Leak-Gauge: A late-mode variability-aware leakage power estimation framework

Leakage power has already become the major contributor to the total on-chip power consumption, rendering its estimation a necessary step in the IC design flow. The problem is further exacerbated with the increasing uncertainty in the manufacturing process known as process variability. We develop a method to estimate the variation of leakage power in the presence of both intra-die and inter-die process variability. Various complicating issues of leakage prediction such as spatial correlation of process parameters, the effect of different input states of gates on the leakage, and DIBL and stack effects are taken into account while we model the simultaneous variability of the two most critical process parameters, threshold voltage and effective channel length. Our subthreshold leakage current model is shown to fit closely on the HSPICE Monte Carlo simulation data with an average coefficient of determination (R2) value of 0.9984 for all the cells of a standard library. We demonstrate the adjustability of this model to wider ranges of variation and its extendability to future technology scalings. We also present a complete framework for estimation of full-chip leakage power and show that our framework which we call Leak-Gauge, imposes little timing penalty on the system design flow and is applicable to real design cases.