A Method to Model Statistical Path Delays for Accurate Defect Coverage

Abstract

The statistical delay of a path is traditionally modeled as a Gaussian random variable assuming that the path is always sensitized by a test pattern. Its sensitization in various circuit instances varies among its test patterns and the pattern induced delay is non-Gaussian. It is modeled using probability mass functions. The defect coverage is improved by test pattern selection using machine learning. Experimental results demonstrate accuracy in defect coverage when comparing to existing methods.