A multistage adaptive sampling scheme for passivity characterization of large-scale macromodels

Journal
IEEE transactions on components, packaging and manufacturing technology  
Volume
11
Issue
3
Start Page
471
End Page
484
Article Number
9345758
Citation
IEEE Transactions on Components, Packaging and Manufacturing Technology 11 (3): 9345758 (2021-03-01)
Publisher DOI
10.1109/TCPMT.2021.3056746
Scopus ID
2-s2.0-85100787668
Publisher
IEEE
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.
Subjects
Adaptive sampling
Hamiltonian matrices
macromodeling
model order reduction
multiscale optimization
passivity
scattering
shielding
DDC Class
600: Technik