Аңдатпа
This paper develops and presents a CAD deployability of small-signal model of Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). First, a Gaussian Process Regression (GPR), a non-parametric probability-based method, is utilized to develop a small-signal model to effectively describe the behavior of the device. The model is developed to captures the bias and frequency dependence of GaN HEMT. The performance of the model is supplemented by advanced preprocessing and tuning of the hyperparameters of GPR approach. The tuning is done using 10-fold cross-validation error loss function. To examine the model's generalization ability the Mean squared error (MSE) metrics is used for both training and testing sets. Thereafter, the developed GPR based model is incorporated into CAD environment. Then the performance of the model is evaluated for stability and is also investigated for the usefulness of the model in class-F power amplifier design. The amplifier achieves excellent maximum available gain and small-signal voltage gain.