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Artificial Neural Network Model for HEMTs Constructed from Large-Signal Time-Domain Measurements



Dominique Schreurs, Jeffrey Jargon, Kate Remley, Donald C. DeGroot, Kuldip Gupta


We develop a methodology to construct behavioral models for microwave devices from time-domain large-signal measurements. The behavioural model for the considered class of devices (microwave transistors) is defined by expressing the terminal currents as functions of the state variables, being the embedded voltages. In this work, we show that artificial neural networks (ANNs) are veluable candidates to represent these relationships. They outperform models based on multivariate polynomials, because they can better represent the typical physical characteristics of the considered devices. Experimental results are quantitatively confirmed by comparison metrics.
Conference Dates
June 6-7, 2002
Conference Location
Seattle, WA
Conference Title
59th Auto. RF Tech. Group Conference


artificial neural network, large-signal, measurment, model, nonlinear, time-domain


Schreurs, D. , Jargon, J. , Remley, K. , DeGroot, D. and Gupta, K. (2002), Artificial Neural Network Model for HEMTs Constructed from Large-Signal Time-Domain Measurements, 59th Auto. RF Tech. Group Conference, Seattle, WA, [online],, (Accessed May 29, 2024)


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Created June 6, 2002, Updated October 12, 2021