Path selection for monitoring unexpected systematic timing effects

Abstract

This paper presents a novel path selection methodology to select paths for monitoring unexpected systematic timing effects. The methodology consists of three components: path filtering, path encoding, and path clustering. Given a large set of critical paths, in path filtering, the goal is to filter out paths that cannot be functionally sensitized. To explore the space of unexpected timing effects, a set of features are defined to encode paths into path vectors. Each feature is a source of concern that may potentially contribute to the cause of an unexpected timing effect. Finally, a kernel-based clustering algorithm is employed to group similar path vectors into clusters from which the best representative paths are selected for post-silicon monitoring. The effectiveness of our proposed methodology is demonstrated through experiments on an industrial ASIC design.