Efficient Parameter Extraction for GaN HEMTs Using Rational Function Assisted Neural Network

This work proposes a novel method integrating Rational Function Preprocessing (RFP) with Artificial Neural Network (ANN) to efficiently extract ASM-HEMT model parameters from S-parameters of Gallium Nitride High Electron Mobility Transistor (GaN HEMT) devices. Compared to conventional ANN parameter extractors, this method represents a significant advancement in the field. The innovation lies in developing an advanced rational function model specifically designed to accurately capture the complex high-dimensional frequency characteristics of GaN HEMT S-parameters. By effectively preserving the essential frequency response characteristics, it successfully eliminates interference from redundant S-parameters. Experimental results highlight substantial performance improvements, with training time reduced from 9035 seconds to 670 seconds under comparable error conditions, achieving a 92.6% reduction. The Root Mean Square Percentage Error (RMSPE) for parameter extraction decreases from 7.55% to 4.13%, representing a 45% optimization in accuracy under the same neural network architecture. This method holds significant engineering as 5G base stations and Rfpower amplifiers.