Bridging the accuracy of functional and machine-learning-based mixed-signal testing

Numerous machine-learning-based test methodologies have been proposed in recent years as a fast alternative to the standard functional testing of mixed-signal/RF integrated circuits. While the test error probability of these methods is rather low, it is still considered prohibitive for accurate production testing. In this paper, we demonstrate how to minimize this test error probability and, thus, how to bridge the accuracy of functional and machine-learning-based test methods. The underlying idea is to measure the confidence of the machine-learning-based test decision and retest the small fraction of circuits for which this confidence is low via standard functional test. Through this approach, the majority of circuits are tested using fast machine-learning-based tests, which, nevertheless, are equivalent to the standard functional ones with regards to test error probability. By varying the acceptable confidence level, the proposed method enables exploration of the trade-off between test time and test accuracy