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

fYear

2008

fDate

28-30 April 2008

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Sarnoff Symposium, 2008 IEEE

Conference_Location

Princeton, NJ

Print_ISBN

978-1-4244-1843-5

Type

conf

DOI

10.1109/SARNOF.2008.4520079

Filename

4520079