Analog IC Fault Diagnosis based on Wavelet Neural Network Ensemble

Author

Lei, Zuo ; Jinhui, Wang ; Ligang, Hou ; Shuqin, Geng ; Wuchen, Wu

Author_Institution

VLSI & Syst. Lab., Beijing Univ. of Technol., Beijing, China

Volume

4

fYear

2009

fDate

19-21 May 2009

Firstpage

45

Lastpage

48

Abstract

A fault diagnosis method for analog IC diagnosis based on wavelet neural network ensemble (WNNE) and Adaboost algorithm, is proposed in this paper. 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 model of the circuit with fault diagnosis system is built. Simulation results have shown that this claim is more effective than wavelet neural network (WNN).

Keywords

analogue integrated circuits; fault diagnosis; feature extraction; neural nets; wavelet transforms; Adaboost algorithm; analog IC fault diagnosis; faulty feature vectors; feature extraction; wavelet decomposition; wavelet neural network ensemble; Analog circuits; Analog integrated circuits; Boosting; Circuit faults; Circuit testing; Fault diagnosis; Feature extraction; Intelligent networks; Intelligent systems; Neural networks; Adaboos; WNNE; fault diaghosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3571-5

Type

conf

DOI

10.1109/GCIS.2009.95

Filename

5209336