This paper presents the response surface methodology in modeling of nonlinear microwave devices. First, different combinations of sampling techniques and types of radial basis functions are evaluated in simulations of the drain current of a 0.15-μ m GaAs HEMT transistor described by the Chalmers model. It allows to determine the best settings of the response surface methodology for the modeling of active microwave devices. It is shown that the best sampling strategy is a combination of space-exploration (Voronoi), problem-exploitation (LOLA), and model-error-driven sample rankers. From the various radial basis function models, the fastest convergence is achieved with exponential functions. This knowledge is then used in behavioral modeling of a low-power amplifier AG303 measured in the load-pull setup. It is shown that the response surface methodology outperforms commonly used factorial design of experiments. Moreover, it gives accurate models within just a few tens of samples. However, attention has to be paid at the noisy regions, which might be oversampled by the sampling techniques.

Compact behavioral models of nonlinear active devices using response surface methodology

GIBIINO, GIAN PIERO;
2015

Abstract

This paper presents the response surface methodology in modeling of nonlinear microwave devices. First, different combinations of sampling techniques and types of radial basis functions are evaluated in simulations of the drain current of a 0.15-μ m GaAs HEMT transistor described by the Chalmers model. It allows to determine the best settings of the response surface methodology for the modeling of active microwave devices. It is shown that the best sampling strategy is a combination of space-exploration (Voronoi), problem-exploitation (LOLA), and model-error-driven sample rankers. From the various radial basis function models, the fastest convergence is achieved with exponential functions. This knowledge is then used in behavioral modeling of a low-power amplifier AG303 measured in the load-pull setup. It is shown that the response surface methodology outperforms commonly used factorial design of experiments. Moreover, it gives accurate models within just a few tens of samples. However, attention has to be paid at the noisy regions, which might be oversampled by the sampling techniques.
2015
Barmuta, Paweł; Ferranti, Francesco; Gibiino, Gian Piero; Lewandowski, Arkadiusz; Schreurs, Dominique M. M.-P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/531235
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