Title :
Device Modeling using Neural Network Techniques for Solid State Power Amplifier Applications
Author :
Karangu, Caroline W. ; Ogunniyi, Aderinto J. ; Henriquez, Stanley L. ; Reece, Michel ; White, Carl
Author_Institution :
Dept. of Electr. & Comput. Eng., Morgan State Univ., Baltimore, MD
Abstract :
In this paper, the integration of an advanced device model for solid state power amplifiers is proposed. The model accounts for both the static and dynamic response of HEMT devices very accurately. This large signal model utilizes a feed-forward neural network using the back-propagation method with the Levenberg-Marquardt (LM) algorithm. The model presented was used on a 3 MI 0.15 mum power pHEMT process developed by Triquint. Excellent agreement is observed between the advance model and measured DC, AC and load pull data.
Keywords :
HEMT integrated circuits; backpropagation; electronic engineering computing; feedforward neural nets; power amplifiers; HEMT devices; Levenberg-Marquardt algorithm; backpropagation method; device modeling; feedforward neural network; load pull data; neural network techniques; solid state power amplifier applications; Biological system modeling; Gallium arsenide; IEEE members; Microwave amplifiers; Neural networks; Neurons; PHEMTs; Power amplifiers; Solid modeling; Solid state circuits; Gallium Arsenide; Ka Band; Solid State Power Amplifiers; neural networks; pHEMT/MESFET;
Conference_Titel :
Sarnoff Symposium, 2008 IEEE
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4244-1843-5
DOI :
10.1109/SARNOF.2008.4520079
