Testability driven statistical path selection

In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical path selection takes advantage of it in at-speed testing. In deterministic path selection, the separation of path selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical path selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical path selection method. Our proposed method is based on a generalized path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical path selection followed by test generation.