Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

• Main Author: Jonghwan Lee
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Electronics
• Subjects: physics-informed neural network; high frequency noise; quasi-ballistic MOSFETs; radial basis function-artificial neural network; tunneling current; thermal channel noise
• Online Access: https://www.mdpi.com/2079-9292/10/18/2219

A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.