Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs

Abstract

This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.