A new approach to study carrier generation in graphene nanoribbons under lateral bias

This paper presents a new approach to study the effect of carrier generation on the current of graphene nanoribbon field effect transistors, which is normally ignored in current-voltage (I-V) modelling. Two analytical models together with a Monte Carlo approach including the edge effect scattering a...

Full description

Saved in:
Bibliographic Details
Main Authors: Ahmad, Harith, Ghadiry, Mahdiar, Abd Manaf, Asrulnizam
Format: Article
Published: American Scientific Publishers 2016
Subjects:
Online Access:http://eprints.um.edu.my/18100/
http://dx.doi.org/10.1166/mex.2016.1305
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.eprints.18100
record_format eprints
spelling my.um.eprints.181002019-12-18T08:05:46Z http://eprints.um.edu.my/18100/ A new approach to study carrier generation in graphene nanoribbons under lateral bias Ahmad, Harith Ghadiry, Mahdiar Abd Manaf, Asrulnizam QC Physics This paper presents a new approach to study the effect of carrier generation on the current of graphene nanoribbon field effect transistors, which is normally ignored in current-voltage (I-V) modelling. Two analytical models together with a Monte Carlo approach including the edge effect scattering are used to calculate the current and net generation rate respectively. In addition, two fabricated devices with gate lengths of 20 and 30 nm are employed for verification. We showed that ignoring this effect in the modelling, results in error up to 10% for a typical 30 nm graphene field effect transistor. This approach is useful in modelling and optimization of graphene-based field effect transistors and photo sensors. American Scientific Publishers 2016 Article PeerReviewed Ahmad, Harith and Ghadiry, Mahdiar and Abd Manaf, Asrulnizam (2016) A new approach to study carrier generation in graphene nanoribbons under lateral bias. Materials Express, 6 (3). pp. 283-288. ISSN 2158-5849 http://dx.doi.org/10.1166/mex.2016.1305 doi:10.1166/mex.2016.1305
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QC Physics
spellingShingle QC Physics
Ahmad, Harith
Ghadiry, Mahdiar
Abd Manaf, Asrulnizam
A new approach to study carrier generation in graphene nanoribbons under lateral bias
description This paper presents a new approach to study the effect of carrier generation on the current of graphene nanoribbon field effect transistors, which is normally ignored in current-voltage (I-V) modelling. Two analytical models together with a Monte Carlo approach including the edge effect scattering are used to calculate the current and net generation rate respectively. In addition, two fabricated devices with gate lengths of 20 and 30 nm are employed for verification. We showed that ignoring this effect in the modelling, results in error up to 10% for a typical 30 nm graphene field effect transistor. This approach is useful in modelling and optimization of graphene-based field effect transistors and photo sensors.
format Article
author Ahmad, Harith
Ghadiry, Mahdiar
Abd Manaf, Asrulnizam
author_facet Ahmad, Harith
Ghadiry, Mahdiar
Abd Manaf, Asrulnizam
author_sort Ahmad, Harith
title A new approach to study carrier generation in graphene nanoribbons under lateral bias
title_short A new approach to study carrier generation in graphene nanoribbons under lateral bias
title_full A new approach to study carrier generation in graphene nanoribbons under lateral bias
title_fullStr A new approach to study carrier generation in graphene nanoribbons under lateral bias
title_full_unstemmed A new approach to study carrier generation in graphene nanoribbons under lateral bias
title_sort new approach to study carrier generation in graphene nanoribbons under lateral bias
publisher American Scientific Publishers
publishDate 2016
url http://eprints.um.edu.my/18100/
http://dx.doi.org/10.1166/mex.2016.1305
_version_ 1654960679521615872
score 13.160551