Abstract:
Machine learning‐based efficient temperature‐dependent small‐signal modelling ap proaches 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 isfound 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 itssensitivity
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