Abstract

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.

Highlights

  • The continuous advances in CMOS fabrication processes and aggressive channel length reductions enable high frequency (HF) operation in the hundreds of GHz range of nanoscale MOSFETs [1,2]

  • The solid line represents the prediction by including the gate and drain shot noise sources of Equation (31), and the dashed line represents the prediction by ignoring the shot noise sources. This figure clearly shows the significant impact of the gate and drain shot noise on the noise parameters, and it demonstrates the good capability of the Artificial neural networks (ANNs)-based equivalent circuit model in predicting the noise performance

  • The physics-informed neural network model for the high frequency noise performance, which takes into account the gate and drain shot noise effects, is proposed and verified by measured noise data for quasi-ballistic MOSFETs

Read more

Summary

Introduction

The continuous advances in CMOS fabrication processes and aggressive channel length reductions enable high frequency (HF) operation in the hundreds of GHz range of nanoscale MOSFETs [1,2]. The HF noise sources in MOSFETs include the thermal drain current noise, due to the channel resistance, giving rise to the induced gate noise and the correlation noise [2,3,4,5,6,7]. As expected, the drain current noise of Equation (8) increases, while the induced gate noise of Equation (11) and the correlation noise of Equation (12) decrease owing to the cubic and quadratic dependence of Id, respectively This is consistent with other noise models and measured results [2,3,4,5,6,7]

Shot Noise Model
Artificial Neural Network Model
RBF-IANN
ANN Structure for Noise Performance
Model Verification
Findings
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.