Abstract
Abstract Machine learning‐based efficient temperature‐dependent small‐signal modelling approaches for GaN high electron mobility transistors (HEMTs) are presented by the authors here. The first method is an artificial neural network (ANN)‐based and makes use of the well‐known multilayer perceptron (MLP) architecture whereas the second technique is developed using support vector regression (SVR). The models are trained on a large set of measurement data obtained from a 2‐mm GaN‐on‐silicon device operating under varying operating conditions (bias voltages and ambient temperatures) over a wide frequency range of 0.1 to 20 GHz. An excellent agreement is found between the measured and the simulated S‐parameters for both models over the entire frequency range. It is identified that the training process and prediction capability of ANN is superior to SVR. However, the SVR is more robust when compared to the artificial neural network (ANN) in term of its sensitivity to local minima and uniqueness of the final solution. Subsequently, the performances of the proposed ANN‐ and SVR‐based models are improved by incorporating particle swarm optimization (PSO) in the model development process. The PSO improves the uniqueness of the ANN model whereas it enhances the performance of the SVR by optimising its control parameters. The proposed models exhibit very good accuracy and scalability.