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
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;
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
DOI :
10.1109/ISIE.2009.5213760
