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
Summary
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]
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