AMS Test Stimulus Generation and Response Analysis Using Hyperdimensional Clustering: Minimizing Misclassification Rate

Prevalent test strategies for analog/mixed-signal systems rely on either (a) prediction of device-under-test (DUT) design specifications from observed test responses to carefully crafted alternate test stimulus, or (b) detecting outliers from known optimized test response statistics of devices subjected to expected manufacturing process variations. In both of these test paradigms, misclassification of DUTs (false positives and false negatives) is not explicitly considered during test generation itself due to computational complexity, but rather based on post-test determination of test acceptance thresholds. In this paper, we propose a novel test generation approach based on hyperdimensional clustering, that explicitly targets DUT misclassification rate during test stimulus generation itself. The use of hyperdimensional vectors for clustering good and bad devices along with a set of simple vector operations for training and inference allows fast determination of misclassification rate within the test generation procedure itself. Experimental results show that the test generation times are reduced by 15X with significant improvements in DUT misclassification rate.