A model for length of saturation velocity region in double-gate Graphene nanoribbon transistors

Ghadiry, Mahdiar; S., M. Nadi; Ahmadi, Mohammad Taghi; Manaf, Asrulnizam Abd

doi:10.1016/j.microrel.2011.07.009

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…A strip of graphene having thickness ( W ) of a few nanometres with much greater length ( L >> W ) as shown in Figure is called graphene nanoribbon (GNR). GNR has immensely attracted the interest of researchers since past 10 to 12 years due to its extraordinary electronic properties such as high mobility, high conductance, dimension‐dependent bandgap tunability, etc . The dimensions of GNR MOS transistors can be scaled down tremendously, which reduces the propagation delay and power consumption of the device .…”

Section: Device Structure and Fabricationmentioning

confidence: 99%

1.0–10.0 THz Radiation from Graphene Nanoribbon Based Avalanche Transit Time Sources

Acharyya

2019

Physica Status Solidi (a)

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The possibilities of terahertz frequency generation by using graphene nanoribbon (GNR) based avalanche transit time (ATT) sources are investigated in this paper. The most promising candidate of ATT device family, i.e., the impact avalanche transit time (IMPATT) diode is chosen for the present study. Parallel connected GNR based IMPATT structures with inherent power combining capability are proposed and simulated by using self-consistent quantum drift-diffusion model based in-house simulation codes in order to study the static, high frequency and noise performance of those at different millimeter-wave and terahertz frequency bands. The detailed study reveals that the in-build power combined GNR IMPATT sources are capable of performing more efficiently than the IMPATT sources based on some other popular semiconductor materials as well as some state-of-the-art terahertz radiators within the terahertz frequency band from 1 to 10 THz.

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Section: Device Structure and Fabricationmentioning

confidence: 99%

1.0–10.0 THz Radiation from Graphene Nanoribbon Based Avalanche Transit Time Sources

Acharyya

2019

Physica Status Solidi (a)

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“…Figure 7, shows a strong correlation among the simulation outcomes and samples used in various doping concentrations, with t = 5 nm and distances from the drain in double gate GNRFET. When the surface potential model was confirmed the particular LVSR model was also proven to be efficient since it is an ideal method of the surface potential Ψ 0, y V , [27]. Furthermore, the surface potential differs in the location of the channel for various peak doping concentrations.…”

Section: Resultsmentioning

confidence: 99%

“…1, whereby it is necessary to determine the boundary conditions as, Ψ 0,0 V andΨ 0, , L =V V , the built-in potential and the source-drain voltage are represented by V and V respectively. Since the electric flux across the top, down GNR and oxide boundary can be constant, the potential function has to satisfy [27].…”

Section: Research Methods the Proposed Model For Lvsr (Surface Potentimentioning

confidence: 99%

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Effect of Device Variables on Surface Potential and Threshold Voltage in DG-GNRFET

Mehrdel

Aziz

Ghadiri

2015

IJECE

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<p><em>In this paper we present four simple analytical threshold voltage model for short- channel and length of saturation velocity region (LVSR) effect that takes into account the built – in potential of the source and drain channel junction, the surface potential and the surface electric field effect on double – gate graphene nanoribbon transistors. Four established models for surface potential, lateral electric field, LVSR and threshold voltage are presented. These models are based on the easy analytical solution of the two dimensional potential distribution in the graphene and Poisson equation which can be used to obtain surface potential, lateral electric field, LVSR and threshold voltage. These models give a closed form solution of the surface potential and electrical field distribution as a function of structural parameters and drain bias. Most of analytical outcomes are shown to correlate with outcomes acquired by Matlab simulation and the end model applicability to the published silicon base devices is demonstrated.</em></p>

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“…They used analytical approach to calculate the breakdown voltage and ionization coefficient in double-gate GNRFET. In modelling, Gauss's law and Poisson's equation [ 6 - 8 ] were applied to derive surface potential equation and the lucky drift theory to calculate the ionization coefficient [ 6 ]. However, they did not take the effect of ionization coefficient into account for surface potential modelling, which makes their model inaccurate for GNR.…”

Section: Introductionmentioning

confidence: 99%

Graphene nanoribbon field-effect transistor at high bias

et al. 2014

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Combination of high-mean free path and scaling ability makes graphene nanoribbon (GNR) attractive for application of field-effect transistors and subject of intense research. Here, we study its behaviour at high bias near and after electrical breakdown. Theoretical modelling, Monte Carlo simulation, and experimental approaches are used to calculate net generation rate, ionization coefficient, current, and finally breakdown voltage (BV). It is seen that a typical GNR field-effect transistor's (GNRFET) breakdown voltage is in the range of 0.5 to 3 V for different channel lengths, and compared with silicon similar counterparts, it is less. Furthermore, the likely mechanism of breakdown is studied.

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A model for length of saturation velocity region in double-gate Graphene nanoribbon transistors

Cited by 16 publications

References 22 publications

1.0–10.0 THz Radiation from Graphene Nanoribbon Based Avalanche Transit Time Sources

1.0–10.0 THz Radiation from Graphene Nanoribbon Based Avalanche Transit Time Sources

Effect of Device Variables on Surface Potential and Threshold Voltage in DG-GNRFET

Graphene nanoribbon field-effect transistor at high bias

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