Аннотация:
The work reported in this article explores a novel Particle Swarm Optimization (PSO)
tuned Support Vector Regression (SVR) based technique to develop the small-signal behavioral model
for GaN High Electron Mobility Transistor (HEMT). The proposed technique investigates issues such
as kernel selection and model optimization usually encountered in the application of SVR to model the
GaN based HEMT devices. Here, the PSO algorithm is utilized to find the optimal hyperparameters to
minimize the fitness function. To enumerate the efficiency and the generalization capability of the predictors,
the performance of the model is investigated in terms of mean square error (MSE) and mean relative error
(MRE). A very good agreement is found between the measured S-parameters and the proposed model for
multi-biasing sets over the complete frequency range of 1 GHz-18 GHz. The proposed technique is even used
to test the frequency extrapolation capability of the model. A comparative analysis indicates that the proposed
PSO-SVR predictor achieves significantly improved computational efficiency and the overall prediction
accuracy. To demonstrate the ready usefulness of the modeling approach, the developed model has been
incorporated in CAD environment using MATLAB Cosimulation in ADS Ptolemy. Subsequently, the smallsignal stability analysis is performed and gain of a power amplifier configuration designed using the proposed
GaN HEMT model is determined.