Fourier Feature Neural Networks (FFNN) for Modeling Terahertz Plasma Wave Oscillations in TeraFETs Using Hydrodynamic Charge Transport System
Conference
Hasan, MM, Pala, N. (2025). Fourier Feature Neural Networks (FFNN) for Modeling Terahertz Plasma Wave Oscillations in TeraFETs Using Hydrodynamic Charge Transport System
. SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI, 13460 10.1117/12.3053891
Hasan, MM, Pala, N. (2025). Fourier Feature Neural Networks (FFNN) for Modeling Terahertz Plasma Wave Oscillations in TeraFETs Using Hydrodynamic Charge Transport System
. SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI, 13460 10.1117/12.3053891
This work applies a neural network model integrated with Fourier Feature Networks (FFN) to accurately capture the terahertz frequency oscillations in plasma wave field-effect transistors (TeraFETs). Modeling high-frequency oscillations in these devices is challenging due to the complex dynamics of the hydrodynamic charge transport system. Our results show that the Fourier Feature Network effectively resolves the terahertz oscillations in the TeraFET channel, providing a better fit than standard neural networks. We used a numerical simulation dataset of Dyakonov-Shur instability in diamond TeraFET to train and test the model. Additionally, we compare the performance of this approach with Physics-Informed Neural Networks (PINNs), which were also tested with Fourier features. Despite this enhancement, the PINN struggled to accurately track the high-frequency solutions, exhibiting difficulties in both convergence and accuracy due to lack of dataset. This work demonstrates the effectiveness of utilizing Fourier features in neural networks for terahertz device modeling and highlights their advantages over PINNs in capturing rapid oscillations in TeraFETs. These findings offer valuable insights into improving the Artificial Intelligence (AI) based simulation of high-frequency THz devices.
