Boundary cost optimization for Alternate Test

Abstract

Alternate Test has demonstrated in the last decade that advanced machine-learning tools can leverage the accuracy gap between functional test and indirect, or model-based, test. If a regression approach is taken, a model should be trained for each specification. The advantage is that the results are interpreted just like performance measurements but the drawback is that accuracy is required over the full variation range. On the other hand, a classification approach can be seen as a wiser solution since it locates the pass/fail boundary, which inherently contains all the specification information, in the cheap measurement space. Cost optimization due to imbalance between test escape and yield loss is usually handled by guard-banding on specifications. This is straightforward to translate to regression-based Alternate Test but not for classification-based. This paper shows that two different asymmetric approaches consistently outperforms an off-the-shelf symmetric algorithm. The first technique is based on manipulating the decision threshold while the second technique directly builds an optimized pass-fail boundary by considering different costs to penalize test escapes and yield losses.