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. © 2006 EuMA.
A distributed approach for millimetre-wave electron device modelling / Resca D.; Santarelli A.; Raffo A.; Cignani R.; Vannini G.; Filicori F.; Cidronali A.. - CD-ROM. - (2006), pp. 4057624.257-4057624.260. (Intervento presentato al convegno 1st European Microwave Integrated Circuits Conference, EuMIC 2006 tenutosi a Manchester, gbr nel 2006) [10.1109/EMICC.2006.282801].
A distributed approach for millimetre-wave electron device modelling
Resca D.;Santarelli A.;Cignani R.;Vannini G.;Filicori F.;
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. © 2006 EuMA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
