Diagnosis of multiple arbitrary faults with mask and reinforcement effect

Authors:
Jing Ye;Yu Hu;Xiaowei Li
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China and Graduate University of Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
Proceedings of the Conference on Design, Automation and Test in Europe
Year:
2010

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Abstract

We propose a multiple-fault diagnosis method with high diagnosability, resolution, first-hit and short run time. The method has no assumption on fault models, thus can diagnose arbitrary faults. To cope with the multiple-fault mask and reinforcement effect, two key techniques of construction and scoring of fault-tuple equivalence trees are introduced to choose and rank the final candidate locations. Experimental results show that, when the circuits have 2 arbitrary faults, the average diagnosability and resolution are 98% and 0.95, respectively, with the best case 100% and 1.00. Moreover, in average, even when 21 arbitrary faults exist, our method can still identify 93% of them with the resolution 0.78, increased by 41% and 39% in comparison with the latest work where the diagnosability and resolution are 66% and 0.56. Finally, 96% of our top-ranked candidate locations are actual fault locations.