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In this paper, we propose a new intelligent sensor based on Electrolyte-Gated Graphene Field-Effect Transistor (EGG-FET) and Artificial Neural Network (ANN)-based Corrector, for high performance and low cost pH monitoring applications. The EGG-FET behavior is investigated analytically using an analytical drain current model based on drift-diffusion carrier transport which is given as function of chemical,...
We modeled the electrostatic doping in multilayer graphene interconnects by self-consistently solving Poisson's equation and we computed the resistivity per layer by accounting for acoustic and optical phonon scattering. For the analysis, we used two different doping concentrations, representative for graphene on top of hexagonal Boron Nitride and SiO2 substrates. Finally, we benchmarked graphene...
In this work we present a simple model for computing the current-voltage characteristics of graphene based transistors (G-FETs). Our model is based on a simple treatment of electrostatics and a transport module including both tunneling and thermionic currents, properly capturing the effect of physical and electrical parameters. The predictive behaviour is demonstrated via comparison with experimental...
This paper presents a compact physics-based model of the drain current, charge, and capacitance of graphene field-effect transistors, which is of relevance for the exploration of dc, ac, and transient behavior of graphene-based circuits. The physical framework is a field-effect model and drift-diffusion carrier transport incorporating saturation velocity effects. First, an explicit model has been...
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