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A Ka-band class F MMIC amplifier design utilizing adaptable knowledge-based neural network modeling techniques

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8 Author(s)
Reece, M.A. ; Dept. of Electr. & Comput. Eng., Morgan State Univ., Baltimore, MD, USA ; White, C. ; Penn, J. ; Davis, B.
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This paper describes the first implementation of an adaptable knowledge-based neural network (AKBNN) model in a high efficiency class F MMIC (monolithic microwave integrated circuit) amplifier design at Ka-band in a 0.25 /spl mu/m GaAs PHEMT technology. A single-stage amplifier based upon the AKBNN model employed shows comparable results to measured performance of a gain of 7.5 dB, a PAE of 35%, and an output power of 17 dBm.

Published in:

Microwave Symposium Digest, 2003 IEEE MTT-S International  (Volume:1 )

Date of Conference:

8-13 June 2003