Bayesian inference-based small-signal modeling technique for GaN HEMTs

Abstract

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.