Abstract:
This work contains aim to propose effective and reliable I-V models with temperature
dependence to explain the characteristic of Gallium Nitride (GaN) High Electron
Mobility Transistor (HEMT). Machine learning (ML) algorithms which are most useful
in less time consumption by excluding multiple calculations were applied. Within
algorithms of ML such as Feed Forward Neural Networks, Radial Basis Neural Networks,
and Generalized Neural Networks developed by the MATLAB programming
tool. During the calculations of the given dataset, network training was conducted by
utilizing the Levenberg-Marquardt backpropagation algorithm. The number of hidden
layers and the neurons they contain, the initialization methods for the weights and
biases, and some other crucial parameters all play a significant role in determining the
accuracy and effectiveness of the model. However, in order to provide the validity of
the model, elapsed time and the capabilities of the proposed model are also assessed
via various learning algorithms. As performance evaluation criteria, the suggested network
model used means squared error (MSE), mean absolute error (MAE), Coefficient
of Determination R2, and regression analysis. Therefore, between all these proposed
neural networks’ architecture the comparison was analyzed to find out which of them
is well fitting to model the behavior of the GaN HEMT that is grown on Silicon. It
is predicted in several research works that the Artificial Neural Network ensures good
generalization performance under change of the parameters of GaN HEMT.