Neural networks for large and small-signal modeling of MESFET/HEMT transistors: a comparative study

Author

Lázaro, Marcelino ; Santamaría, Ignacio ; Pantaleón, Carlos

Author_Institution

DICOM, Cantabria Univ., Santander, Spain

Volume

3

fYear

2000

fDate

2000

Firstpage

1272

Abstract

In this paper we present a comparative study, of three neural networks-based solutions for large and small-signal modeling of MESFET and HEMT transistors. The first two neural architectures are specific for this modeling problem: the Generalized Radial Basis Function (GRBF) network, and the Smoothed Piecewise Linear (SPWL) model. The results provided by these models are compared with those obtained by the well-known Multilayer Perceptron (MLP) network. Results are presented for both the large and small-signal regimes separately. Finally, a global model is proposed that is able to accurately characterize the whole behavior of the transistors. This model is based on a simple combination of the best models obtained for the two kinds of regimes

Keywords

Schottky gate field effect transistors; computational complexity; electronic design automation; high electron mobility transistors; multilayer perceptrons; neural net architecture; piecewise linear techniques; semiconductor device models; MESFET/HEMT transistors; generalized radial basis function network; global model; large signal modeling; multilayer perceptron network; neural architectures; neural networks; small-signal modeling; small-signal regimes; smoothed piecewise linear model; HEMTs; Intrusion detection; MESFETs; Microwave circuits; Microwave devices; Microwave transistors; Multilayer perceptrons; Neural networks; Piecewise linear approximation; Piecewise linear techniques;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE

Conference_Location

Baltimore, MD

ISSN

1091-5281

Print_ISBN

0-7803-5890-2

Type

conf

DOI

10.1109/IMTC.2000.848681

Filename

848681