Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105010
Title: A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications
Authors: Hari Krishna, S. V.
An, Jianing
Zheng, Lianxi
Issue Date: 2013
Source: 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).
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%.
URI: https://hdl.handle.net/10356/105010
http://hdl.handle.net/10220/16823
DOI: 10.1109/INEC.2013.6465961
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Conference Papers

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