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

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...

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Bibliographic Details
Main Authors: Reece, M.A., White, C., Penn, J., Davis, B., Bayne, M.Jr, Richardson, N., Thompson, W.I.I., Walker, L.
Format: Conference Proceeding
Language:English
Subjects:
Gallium arsenide
Integrated circuit modeling
Integrated circuit technology
Microwave amplifiers
Microwave integrated circuits
MMICs
Monolithic integrated circuits
Neural networks
PHEMTs
Power amplifiers
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Summary: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.
ISSN:0149-645X
2576-7216
DOI:10.1109/MWSYM.2003.1211014