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A Novel ANN-PSO Method for Optimizing a Small-Signal Equivalent Model of a Dual-Field-Plate GaN HEMT.

Haowen Shen1, Wenyong Zhou2, Jinye Wang1

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Summary

This study presents a new method combining artificial neural networks (ANNs) and Particle Swarm Optimization (PSO) for efficient parameter optimization of Gallium Nitride High Electron Mobility Transistor (GaN HEMT) devices. The ANN-PSO approach improves automation and accuracy in device modeling.

Keywords:
ANNGaN HEMTPSOequivalent circuit modelingparameter optimization

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Area of Science:

  • Semiconductor Device Physics
  • Artificial Intelligence in Electronics
  • Computational Electromagnetics

Background:

  • Accurate small-signal equivalent models are crucial for designing Gallium Nitride High Electron Mobility Transistor (GaN HEMT) devices.
  • Traditional parameter optimization methods can be time-consuming and lack precision.
  • Dual-field-plate GaN HEMTs present unique modeling challenges due to their complex structure.

Purpose of the Study:

  • To develop and validate a novel, efficient, and precise method for optimizing the small-signal equivalent model parameters of dual-field-plate GaN HEMT devices.
  • To integrate artificial neural networks (ANNs) with the Particle Swarm Optimization (PSO) algorithm for enhanced parameter extraction.
  • To compare the performance of the proposed ANN-PSO method against other optimization algorithms like NSGA2 and DE.

Main Methods:

  • An artificial neural network (ANN) model was developed to predict S-parameters of the GaN HEMT device.
  • The Particle Swarm Optimization (PSO) algorithm was employed to optimize the parameters of the small-signal equivalent circuit model.
  • The ANN-PSO method was applied to a 4 × 250 μm dual-field-plate GaN HEMT model over a 1-18 GHz frequency range.
  • Performance was validated against traditional physical formula analysis under various bias conditions.

Main Results:

  • The PSO algorithm demonstrated superior convergence speed and accuracy compared to NSGA2 and DE algorithms.
  • The ANN-PSO approach achieved automated and efficient optimization of the GaN HEMT equivalent circuit model parameters.
  • The optimized model showed high accuracy, validated under varying bias conditions.
  • The method proved effective in enhancing automation and efficiency while maintaining model precision.

Conclusions:

  • The integrated ANN-PSO method offers a significant advancement in the automated parameter optimization of GaN HEMT device models.
  • This approach provides a reliable and efficient reference for optimizing other complex semiconductor device models.
  • The study highlights the potential of combining machine learning with metaheuristic optimization for device modeling and design.