In this paper, we describe an extraction procedure of nonlinear models for microwave field-effect transistor (FET). We use a nonlinear model available in commercial CAD tools, and we extract the parameters by combining direct extraction and numerical optimization. We determine a first estimate of the model parameters by few DC and S-parameter measurements. Next, we refine the parameters by optimization against low-frequency and high-frequency vector-calibrated large-signal measurements gathered with a Large-Signal-Network Analyzer (LSNA). As case study we consider a 0.25×200μm2 GaN FET on SiC for power amplifier applications. Ultimately, we want to show that a good accuracy level can be achieved while minimizing the extraction effort and that an accurate model can be built and suitably tailored depending on the final application.

A procedure for the extraction of a nonlinear microwave GaN FET model

VADALA', Valeria;RAFFO, Antonio;VANNINI, Giorgio;
2017

Abstract

In this paper, we describe an extraction procedure of nonlinear models for microwave field-effect transistor (FET). We use a nonlinear model available in commercial CAD tools, and we extract the parameters by combining direct extraction and numerical optimization. We determine a first estimate of the model parameters by few DC and S-parameter measurements. Next, we refine the parameters by optimization against low-frequency and high-frequency vector-calibrated large-signal measurements gathered with a Large-Signal-Network Analyzer (LSNA). As case study we consider a 0.25×200μm2 GaN FET on SiC for power amplifier applications. Ultimately, we want to show that a good accuracy level can be achieved while minimizing the extraction effort and that an accurate model can be built and suitably tailored depending on the final application.
2017
Avolio, Gustavo; Vadala', Valeria; Angelov, Iltcho; Raffo, Antonio; Marchetti, Mauro; Vannini, Giorgio; Schreurs, Dominique
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2353330
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