Title :
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
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;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-5890-2
DOI :
10.1109/IMTC.2000.848681
