A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels
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.
Cite
@article{arxiv.2011.02789,
title = {A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels},
author = {Marco De Stefano and Stefano Grivet-Talocia and Torben Wendt and Cheng Yang and Christian Schuster},
journal= {arXiv preprint arXiv:2011.02789},
year = {2020}
}
Comments
Submitted to the IEEE Transactions on Components, Packaging and Manufacturing Technology