An extraction technique for small signal intrinsic parameters of HEMTs based on artificial neural networks

Authors:  M. Hayati, B. Akhlaghi

Abstract:
This paper presents a fast and accurate procedure for extraction of small signal intrinsic parameters of AlGaAs/GaAshighelectronmobility transistors (HEMTs)using artificialneuralnetwork (ANN)techniques. The extraction procedure has been done in a wide range offrequencies and biases at various temperatures. Intrinsicparameters ofHEMT are acquiredusing its values of common-source S-parameters. Twodifferent ANN structures have been constructed in this work to extract the parameters, multi layer perceptron (MLP) and radial basis function (RBF) neural networks. These two kinds of ANNs are compared to each other in terms of accuracy, speed and memory usage. To validate the capability ofthe proposed method in small signal modeling of GaAs HEMTs, data and modeled values of S-parameters of a 200 m gate width 0.25 m GaAs HEMT are compared to each other and very good agreement between them is achieved up to 30 GHz. The effect of bias, temperature and frequency conditions on the extracted parameters of HEMT has been investigated, and the obtained results match the theoretical expectations. The proposed model can be inserted to computer-aided design (CAD) tools in order to have an accurate and fast design, simulation and optimization of microwave circuits including GaAs HEMTs.

Keywords:
HEMT
Equivalent circuit parameters
Parameter extraction
Artificial neural networks
Radial basis function
Multi layer perceptron

Published in: AEÜ-International Journal of Electronics and Communications (Volume 67, Issue 2, February 2013)

Publisher: Elsevier

ISSN Information: 1434-8411

An extraction technique for small signal intrinsic parameters of HEMTs based on artificial neural networks

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