Fault modeling and diagnosis for nanometric analog circuits

Abstract

Fault diagnosis of Integrated Circuits (ICs) has grown into a special field of interest in the Semiconductor Industry. Fault diagnosis is very useful at the design stage for debugging purposes, at high-volume manufacturing for obtaining feedback about the underlying fault mechanisms and improving the design and layout in future IC generations, and in cases where the IC is part of a larger safety-critical system (e.g. automotive, aerospace) for identifying the root-cause of failure and for applying corrective actions that will prevent failure reoccurrence and, thereby, will expand the safety features. In this summary paper, we present a methodology for fault modeling and fault diagnosis of analog circuits based on machine learning. A defect filter is used to recognize the type of fault (parametric or catastrophic), inverse regression functions are used to locate and predict the values of parametric faults, and multi-class classifiers are used to list catastrophic faults according to their likelihood of occurrence. The methodology is demonstrated on both simulation and high-volume manufacturing data showing excellent overall diagnosis rate.