Information-theoretic syndrome evaluation, statistical root-cause analysis, and correlation-based feature selection for guiding board-level fault diagnosis


Journal Article

© 1982-2012 IEEE. Reasoning-based functional-fault diagnosis has recently been advocated to achieve high diagnosis accuracy, low defect escapes, and reducing manufacturing cost. However, such diagnosis method requires a rich set of test items (syndromes) and a sizable database of faulty boards to learn from. An insufficient number of failed boards, ambiguous root-cause identification, and redundant or irrelevant syndromes can render reasoning-based diagnosis ineffective. Periodic evaluation and analysis can help locate weaknesses in a diagnosis system and thereby provide guidelines for redesigning the tests, which facilitates better diagnosis. We propose an information-theoretic framework for evaluating the effectiveness of and providing guidance to a reasoning-based functional-fault diagnosis system. Syndrome analysis based on feature selection methods provides a representative set of syndromes and suggests irrelevant syndromes in diagnosis. Root-cause analysis measures the discriminative ability of differentiating a given root cause from others. Results are presented for four types of diagnosis systems for three complex boards that are in volume production.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • June 1, 2015

Published In

Volume / Issue

  • 34 / 6

Start / End Page

  • 1014 - 1026

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2015.2399438

Citation Source

  • Scopus