-SVDD）来解决这一问题。通过将拟共形变换引入预定义核，QK-SVDD可以显著提高传统SVDD的泛化性能。在台湾一家LCD制造商提供的真实LCD图像上进行的实验结果表明，所提出的QK-SVDD不仅获得了96%的高缺陷检测率，而且大大提高了SVDD的泛化性能。改进幅度已超过30%。此外，结果还表明，QK-SVDD缺陷检测器能够在60毫秒内完成对LCD图像的缺陷检测任务。
