Passive Reduced Order Macromodeling Based on Admittance Parameter Using Hamiltonian-Symplectic Matrix Pencil Perturbation

In this paper, a method for obtaining a reduced order macromodel based on measured/simulated admittance parameters is presented. Loewner Matrix based macromodeling may require an higher than optimal system order in order to preserve the passivity of the macromodel. In this paper, a modified order selection scheme, combined with the Hamiltonian-Symplectic Matrix Pencil perturbation methodology, is used in order to obtain a reduced order passive Loewner Matrix based macromodel based on measured/simulated admittance parameters. The efficiency of the new approach is shown in the examples.