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

This article proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. In this article, 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 nonapplicable for large-scale models, due to an excessive computational cost. This article builds on existing adaptive sampling methods and proposes a hybrid multistage 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.