De Stefano, MarcoMarcoDe StefanoGrivet-Talocia, StefanoStefanoGrivet-TalociaWendt, TorbenTorbenWendtYang, ChengChengYangSchuster, ChristianChristianSchuster2021-11-022021-11-022021-03-01IEEE Transactions on Components, Packaging and Manufacturing Technology 11 (3): 9345758 (2021-03-01)http://hdl.handle.net/11420/10743This 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.en2156-398520213471484IEEEAdaptive samplingHamiltonian matricesmacromodelingmodel order reductionmultiscale optimizationpassivityscatteringshieldingTechnikJournal Article10.1109/TCPMT.2021.3056746Other