A neural-network approach for an automatic LED inspection system

This paper presents neural-network-based recognition system for automatic light emitting diode (LED) inspection. Two types of neural-networks, back-propagation neural-network (BPNN) and radial basis function network (RBFN), are proposed and tested. The current–voltage (I–V) data from the LED inspection process is used for the network training and testing. This study adopts 300 random picking 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% for BPNN and 96% for RBFN, and the testing speed of the proposed approach 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 system for a practical application purpose.