Exploiting Extended Krylov Subspace for the Reduction of Regular and Singular Circuit Models

TL;DR

This paper introduces an advanced moment-matching model order reduction method based on extended Krylov subspaces, significantly improving accuracy for large-scale regular and singular circuit models, as demonstrated on industrial power grids.

Contribution

It presents a novel extended Krylov subspace-based moment-matching method that effectively reduces large-scale circuit models, outperforming standard Krylov approaches.

Findings

Achieves up to 83.69% error reduction compared to standard methods.

Handles both regular and singular large-scale circuits effectively.

Demonstrated on industrial IBM power grid models.

Abstract

During the past decade, Model Order Reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on moment-matching are well established due to their simplicity and computational performance in the reduction process. However, moment-matching methods based on the ordinary Krylov subspace are usually inadequate to accurately approximate the original circuit behavior. In this paper, we present a moment-matching method which is based on the extended Krylov subspace and exploits the superposition property in order to deal with many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation. Experimental results on industrial IBM power grids demonstrate that our method achieves an error reduction up to 83.69% over a standard Krylov subspace method.