Application of Correlation-Based Regression Analysis for Improvement of Power Distribution Network

Abstract

Stochastic approaches for effective power distribution network optimization are proposed. Considering node voltages obtained using dynamic voltage drop analysis as sample variables, multi-variate regression is conducted to optimize clock timing metrics, such as clock skew or jitter. Aggregate correlation coefficient (ACC) which quantifies connectivity between different chip regions is defined in order to find a possible insufficiency in wire connections of a power distribution network. Based on the ACC, we also propose a procedure using linear regression to find the most effective region for improving clock timing metrics. By using the proposed procedure, effective fixing point were obtained two orders faster than by using brute force circuit simulation.