A survey of power estimation techniques in VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Parametric yield estimation considering leakage variability
Proceedings of the 41st annual Design Automation Conference
Full-chip analysis of leakage power under process variations, including spatial correlations
Proceedings of the 42nd annual Design Automation Conference
Projection-based statistical analysis of full-chip leakage power with non-log-normal distributions
Proceedings of the 43rd annual Design Automation Conference
Modeling and estimation of full-chip leakage current considering within-die correlation
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 45th annual Design Automation Conference
Efficient Monte Carlo based incremental statistical timing analysis
Proceedings of the 45th annual Design Automation Conference
Proceedings of the 47th Design Automation Conference
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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.