Machine-learning-based test methods for analog/RF devices have been the subject of intense investigation over the last decade. However, despite the significant cost benefits that these methods promise, they have seen a limited success in replacing the traditional specification testing, mainly due to the incurred test error which, albeit small, cannot meet industrial standards. To address this problem, we introduce a neural system that is trained not only to predict the pass/fail labels of devices based on a set of low-cost measurements, as aimed by the previous machinelearning-based test methods, but also to assess the confidence in this prediction. Devices for which this confidence is insufficient are then retested through the more expensive specification testing in order to reach an accurate test decision. Thus, this two-tier test approach sustains the high accuracy of specification testing while leveraging the low cost of machine-learning-based testing. In addition, by varying the desired level of confidence, it enables the exploration of the tradeoff between test cost and test accuracy and facilitates the development of cost-effective test plans. We discuss the structure and the training algorithm of an ontogenic neural network which is embodied in the neural system in the first tier, as well as the extraction of appropriate measurements such that only a small fraction of devices are funneled to the second tier. The proposed test-error-moderation method is demonstrated on a switched-capacitor filter and an ultrahigh-frequency receiver front end.

No abstract