Capacitance Extraction and Power Grid Analysis Using Statistical and AI Methods

Abstract

Capacitance extraction and power grid (PG) analysis for IC design involve large-scale numerical simulation problems. As the process technology becomes more complicated and design margin is shrinking, the capacitance field solver and power-grid matrix solver with high accuracy and capability for handing large and complex structure are highly demanded. In this invited paper, we present recent application of statistical and AI methods in these two fields. The Markov-chain model and relevant analysis are presented for developing an efficient technique for handling conformal dielectrics in the floating random walk based capacitance extraction. Then, two approaches reducing the computational cost of a domain decomposition based power-grid solver are presented. One employs supervised machine learning while the other is inspired by the A*-search algorithm.