A Deterministic-Statistical Multiple-Defect Diagnosis Methodology

Abstract

Software diagnosis is the process of locating and characterizing a defect in a failing chip. It is the cornerstone of failure analysis that consequently enables yield learning and monitoring. However, multiple-defect diagnosis is challenging due to error masking and unmasking effects, and exponential complexity of the solution search process. This paper describes a three-phase, physically-aware diagnosis methodology called MDLearnX to effectively diagnose multiple defects, and in turn, aid in accelerating the design and process development. The first phase identifies a defect that resembles traditional fault models. The second and the third phases utilize the X-fault model and machine learning to identify correct candidates. Results from a thorough fault injection and simulation experiment demonstrate that MD-LearnX returns an ideal diagnosis 2X more often than commercial diagnosis. Its effectiveness is further evidenced through a silicon experiment, where, on average, MD-LearnX returns 5.3 fewer candidates per diagnosis as compared to state-of-the-art commercial diagnosis without losing accuracy.