Board-level functional fault diagnosis using artificial neural networks, support-vector machines, and weighted-majority voting

Journal Article (Journal Article)

Increasing integration densities and high operating speeds lead to subtle manifestation of defects at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost and adversely impact yield. Advanced machine-learning (ML) techniques offer an unprecedented opportunity to increase the accuracy of board-level functional diagnosis and reduce high-volume manufacturing cost through successful repair. We propose a smart diagnosis method based on two ML classification models, namely, artificial neural networks (ANNs) and support-vector machines (SVMs) that can learn from repair history and accurately localize the root cause of a failure. Fine-grained fault syndromes extracted from failure logs and corresponding repair actions are used to train the classification models. We also propose a decision machine based on weighted-majority voting, which combines the benefits of ANNs and SVMs. Three complex boards from the industry, currently in volume production, and additional synthetic data, are used to validate the proposed methods in terms of diagnostic accuracy, resolution, and quantifiable improvement over current diagnostic software. © 1982-2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ye, F; Zhang, Z; Chakrabarty, K; Gu, X

Published Date

  • May 1, 2013

Published In

Volume / Issue

  • 32 / 5

Start / End Page

  • 723 - 736

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2012.2234827

Citation Source

  • Scopus