Optimized Detection of Marginal Defects in Standard Cells Using Unsupervised Learning

Marginal defects, such as high-resistance short or low-resistance open defects, are hard to detect by conventional pass-fail test methods because their manifestations are practically indistinguishable from the effects of regular variations. However, their coverage is essential for circuits with high-quality requirements and/or when early-life failures are a concern. In this paper, we propose an alternative detection concept based on evaluating several parametric responses of a circuit against a machine learning (ML) model. We use a 14nm FinFET transistor model validated against industrial measurements. We show that high detection performance is possible even when unsupervised learning that does not consider defective behavior is used; to this end, the procedure is generic. Moreover, an AUC score of over 0.96 is achieved when only measurements from a single voltage level are utilized, in contrast to earlier work. We also present a procedure to select a reduced set of test sequences, achieving an improvement of 50% reduction with a limited impact on detection performance.