DocumentCode :
597566
Title :
A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
Author :
Hari Krishna, S.V. ; Jianing An ; Lianxi Zheng
Author_Institution :
Sch. of Mech. & Aerosp. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2013
fDate :
2-4 Jan. 2013
Firstpage :
85
Lastpage :
88
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 :
carbon nanotube field effect transistors; electronic engineering computing; multilayer perceptrons; ANN; MLP architecture; SWCNT; artificial neural network approach; high mobility ultralong channel single walled carbon nanotube field-effect transistors; inversion region classification; long-channel CNTFET; millimetre long individual single walled carbon nanotubes; mobility curves; mobility data; multilayer perceptron; near-threshold region; sensing applications; subthreshold region; Artificial neural networks; CNTFETs; Carbon nanotubes; Chemicals; Logic gates; Nanobioscience; Sensors; artificial neural network; carbon nanotube; field-effect transistor; mobility; multi-layer perceptron;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nanoelectronics Conference (INEC), 2013 IEEE 5th International
Conference_Location :
Singapore
ISSN :
2159-3523
Print_ISBN :
978-1-4673-4840-9
Electronic_ISBN :
2159-3523
Type :
conf
DOI :
10.1109/INEC.2013.6465961
Filename :
6465961
Link To Document :
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