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Novel neural approach for parameter extraction of microwave transistor noise models
Author(s) -
Marinković Zlatica,
Ivković Nenad,
PronićRančić Olivera,
Marković Vera,
Caddemi Alina
Publication year - 2015
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2083
Subject(s) - microwave , extraction (chemistry) , transistor , noise (video) , electronic engineering , artificial neural network , computer science , electrical engineering , artificial intelligence , engineering , telecommunications , chemistry , chromatography , voltage , image (mathematics)
A novel approach for parameter extraction of microwave transistor noise models based on artificial neural networks is proposed in this work. Neural networks are applied to determine parameters of the noise model directly from the measured noise and small‐signal scattering parameters without any optimization procedure. Moreover, unlike the similar existing procedures, development of the extraction procedure does not require any measured data or optimizations in a circuit simulator, making the procedure more efficient, as described in detail in the paper. The approach has been applied to extraction of the Pospieszalski's noise model parameters for a specific pseudomorphic high‐electron‐mobility transistor (pHEMT) device working under different temperatures. Copyright © 2015 John Wiley & Sons, Ltd.