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.
, Jargon, J.
, Remley, K.
, DeGroot, D.
and Gupta, K.
Artificial Neural Network Model for HEMTs Constructed from Large-Signal Time-Domain Measurements, 59th Auto. RF Tech. Group Conference, Seattle, WA, [online], https://doi.org/10.1109/ARFTGS.2002.1214677, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=33097
(Accessed December 2, 2023)