Advanced Neural Network Techniques for GaN-HEMT Dynamic Behavior Characterization

Advanced Neural Network Techniques for GaN-HEMT Dynamic Behavior Characterization

This paper presents a new approach to build RF dynamic behavioral models, based on time-delay neural networks (TDNNs), suitable for FET devices, and capable to identify the working class and to characterize both short- and long-term device memory, through a time-domain training procedure, for a wide...

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Bibliographic Details
Published in:2006 European Microwave Integrated Circuits Conference pp. 249 - 252
Main Authors: Orengo, G., Colantonio, P., Giannini, F., Pirola, M., Camarchia, V., Guerrieri, S.D.
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2006
Subjects:
Delay effects
Dynamic behavioral model
Finite impulse response filter
GaN
Intrusion detection
Network synthesis
Neural Network
Neural networks
Neurons
Nonlinear filters
Radio frequency
Sampling methods
Thermal model
Time domain analysis
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Summary:This paper presents a new approach to build RF dynamic behavioral models, based on time-delay neural networks (TDNNs), suitable for FET devices, and capable to identify the working class and to characterize both short- and long-term device memory, through a time-domain training procedure, for a wide range of input power levels. The presented model has been effectively applied to GaN-based devices, working in class A, AB and B
ISBN:9782960055184
2960055187
DOI:10.1109/EMICC.2006.282799