A Highly Accurate Machine Learning Approach to Modelling PVT Variation Aware Leakage Power in FinFET Digital Circuits

Due to the advent of deep sub-micron technologies, statistical (in addition to temperature and supply voltage) variations aware estimation of leakages power has become prominent. Also, estimation of leakage currents at SPICE level guarantees the most accurate results, however not feasible means in high complexity ICs. This performs adversely for Monte-Carlo iterations for statistical analysis. In this paper we introduced an accurate machine learning technique to model statistical and operating variation aware estimation from Artificial Neural Network and regression based Multivariate Polynomial Regression which exhibits innately faster computation and attained error less than 1% for the targeted 16nm FinFET technology node although model is black box for any technology. The accuracy of the proposed technique has been tested over several basic cells and estimation of the complex circuits have been carried out utilizing the pre-modelled basic cells.