A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications

A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications

Millimetre long individual single walled carbon nanotubes (SWCNTs) were consistently grown and fabricated into carbon nanotube field effect transistors (CNTFETs). In this work, we extracted the effective mobilities in the strong inversion region, near-threshold region and subthreshold region respect...

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Main Authors: Hari Krishna, S. V., An, Jianing, Zheng, Lianxi
Other Authors: School of Mechanical and Aerospace Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/105010
http://hdl.handle.net/10220/16823
http://dx.doi.org/10.1109/INEC.2013.6465961
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1050102019-12-06T21:44:28Z A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications Hari Krishna, S. V. An, Jianing Zheng, Lianxi School of Mechanical and Aerospace Engineering International Nanoelectronics Conference (5th : 2013 : Singapore) Millimetre long individual single walled carbon nanotubes (SWCNTs) were consistently grown and fabricated into carbon nanotube field effect transistors (CNTFETs). In this work, we extracted the effective mobilities in the strong inversion region, near-threshold region and subthreshold region respectively for these long-channel CNTFETs. Using the mobility data as an input parameter, an artificial neural network (ANN) employing multi-layer perceptron (MLP) architecture was used to classify the different inversion regions of the mobility curves with an accuracy of 90%. 2013-10-24T08:13:17Z 2019-12-06T21:44:28Z 2013-10-24T08:13:17Z 2019-12-06T21:44:28Z 2013 2013 Conference Paper Hari Krishna, S. V., An, J., & Zheng, L. (2013).A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications. 2013 IEEE 5th International Nanoelectronics Conference (INEC). https://hdl.handle.net/10356/105010 http://hdl.handle.net/10220/16823 http://dx.doi.org/10.1109/INEC.2013.6465961 en
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description Millimetre long individual single walled carbon nanotubes (SWCNTs) were consistently grown and fabricated into carbon nanotube field effect transistors (CNTFETs). In this work, we extracted the effective mobilities in the strong inversion region, near-threshold region and subthreshold region respectively for these long-channel CNTFETs. Using the mobility data as an input parameter, an artificial neural network (ANN) employing multi-layer perceptron (MLP) architecture was used to classify the different inversion regions of the mobility curves with an accuracy of 90%.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Hari Krishna, S. V.
An, Jianing
Zheng, Lianxi
format Conference or Workshop Item
author Hari Krishna, S. V.
An, Jianing
Zheng, Lianxi
spellingShingle Hari Krishna, S. V.
An, Jianing
Zheng, Lianxi
A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
author_sort Hari Krishna, S. V.
title A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
title_short A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
title_full A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
title_fullStr A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
title_full_unstemmed A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
title_sort neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
publishDate 2013
url https://hdl.handle.net/10356/105010
http://hdl.handle.net/10220/16823
http://dx.doi.org/10.1109/INEC.2013.6465961
_version_ 1681042269464952832

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