A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels

TL;DR

This paper introduces a hierarchical adaptive sampling method that efficiently detects passivity violations in large-scale linear macromodels, significantly reducing computational costs compared to existing techniques.

Contribution

It presents a novel multi-stage hybrid algorithm tailored for large-scale models, improving efficiency in passivity characterization over traditional spectral-based methods.

Findings

Major reduction in computational requirements

Effective detection of passivity violations

Applicable to models with high dynamic order and many input/output ports

Abstract

This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, large-scale is intended both in terms of dynamic order and especially number of input/output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches.