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A Fault Diagnosis Methodology for Module Process of TFT-LCD Manufacture Using Support Vector Machines  

Shin, Hyun-Joon (Department of Management Engineering, Sangmyung University)
Publication Information
Journal of the Semiconductor & Display Technology / v.9, no.4, 2010 , pp. 93-97 More about this Journal
Abstract
Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.
Keywords
Fault diagnosis; Support vector machines; Classification; Artificial neural networks; Multilayer perception;
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Times Cited By KSCI : 1  (Citation Analysis)
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