Electron device modelling at very high frequencies needs, as a preliminary step, the identification of suitable parasitic elements mainly describing the passive structure used for accessing the intrinsic device. However, when dealing with device modelling at millimetre-wave frequencies conventional lumped parasitic networks necessarily become less adequate in describing inherently distributed parasitic phenomena. In this paper, a distributed approach is adopted for the modelling of the parasitic network and a new identification procedure, based on electromagnetic simulation and conventional S-parameter measurements, is proposed. The intrinsic device, obtained after de-embedding from the distributed parasitic network, is particularly suitable for the extraction of accurate nonlinear models. Preliminary validation results are provided in the paper.
D. Resca, A. Santarelli, A. Raffo, R. Cignani, G. Vannini, F. Filicori, et al. (2006). A distributed approach for millimetre-wave electron device modelingA distributed approach for millimetre-wave electron device modeling. LONDON : Horizon House Publications Ltd.
A distributed approach for millimetre-wave electron device modelingA distributed approach for millimetre-wave electron device modeling
RESCA, DAVIDE;SANTARELLI, ALBERTO;CIGNANI, RAFAEL;FILICORI, FABIO;
2006
Abstract
Electron device modelling at very high frequencies needs, as a preliminary step, the identification of suitable parasitic elements mainly describing the passive structure used for accessing the intrinsic device. However, when dealing with device modelling at millimetre-wave frequencies conventional lumped parasitic networks necessarily become less adequate in describing inherently distributed parasitic phenomena. In this paper, a distributed approach is adopted for the modelling of the parasitic network and a new identification procedure, based on electromagnetic simulation and conventional S-parameter measurements, is proposed. The intrinsic device, obtained after de-embedding from the distributed parasitic network, is particularly suitable for the extraction of accurate nonlinear models. Preliminary validation results are provided in the paper.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.