GaN power amplifier design based on artificial neural network modelling

Xiao, Dongping; Schreurs, Dominique; Raedt, W. De; Derluyn, Joff; Balachander, K.; Viaene, J.; Germain, Marianne; Nauwelaers, Bart; Borghs, Gustaaf

doi:10.1109/emicc.2007.4412642

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…Contrary to empirical models such as Chalmers [1] model, behavioral models are cheap to extract and can describe any kind of devices. Their usage has been shown both for research and design purposes [2], [3]. However, since they provide very limited extrapolation capabilities, they can require a large number of samples, as the variables space should cover the interpolation domain.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear behavioral models of HEMTs using response surface methodology

Barmuta

Gibiino

Ferranti

et al. 2014

2014 International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applicati

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Abstract-In this paper, the response surface methodology is proposed to model nonlinear microwave devices using different sampling techniques. Each of the methods represents a distinct approach: exploration-oriented (Voronoi tessellation), nonlinearity-exploitation-oriented (LOcal Linear Approximation) and model-error-minimization-oriented. This allows to build accurate and compact global behavioral models of drain voltage at different harmonics of a 0.15 µm GaAs HEMT transistor with only few hundreds of samples. After choosing the best sampling technique, two types of global models are compared: Radial Basis Function and Kriging. It is shown that the modeling convergence depends on the model type, and better results are obtained using the Kriging model.

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Section: Introductionmentioning

confidence: 99%