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A Surface Potential Based Model for Dual Gate Bilayer Graphene Field Effect Transistor Including the Capacitive Effects

DOI:10.1142/S0218126619502414 期刊:Journal of Circuits, Systems and Computers 出版年份:2019 更新时间:2025-09-23 15:22:29
摘要: In this work, a surface potential modeling approach has been proposed to model dual gate, bilayer graphene field effect transistor. The equivalent capacitive network of GFET has been improved considering the quantum capacitance effect for each layer and inter-layer capacitances. Surface potentials of both layers are determined analytically from equivalent capacitive network. The explicit expression of drain to source current is established from drift-diffusion transport mechanism using the surface potentials of the layers. The drain current characteristics and transfer characteristics of the developed model shows good agreement with the experimental results in literatures. The small signal parameters of intrinsic graphene transistor i.e. output conductance (gds), trans-conductance (gm), gate to drain capacitance (Cgd) and gate to source capacitance (Cgs) have been derived and finally, the cut-off frequency is determined for the developed model. The model is compared with reported experimental data using Normalised Root Mean Square Error (NRMSE) metric and it shows less than 16% NRMSE. A Verilog-A code has been developed for this model and a single ended frequency doubler has been designed in Cadence design environment using this Verilog-A model.
作者: Sudipta Bardhan,Manodipan Sahoo,Hafizur Rahaman
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To develop a surface potential based model for dual gate bilayer graphene field effect transistor including capacitive effects, such as quantum capacitances and inter-layer capacitances, to accurately simulate its electrical characteristics and validate with experimental data.

The developed GFET model accurately captures capacitive effects and shows good agreement with experimental data, with NRMSE less than 16%. It enables circuit design applications, such as frequency doublers, and provides a foundation for further optimization in graphene-based electronics.

The model may have limitations in accounting for all parasitic effects and variations in material properties; the NRMSE error is less than 16%, indicating room for improvement. Application is focused on specific GFET structures and may not generalize to other configurations.

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