Authors

Marco De Stefano, Stefano Grivet-Talocia, Torben Wendt, Cheng Yang, Christian Schuster

Abstract

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.

Citation

  • Journal: IEEE Transactions on Components, Packaging and Manufacturing Technology
  • Year: 2021
  • Volume: 11
  • Issue: 3
  • Pages: 471–484
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/tcpmt.2021.3056746

BibTeX

@article{De_Stefano_2021,
  title={{A Multistage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels}},
  volume={11},
  ISSN={2156-3985},
  DOI={10.1109/tcpmt.2021.3056746},
  number={3},
  journal={IEEE Transactions on Components, Packaging and Manufacturing Technology},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={De Stefano, Marco and Grivet-Talocia, Stefano and Wendt, Torben and Yang, Cheng and Schuster, Christian},
  year={2021},
  pages={471--484}
}

Download the bib file

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