Minimizing outlier delay test cost in the presence of systematic variability

Abstract

This work proposes a methodology to minimize the application cost of outlier analysis when applied to delay testing in the presence of systematic variability. Support vector machine (SVM) outlier analysis algorithms and traditional entropy measures are used to detect delay defects by choosing a minimum number of suitable test clocks. Monte Carlo simulations generate realistic test data while information content measurements guide test clock selection. Exhaustive simulation found trade-offs between reducing the number of clocks, patterns, and chip samples. Substantial cost reduction was obtained with proper clock selection, while minimizing both test patterns and circuit samples required for effective outlier analysis.