Next Article in Journal
Electromagnetic Biostimulation of Living Cultures for Biotechnology, Biofuel and Bioenergy Applications
Next Article in Special Issue
Application of Δ- and Λ-Isomerism of Octahedral Metal Complexes for Inducing Chiral Nematic Phases
Previous Article in Journal
Köln-Timişoara Molecular Activity Combined Models toward Interspecies Toxicity Assessment
Previous Article in Special Issue
Liquid Crystals in Tribology
Article

In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis

1
Department of Mechanical Engineering, Chung Yuan Christian University, Chungli, 320, Taiwan
2
Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Hsinchu 310, Taiwan
3
Institute of Optoelectronic Sciences, National Taiwan Ocean University, Keelung, 202, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2009, 10(10), 4498-4514; https://doi.org/10.3390/ijms10104498
Received: 3 September 2009 / Revised: 5 October 2009 / Accepted: 21 October 2009 / Published: 22 October 2009
(This article belongs to the Special Issue Liquid Crystals)
Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image. View Full-Text
Keywords: thin film transistor liquid crystal display; TFT array process; automatic optical inspection; defect inspection; kernel principal component analysis; support vector machine thin film transistor liquid crystal display; TFT array process; automatic optical inspection; defect inspection; kernel principal component analysis; support vector machine
Show Figures

MDPI and ACS Style

Liu, Y.-H.; Wang, C.-K.; Ting, Y.; Lin, W.-Z.; Kang, Z.-H.; Chen, C.-S.; Hwang, J.-S. In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis. Int. J. Mol. Sci. 2009, 10, 4498-4514. https://doi.org/10.3390/ijms10104498

AMA Style

Liu Y-H, Wang C-K, Ting Y, Lin W-Z, Kang Z-H, Chen C-S, Hwang J-S. In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis. International Journal of Molecular Sciences. 2009; 10(10):4498-4514. https://doi.org/10.3390/ijms10104498

Chicago/Turabian Style

Liu, Yi-Hung, Chi-Kai Wang, Yung Ting, Wei-Zhi Lin, Zhi-Hao Kang, Ching-Shun Chen, and Jih-Shang Hwang. 2009. "In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis" International Journal of Molecular Sciences 10, no. 10: 4498-4514. https://doi.org/10.3390/ijms10104498

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop