Additive Statistical Leakage Analysis Using Exponential Mixture Model

Abstract

Variation-aware leakage analysis becomes an essential design process as the technology node continuously shrinks. This article proposes a novel additive statistical leakage analysis method that uses exponential mixture model (EMM) to estimate the leakage distribution. Using a few leakage data for sub-blocks of an input circuit, we estimate any shape of leakage distribution regardless of new process nodes or operating conditions. Leakage distribution of an input circuit can be obtained by adding the leakage distributions of the sub-blocks. The proposed addition step sequentially adds the leakage distributions of sub-blocks that are expressed as EMMs. Before the addition step, we improve the accuracy by handling linear dependence among leakage simulation data of sub-blocks. In addition, we propose a method to reduce the number of components of an EMM to prevent exponential increase in runtime and memory during the addition process. The proposed method achieved 43.6 times improvement in goodness-of-fit of the estimated cumulative density functions compared to the best results of other analytic model-based methods.