Compact model for switching characteristics of graphene field effect transistor

Sreenath, R; Sundari, B. Bala Tripura

doi:10.1063/1.4945133

Cited by 5 publications

(1 citation statement)

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“…Stacks of graphene built of monolayer and bilayer graphene were modelled as R-L-C circuits to study the variation of the input impedance, absorbance, reflection coefficient and absorption frequency with respect to the variation of thickness of the dielectric layer [26]. Various compact models and computation of quantum capacitances has been carried out to simulate circuits of graphene MOSFETs [27]- [29]. Gate all around MOSFET circuits of numerical models developed and been validated with ATLAS TCAD simulation for implementing the device as an inverter circuit and gain value is also estimated [30].…”

Section: Introduction-graphene Modeling and Applicationsmentioning

confidence: 99%

A Novel Graphene Modelling: Bottom up Approach - Band Gap Studies to Transport in Python

Bhardwaj

2021

Preprint

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In this work we develop a computational, quantum level monolayer graphene nanoribbon (GNR) MOSFET of channel length of 10 and 20 nm, with a width of 2 nm and contacts of 2nm width is attached. To develop the MOSFET channel, a bottom up approach is adopted by developing the material model. First the material models of graphene nanoribbon is developed using pybinding module tool in python. The material models of monolayer, bilayer graphene nanoribbon are built on the principles of tight binding module. The methodology developed is based on the Hamiltonian matrix formulation that has been used to determine the E-k plots and LDOS plots of graphene monolayer, bilayer graphene nano ribbon. The GNR MOSFET that is structurally built in python is used to simulate graphene as a switch. Its band gap characteristics is presented as its performance as a switch and is verified with relevant work. Then GNR MOSFET is modelled using quantum principles of NEGF and greens function to determine the transmission characteristics and the I-V characteristics for channel lengths of 10 nm and 20 nm.

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Section: Introduction-graphene Modeling and Applicationsmentioning

confidence: 99%