In-TFT-array-process micro defect inspection using nonlinear principal component analysis
| Autor: | Yung Ting, Chi-Kai Wang, Ching-Shun Chen, Wei-Zhi Lin, Yi-Hung Liu, Jih-Shang Hwang, Zhi-Hao Kang |
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| Rok vydání: | 2009 |
| Předmět: |
Transistors
Electronic Computer science Thin-film-transistor liquid-crystal display Hardware_PERFORMANCEANDRELIABILITY thin film transistor liquid crystal display Catalysis Kernel principal component analysis Article law.invention lcsh:Chemistry Inorganic Chemistry law support vector machine Physical and Theoretical Chemistry lcsh:QH301-705.5 Molecular Biology Spectroscopy Principal Component Analysis Liquid-crystal display business.industry Organic Chemistry Pattern recognition General Medicine kernel principal component analysis Real image Inspection time Computer Science Applications Liquid Crystals Support vector machine lcsh:Biology (General) lcsh:QD1-999 Thin-film transistor defect inspection Principal component analysis automatic optical inspection Artificial intelligence business Algorithms TFT array process |
| Zdroj: | International Journal of Molecular Sciences Volume 10 Issue 10 Pages: 4498-4514 International Journal of Molecular Sciences, Vol 10, Iss 10, Pp 4498-4514 (2009) |
| ISSN: | 1422-0067 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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