Papers Published

1. Ye, F; Zhang, Z; Chakrabarty, K; Gu, X, Information-theoretic syndrome evaluation, statistical root-cause analysis, and correlation-based feature selection for guiding board-level fault diagnosis, Ieee Transactions on Computer Aided Design of Integrated Circuits and Systems, vol. 34 no. 6 (June, 2015), pp. 1014-1026, Institute of Electrical and Electronics Engineers (IEEE) [doi] .
   (last updated on 2022/12/30)

   Abstract:
   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.