Enhancing diagnosis resolution for delay defects based upon statistical timing and statistical fault models

In this paper, we propose a new methodology for diagnosis of delay defects in the deep submicron domain. The key difference between our diagnosis framework and other traditional diagnosis methods lies in our assumptions of the statistical circuit timing and the statistical delay defect size. Due to the statistical nature of the problem, achieving 100% diagnosis resolution cannot be guaranteed. To enhance diagnosis resolution, we propose a 3-phase diagnosis methodology. In the first phase, our goal is to quickly identify a set of candidate suspect faults that are most likely to cause the failing behavior based on logic constraints. In the second phase, we obtain a much smaller suspect fault set by applying a novel diagnosis algorithm that can effectively utilize the statistical timing information based upon a single defect assumption. In the third phase, our goal is to apply additional fine-tuned patterns to successfully narrow down to more exact suspect defect locations. Using a statistical timing analysis framework, we demonstrate the effectiveness of the proposed methodology for delay defect diagnosis, and discuss experimental results based on benchmark circuits.