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

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.