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The Neural-Network Approach to Recognize Defect Pattern in LED Manufacturing  

Chen, Wen-Chin (Graduate Institute of Management of Technology Chung-Hua University)
Tsai, Chih-Hung (Department of Industrial Engineering and Management Ta-Hwa Institute of Technology)
Hsu, Shou-Wen (Graduate Institute of Management of Technology Chung-Hua University)
Publication Information
International Journal of Quality Innovation / v.7, no.3, 2006 , pp. 58-69 More about this Journal
Abstract
This paper presents neural network-based recognition system for automatic light emitting diode (LED) inspection. The back-propagation neural network (BPNN) is proposed and tested. The current-voltage (I-V) characteristic data of LED from the inspection process is used for the network training and testing. This study selects 300 random samples as network training and employs 100 samples as network testing. The experimental results show that if the classification work is done well, the accuracy of recognition is 100%, and the testing speed of the proposed recognition system is almost one half faster than the traditional inspection system does. The proposed neural-network approach is successfully demonstrated by real data sets and can be effectively developed as a recognition system for a practical application purpose.
Keywords
Neural Network-based Recognition System; Light Emitting Diode; Back-propagation Neural Network; Current-voltage (I-V) Characteristic Data;
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