Accurate Modeling of GaN HEMTs Oriented to Analysis of Kink Effects in S₂₂ and h₂₁: An Effective Machine Learning Approach

Abstract

In this work, for the first time, a machine learning behavioral modeling methodology based on gate recurrent unit (GRU) is developed and used to model and then analyze the kink effects (KEs) in the output reflection coefficient $(S_{22})$ and the short-circuit current gain $(h_{21})$ of an advanced microwave transistor. The device under test (DUT) is a 0.25- $\mu \text{m}$ gallium nitride (GaN) high electron mobility transistor (HEMT) on silicon carbide (SiC) substrate, which has a large gate periphery of 1.5 mm. The scattering (S-) parameters of the DUT are measured at a frequency up to 65 GHz and at an ambient temperature up to 200°C. The proposed model can accurately reproduce the KEs in $S_{22}$ and in $h_{21}$ , enabling an effective analysis of their dependence on the operating conditions, bias point and ambient temperature. It is worth noticing that the proposed transistor model shows also good performance in both interpolation and extrapolation test.

Keywords

• GaN HEMT
• GRU
• kink effect
• machine learning methods
• semiconductor device modeling
• scattering parameter measurements