Linear circuit fault diagnosis using neuromorphic analyzers

Author

Spina, Robert ; Upadhyaya, Shambhu

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA

Volume

44

Issue

3

fYear

1997

fDate

3/1/1997 12:00:00 AM

Firstpage

188

Lastpage

196

Abstract

This paper presents a method of analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a simple mechanism for automatic test pattern generation while reducing test time. A new diagnosis framework consisting of a white noise generator and an artificial neural network for response analysis and classification is proposed. This approach moves the diagnosis of analog circuits closer to the goal of built-in test. Networks of reasonable dimension are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances

Keywords

analogue circuits; automatic testing; built-in self test; circuit testing; electronic engineering computing; fault diagnosis; neural nets; noise generators; signal processing; white noise; ANN classifier; ATPG; BIST; analog fault diagnosis; artificial neural network; automatic test pattern generation; diagnosis framework; linear circuit fault diagnosis; neural network application; neuromorphic analyzers; response analysis; response classification; robust diagnosis; white noise generator; Analog circuits; Artificial neural networks; Automatic test pattern generation; Automatic testing; Circuit testing; Fault diagnosis; Linear circuits; Neuromorphics; Noise robustness; White noise;

fLanguage

English

Journal_Title

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7130

Type

jour

DOI

10.1109/82.558453

Filename

558453