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Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices
Author(s) -
Jarndal Anwar
Publication year - 2013
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.20660
Subject(s) - high electron mobility transistor , intermodulation , gallium nitride , optoelectronics , artificial neural network , materials science , harmonics , microwave , transistor , electronic engineering , signal (programming language) , computer science , substrate (aquarium) , genetic algorithm , large signal model , power (physics) , wafer , algorithm , physics , electrical engineering , engineering , layer (electronics) , amplifier , voltage , telecommunications , nanotechnology , artificial intelligence , quantum mechanics , machine learning , programming language , geology , cmos , oceanography
An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.