DocumentCode
3108145
Title
Automatic defect classification of TFT-LCD panels using machine learning
Author
Kang, S.B. ; Lee, J.H. ; Song, K.Y. ; Pahk, H.J.
Author_Institution
R&D Center, SNU Precision, Co., Ltd., Seoul, South Korea
fYear
2009
fDate
5-8 July 2009
Firstpage
2175
Lastpage
2177
Abstract
Defect classification in the liquid crystal display (LCD) manufacturing process is one of the most crucial issues for quality control. To resolve this constraint, an automatic defect classification (ADC) method based on machine learning is proposed. Key features of LCD micro-defects are defined and extracted, and support vector machine is used for classification. The classification performance is presented through several experimental results.
Keywords
image classification; liquid crystal displays; support vector machines; TFT-LCD panels; automatic defect classification; liquid crystal display; machine learning; micro-defects; support vector machine; Automatic control; Humans; Industrial electronics; Liquid crystal displays; Machine learning; Manufacturing processes; Quality control; Region 3; Support vector machine classification; Support vector machines; Defect Classification; LCD; Machine Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on
Conference_Location
Seoul
Print_ISBN
978-1-4244-4347-5
Electronic_ISBN
978-1-4244-4349-9
Type
conf
DOI
10.1109/ISIE.2009.5213760
Filename
5213760
Link To Document