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Linking Statistical Learning to Diagnosis
May-June 2008 (vol. 25 no. 3)
pp. 232-239
Pouria Bastani, University of California, Santa Barbara
Li-C. Wang, University of California, Santa Barbara
Magdy S. Abadir, Freescale Semiconductor
Traditional diagnosis of defects is based on an assumed fault model. A failing chip is diagnosed to find the subset of faults that can best explain the failure. This article discusses a new type of diagnosis to explain the mismatch between predicted timing behavior from modeling and simulation, and observed timing behavior measured on silicon. The authors illustrate that this type of diagnosis can be formulated as a statistical learning problem, and they propose a statistical diagnosis framework based on a learning technique called support vector classification. To diagnose the mismatch, they use a list of features to describe path characteristics. Each feature represents a potential source of uncertainty causing the mismatch. The output of the diagnosis is a rank of those features, such that a feature inducing a larger unexpected timing deviation is ranked higher. The authors explain the design of the proposed diagnosis framework, and they present experimental results to illustrate the effectiveness of the feature-ranking method.

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Index Terms:
statistical learning, timing mismatch, statistical diagnosis, feature ranking, timing behavior
Citation:
Pouria Bastani, Li-C. Wang, Magdy S. Abadir, "Linking Statistical Learning to Diagnosis," IEEE Design & Test of Computers, vol. 25, no. 3, pp. 232-239, May-June 2008, doi:10.1109/MDT.2008.79
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