DocumentCode :
1659936
Title :
A large signal elements´ simulation of GaAs MESFET using neural network model
Author :
Yifan Gao ; Gu, Cong
Author_Institution :
Xi´´an Highway Univ., China
fYear :
1999
fDate :
6/21/1905 12:00:00 AM
Firstpage :
593
Lastpage :
596
Abstract :
Neural networks are important as fast and flexible tools for microwave modeling, simulation, optimization and design. A new approach of a neural-network based model is proposed to determine the large signal elements of a GaAs MESFET. To conveniently implement this in standard circuit simulators, we extracted the single-cell GaAs MESFET\´s bias-dependent behavior in terms of conventional small signal equivalent circuit elements. We represented seven intrinsic elements with a four-layered neural network whose inputs are the gate-to-source bias and drain-to-source bias. A "well-trained" neural network shows excellent accuracy and generates good extractions. The results of calculation between the new neural model and improved optimization are in good agreement.
Keywords :
III-V semiconductors; Schottky gate field effect transistors; electronic engineering computing; equivalent circuits; gallium arsenide; neural nets; semiconductor device models; GaAs; GaAs MESFET; bias-dependent behavior; circuit simulators; drain-to-source bias; four-layered neural network; gate-to-source bias; intrinsic elements; large signal element simulation; neural network model; optimization; small signal equivalent circuit elements; well-trained neural network; Equivalent circuits; Gallium arsenide; Hoses; MESFET circuits; Microwave devices; Nails; Neural networks; Optimization methods; Scattering parameters; Signal analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Electromagnetics and Its Applications, 1999. Proceedings. (ICCEA '99) 1999 International Conference on
Print_ISBN :
0-7803-5802-3
Type :
conf
DOI :
10.1109/ICCEA.1999.825253
Filename :
825253
Link To Document :
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