Efficient smart sampling based full-chip leakage analysis for intra-die variation considering state dependence

Leakage power minimization is critical to semiconductor design in nanoscale CMOS. On the other hand increasing variability with scaling adds complexity to the leakage analysis problem. In this work we seek to achieve tractability in Monte Carlo-based statistical leakage analysis. A novel approach for fast and accurate statistical leakage analysis considering inter-die and intra-die components is proposed. We show that the optimal way to select samples, to capture intra-die variation accurately, is according to the probability distribution function of total process variation. Intelligent selection of samples is performed using a Quasi Monte Carlo technique. Results are presented for benchmarks with sizes varying from approximately 5,000 to 200,000 gates. The largest benchmark with 198461 gates is evaluated in 3 minutes with the proposed approach compared to 23 hours for random sampling with comparable accuracy. Compared to a conventional analytical approach using Wilkinson's approximation, the proposed technique offers superior accuracy while maintaining efficiency. State dependence and multiple sources of variation are considered and the approach is scalable with number of process parameter variables for standard cell characterization cost. We also show reduction in sample size to meet target accuracy for computing leakage distribution due to the inter-die component only when compared to random selection of samples.