Premium
Bayesian inference‐based small‐signal modeling technique for GaN HEMTs
Author(s) -
Cai Jialin,
King Justin,
Yu Chao,
Sun Lingling
Publication year - 2018
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.21509
Subject(s) - high electron mobility transistor , overfitting , computer science , bayesian inference , artificial neural network , gallium nitride , algorithm , artificial intelligence , electronic engineering , topology (electrical circuits) , equivalent circuit , machine learning , transistor , bayesian probability , materials science , engineering , layer (electronics) , electrical engineering , voltage , composite material
A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I – V curves and S ‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I – V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I – V curves.