Variation-Aware Layer Assignment With Hierarchical Stochastic Optimization on a Multicore Platform

Abstract

As the very large scale integration (VLSI) technology enters the nanoscale regime, VLSI design is increasingly sensitive to variations on process, voltage, and temperature. Layer assignment technology plays a crucial role in industrial VLSI design flow. However, existing layer assignment approaches have largely ignored these variations, which can lead to significant timing violations. To address this issue, a variation-aware layer assignment approach for cost minimization is proposed in this paper. The proposed layer assignment approach is a single-stage stochastic program that directly controls the timing yield via a single parameter, and it is solved using Monte Carlo simulations and the Latin hypercube sampling technique. A hierarchical design is also adopted to enable the optimization process on a multicore platform. Experiments have been performed on 5000 industrial nets, and the results demonstrate that the proposed approach: 1) can significantly improve the timing yield by 64% in comparison with the nominal design and 2) can reduce the wire cost by 15.7% in comparison with the worst case design.