A fast and stochastic analysis methodology for electromigration (EM) assessment of power distribution networks is presented in this paper. We examine the impact of variability on EM time-to-failure (TTF), considering altered current densities due to global/local process variations as well as the fundamental factors that cause the conventional EM TTF distribution. Through the novel variations-aware current density model based on Hermite polynomial chaos, we demonstrate significant margins in EM lifetime when compared with the traditional worst case approach. On the other hand, we show that the traditional approach is altogether incompetent in handling transistor-level local variations leading to significantly optimistic lifetime estimates for lower metal level interconnects of power delivery network. Subsequently, we attempt to bridge the conventional, component-level EM verification method to the system level failures, inspired by the extreme order statistics. We make use of asymptotic order models to determine the TTF for the k th component failure due to EM, and demonstrate application of this approach in developing IR drop aware system-level failure criteria.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|State||Published - Sep 2017|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
- Electromigration (EM)
- extreme value theory
- lognormal distribution
- worst case (WC) corner