Abstract
This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers and feedforward neural networks trained with various measurement selection criteria. The feedforward networks consistently perform as well as or better than the other classifiers in term of accuracy. Training using BBD consistently reduces the FFN training efforts without degrading the performance. Experimental results suggest that feedforward networks provide a cost efficient method for IC fault diagnosis in a large scale production testing environment.
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This work is supported by NSF-IUC CDADIC, Project 90-1.
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Wu, A., Meador, J. Measurement selection for parametric IC fault diagnosis. J Electron Test 5, 9–18 (1994). https://doi.org/10.1007/BF00971959
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DOI: https://doi.org/10.1007/BF00971959