A large-signal characterization of an HEMT using a multilayered neural network

We propose an approach to describe the large-signal behavior of a high electron-mobility transistor (HEMT) by using a multilayered neural network. To conveniently implement this in standard circuit simulators, we extracted the HEMT's bias dependent behavior in terms of conventional small-signal...

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Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques 1997-09, Vol.45 (9), p.1630-1633
Main Authors: Shirakawa, K., Shimiz, M., Okubo, N., Daido, Y.
Format: Article
Language:English
Subjects:
Analytical models
Circuit simulation
Equations
Extrapolation
HEMTs
MODFETs
Multi-layer neural network
Neural networks
Neurons
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Summary:We propose an approach to describe the large-signal behavior of a high electron-mobility transistor (HEMT) by using a multilayered neural network. To conveniently implement this in standard circuit simulators, we extracted the HEMT's bias dependent behavior in terms of conventional small-signal equivalent-circuit elements. We successfully represented seven intrinsic elements with a five-layered neural network (composed of 28 neurons) whose inputs are the gate-to source bias (V/sub gs/,) and drain-to-source bias (V/sub ds/) A "well-trained" neural network shows excellent accuracy and generates good extrapolations.
ISSN:0018-9480
1557-9670
DOI:10.1109/22.622932