Fast diagnosis of integrated circuit faults using feedforward neural networks

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • J. Meador
  • A. Wu
  • C. T. Tseng
  • T. S. Lin

Detail(s)

Original languageEnglish
Title of host publicationProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages269-273
ISBN (Print)780301641
Publication statusPublished - 1991
Externally publishedYes

Conference

TitleInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period8 - 12 July 1991

Abstract

The authors present experimental results which show that feedforward neural networks are suitable for analog IC fault diagnosis. The results suggest that feedforward networks provide a cost-efficient method for IC fault diagnosis in large-scale production. The authors compare the diagnostic accuracy and the computational requirements of a simple feedforward network against that of Gaussian maximum likelihood and K-nearest neighbors classifiers. The feedforward network was found to provide an order-of-magnitude improvement in diagnostic speed while consistently performing as well as or better than any of the other classifiers in terms of accuracy.

Citation Format(s)

Fast diagnosis of integrated circuit faults using feedforward neural networks. / Meador, J.; Wu, A.; Tseng, C. T.; Lin, T. S.

Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks. Publ by IEEE, 1991. p. 269-273.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review