Title :
A Ka-band class F MMIC amplifier design utilizing adaptable knowledge-based neural network modeling techniques
Author :
Reece, M.A. ; White, C. ; Penn, J. ; Davis, B. ; Bayne, M.Jr. ; Richardson, N. ; Thompson, W.I.I. ; Walker, L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Morgan State Univ., Baltimore, MD, USA
Abstract :
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.
Keywords :
HEMT integrated circuits; III-V semiconductors; MMIC amplifiers; circuit CAD; circuit simulation; gallium arsenide; integrated circuit design; integrated circuit measurement; integrated circuit modelling; knowledge based systems; neural nets; 0.25 micron; 29 GHz; 35 percent; 5 to 35 GHz; 7.5 dB; AKBNN; GaAs; GaAs PHEMT technology; adaptable knowledge-based neural network modeling/design techniques; high efficiency Ka-band class F MMIC amplifiers; monolithic microwave integrated circuits; single-stage amplifier gain/PAE/output power; Gallium arsenide; Integrated circuit modeling; Integrated circuit technology; MMICs; Microwave amplifiers; Microwave integrated circuits; Monolithic integrated circuits; Neural networks; PHEMTs; Power amplifiers;
Conference_Titel :
Microwave Symposium Digest, 2003 IEEE MTT-S International
Conference_Location :
Philadelphia, PA, USA
Print_ISBN :
0-7803-7695-1
DOI :
10.1109/MWSYM.2003.1211014
