Bias-Dependent Intrinsic RF Thermal Noise Modeling and Characterization of Single-Layer Graphene FETs

In this article, the bias dependence of intrinsic channel thermal noise of single-layer (SL) 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, such as velocity saturation (VS) 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 2-D material’s underlying physics and including a bias dependence is presented. The implemented model is validated with deembedded high-frequency data from two short-channel devices at quasi-static (QS) 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 nondegenerate case and the model accurately captures this behavior. This work can also be of utmost significance from the 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.