Abstract
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 1GHz-18GHz. 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 small-signal stability analysis is performed and gain of a power amplifier configuration designed using the proposed GaN HEMT model is determined.
Highlights
G ALLIUM NITRIDE (GaN) High Electron Mobility Transistor (HEMT) is getting very popular in the design of circuits and components such as RF Power Amplifier (PA) owing to its unique traits like wide energy bandgap, high breakdown field, and high electron mobility [1]-[7]
A key feature of machine learning (ML) is its ability to predict the outcome in real-time very quickly and this is very appealing for device modeling especially at RF and microwave frequencies where the inter-dependence of various device parameters on each other is huge [24]-[25]
In this paper, a novel Particle Swarm Optimization (PSO)-Support Vector Regression (SVR) based technique is presented to model the small-signal behavior of GaN HEMT device
Summary
G ALLIUM NITRIDE (GaN) HEMT is getting very popular in the design of circuits and components such as RF Power Amplifier (PA) owing to its unique traits like wide energy bandgap, high breakdown field, and high electron mobility [1]-[7]. A number of GaN HEMT small-signal modeling techniques exist and many of these make use of analytical formulations, cold pinch-off concept, and de-embedding [11]–[21] These conventional methods, accurate, are often found to be highly cumbersome and computationally inefficient. Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been used in the development of small-signal model for GaN HEMT [27][34]. Keeping the above issues in perspective, a novel technique making use of Particle Swarm Optimization (PSO) and SVR is developed to model behavior of GaN HEMT under small-signal conditions. The main contributions of this paper are: (a) development of a novel PSO-SVR based small signal behavioral model for GaN HEMT device, (b) validation of the prediction ability of the model by frequency and geometric extrapolation, (c) robustness evaluation of the proposed model by mixing random. It leads to expression (6) and which needs to satisfy the complementarily Karush-Kuhn Tucker (KKT) conditions expressed by equations 7(a)-(c) [39]-[41]
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