Fault Diagnosis of Analog IC Based on Wavelet Neural Network Ensemble

Authors:
Lei Zuo;Ligang Hou;Wuchen Wu;Jinhui Wang;Shuqin Geng
Affiliations:
VLSI & System Lab, Beijing University of Technology, Beijing, China 100022;VLSI & System Lab, Beijing University of Technology, Beijing, China 100022;VLSI & System Lab, Beijing University of Technology, Beijing, China 100022;VLSI & System Lab, Beijing University of Technology, Beijing, China 100022;VLSI & System Lab, Beijing University of Technology, Beijing, China 100022
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

A new method of analog IC fault diagnosis is proposed in this paper, which is based on wavelet neural network ensemble (WNNE) technique and Adaboost algorithm. This makes the way of the directory be of use in fault, and enhances the validity of the fault diagnosis. Using wavelet decomposition as a tool for extracting feature, Then, after training the WNNE by faulty feature vectors, the fault diagnosis of a radar scanning circuit is implemented with this new method. The simulation results show that the new method is more effective than the traditional wavelet neural network (WNN) method.