Reduction of large-scale RLCk models via low-rank balanced truncation

TL;DR

This paper introduces a low-rank balanced truncation method using extended Krylov subspaces to efficiently reduce large-scale RLCk models, significantly decreasing model size while maintaining accuracy.

Contribution

It presents a novel low-rank balanced truncation approach tailored for large-scale RLCk models, leveraging extended Krylov subspaces for improved efficiency.

Findings

Up to 5.5 times smaller ROMs achieved

Maintains similar accuracy to existing methods

Effective on large analog and mixed-signal circuits

Abstract

Model order reduction (MOR) is an important step in the design process of integrated circuits. Specifically, the electromagnetic models extracted from modern complex designs result in a large number of passive elements that introduce limitations in the simulation process. MOR techniques based on balanced truncation (BT) can overcome these limitations by producing compact reduced-order models (ROMs) that approximate the behavior of the original models at the input/output ports. In this paper, we present a low-rank BT method that exploits the extended Krylov subspace and efficient implementation techniques for the reduction of large-scale models. Experimental evaluation on a diverse set of analog and mixed-signal circuits with millions of elements indicates that up to x5.5 smaller ROMs can be produced with similar accuracy to ANSYS RaptorX ROMs.