Representative critical-path selection for aging-induced delay monitoring

Transistor aging degrades path delay over time and may eventually induce circuit failure due to timing variations. Therefore, in-field tracking of path delays is essential and to respond to this need, several delay sensor designs have been proposed in the literature. However, due to the significant overhead of these designs and the large number of critical paths in today´s IC, it is infeasible to monitor the delay of every critical path in silicon. We present an aging-aware representative path-selection method that allows us to measure the delay of a small set of paths and infer the delay of a larger pool of paths that are likely to fail due to transistor aging. Moreover, since aging is affected by process variations and runtime variations in temperature and voltage, we use machine learning and linear algebra to incorporate these variations during representative path selection. Simulation results for benchmark circuits highlight the accuracy of the proposed approach for predicting critical path delay based on the selected representative paths.