Bias-dependent intrinsic RF thermal noise modeling and characterization of single layer graphene FETs

TL;DR

This paper develops a physics-based compact model for the bias-dependent intrinsic RF thermal noise in short-channel graphene FETs, validated with experimental data, and highlights the impact of graphene's degenerate nature on noise behavior.

Contribution

It introduces a novel, physics-based compact noise model for short-channel graphene FETs that accounts for bias dependence and graphene's degenerate physics, validated without fitting parameters.

Findings

Noise increases with drain current but saturates at high carrier densities.

Short-channel effects like velocity saturation increase noise at high electric fields.

Degenerate graphene reduces noise significantly compared to non-degenerate models.

Abstract

In this article, the bias-dependence of intrinsic channel thermal noise of single-layer graphene field-effect transistors (GFETs) is thoroughly investigated by experimental observations and compact modeling. The findings indicate an increase of the specific noise as drain current increases whereas a saturation trend is observed at very high carrier density regime. Besides, short-channel effects like velocity saturation also result in an increment of noise at higher electric fields. The main goal of this work is to propose a physics-based compact model that accounts for and accurately predicts the above experimental observations in short-channel GFETs. In contrast to long-channel MOSFET based models adopted previously to describe thermal noise in graphene devices without considering the degenerate nature of graphene, in this work a model for short-channel GFETs embracing the 2D materials underlying physics and including a bias dependency is presented. The implemented model is validated with de-embedded high frequency data from two short-channel devices at Quasi-Static region of operation. The model precisely describes the experimental data for a wide range of low to high drain current values without the need of any fitting parameter. Moreover, the consideration of the degenerate nature of graphene reveals a significant decrease of noise in comparison with the non degenerate case and the model accurately captures this behavior. This work can also be of outmost significance from circuit designers aspect, since noise excess factor, a very important figure of merit for RF circuits implementation, is defined and characterized for the first time in graphene transistors.