Conference Paper

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

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Abstract

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.

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... Furthermore, while the algorithm in [15] works well for relatively noise free data generated using the frequency-domain solution of the Telegrapher's equation [27], when full-wave simulation data containing discretization noise is used, the macromodel generated is often non-passive. As a result, a costly passivity enforcement step such as [28]- [30] must be used to guarantee the passivity of the macromodel. ...
... However, this process leads to some loss of accuracy. Furthermore, for cases where the data is obtained from fullwave simulation, the resulting macromodel is often stable but non-passive and thus requires a CPU expensive passivity enforcement step using methods such as [28]- [30]. ...
... Furthermore, since LM method results in a compact descriptor system, the passivity checking step is also very efficient, particularly for systems with a large number of ports. Finally, in the rare cases when passivity correction is required the method in [28]- [30] can be used, although it is CPU expensive. ...
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