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

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 doen well, the accuracy of recognition is 100 per cent, and the testing speed of the proposed recognition system is amost 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.

Citation

Chen, W., Tsai, C. and Hsu, S. (2006), "The Neural‐Network Approach to Recognize Defect Pattern in LED Manufacturing", Asian Journal on Quality, Vol. 7 No. 3, pp. 58-69. https://doi.org/10.1108/15982688200600027