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

S. V. Hari Krishna; Jianing An; Lianxi Zheng

doi:10.1109/INEC.2013.6465961

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

S. V. Hari Krishna, Jianing An, Lianxi Zheng

Mechanical & Nuclear Engineering

School of Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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%.

Original language	British English
Title of host publication	Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013
Pages	85-88
Number of pages	4
DOIs	https://doi.org/10.1109/INEC.2013.6465961
State	Published - 2013
Event	2013 IEEE 5th International Nanoelectronics Conference, INEC 2013 - Singapore, Singapore Duration: 2 Jan 2013 → 4 Jan 2013

Publication series

Name	Proceedings - Winter Simulation Conference
ISSN (Print)	0891-7736

Conference

Conference	2013 IEEE 5th International Nanoelectronics Conference, INEC 2013
Country/Territory	Singapore
City	Singapore
Period	2/01/13 → 4/01/13

Keywords

artificial neural network
carbon nanotube
field-effect transistor
mobility
multi-layer perceptron

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10.1109/INEC.2013.6465961

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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. In Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013 (pp. 85-88). Article 6465961 (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/INEC.2013.6465961

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. Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013. 2013. pp. 85-88 (Proceedings - Winter Simulation Conference).

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

abstract = "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%.",

keywords = "artificial neural network, carbon nanotube, field-effect transistor, mobility, multi-layer perceptron",

author = "{Hari Krishna}, {S. V.} and Jianing An and Lianxi Zheng",

year = "2013",

doi = "10.1109/INEC.2013.6465961",

language = "British English",

isbn = "9781467348416",

series = "Proceedings - Winter Simulation Conference",

pages = "85--88",

booktitle = "Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013",

note = "2013 IEEE 5th International Nanoelectronics Conference, INEC 2013 ; Conference date: 02-01-2013 Through 04-01-2013",

}

Hari Krishna, SV, 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. in Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013., 6465961, Proceedings - Winter Simulation Conference, pp. 85-88, 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013, Singapore, Singapore, 2/01/13. https://doi.org/10.1109/INEC.2013.6465961

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.
Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013. 2013. p. 85-88 6465961 (Proceedings - Winter Simulation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications

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Y1 - 2013

N2 - 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%.

AB - 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%.

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KW - carbon nanotube

KW - field-effect transistor

KW - mobility

KW - multi-layer perceptron

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M3 - Conference contribution

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BT - Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013

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Hari Krishna SV, An J, Zheng L. A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications. In Proceedings of the 2013 IEEE 5th International Nanoelectronics Conference, INEC 2013. 2013. p. 85-88. 6465961. (Proceedings - Winter Simulation Conference). doi: 10.1109/INEC.2013.6465961

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

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