Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks

Subject Areas : International Journal of Smart Electrical Engineering

Roya  Abdollahzadeh Badelbo ¹ (Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.)
Fardad  Farokhi ² (Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.)
Alireza  Kashaniniya ³ (Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.)

Received: 2015-04-15 Accepted : 2015-04-15 Published : 2013-09-01

Keywords:

References:

[1]

M. Hayati, A. Rezaei and M. Seifi, “CNT-MOSFET Modelling Based on Artificial Neural Network: Application to Simulation of Nanoscale circuits”, Solid-State Electronics, Vol.54, No.1, pp.52–57, Oct 2010.

[2]

M. Fakhrabadi, M. Samadzadeh, A. Rastgoo, M. Yazdi and M. Mashhadi, “Vibrational Analysis of Carbon Nanotubes Using Molecular Mechanics and Artificial neural network”, Physica E, Vol.44, pp.565–578, Oct 2011.

[3]

S. Datta, “Nanoscale Device Modelling: the green’s function method”, Super lattices Microstruct, Vol.28, No.4, pp.253–278, 2000.

[4]

T.J. Kazmierski, D. Zhou, BM. Al-Hashimi, “Efficient Circuit-level Modelling of Ballistic CNT Using Piecewise Non-linear Approximation of Mobile Charge Density”, IEEE int. Conf. Design, Automation, Test, Munich, Europe, pp.146–51, Mar 2008.

[5]

A. Abdollahi-Nohoji, F. Farokhi, M. Zamani, “Performance Comparison of Artificial Intelligence Networks in Nanoscale MOSFET Modelling”, IEEE int. Conf. Natural Computation (ICNC), 26-28, PP.807 – 810, Jul 2011.

[6]

F. Djeffal, Z. Dibi, M.L. Hafiane and D. Arar, “Design and Simulation of a Nanoelectronic DGMOSFET Current Source Using Artificial Neural Networks”, Materials Science and Engineering, Vol.27, pp.1111–1116, 2007.

[7]

A. Abdollahi-Nohoji, F. Farokhi, M. Shokouhifar, M. Zamani, “Efficent parameters selection for artificial Intelligence Models of Nanoscale MOSFETs”, IEEE int. Conf. Electrical and Computer Engineering (CCECE), Niagara Falls, Canada, Vol.24, pp.840–844, May 2011.

[8]

R. Yousefi and M. Shabani, “A Model for Carbon Nanotube FETs in the Ballistic Limit”, Microelectronics Journal, Vol.42, No.11, pp.1299–1304, Sep 2011.

[9]

FETToy/matlab/CNTFET, FETToy-1.0, 2012. http:// www.nanohub.org/ resources/downloads.

[10]

J.L. HOFFA, “Simulation of Carbon Nanotube Based Field Effect Transistors”, MSc, Thesis, Research and Advanced Studies of the University of Cincinnati, 2007.

[11]

S. Haykin, Neural networks: A Comprehensive foundation, NJ: Prentice-Hall, 1999.

[12]

M.F. Redondo, CH. Espinosa, “A comparison among Feature Selection Methods Based On Trained Networks”, Proc. IEEE Int. Neural Network for Signal Processing, Madison, WI, USA, pp.205-214, Aug 1999.

[13]

S.J. Tans, A.M. Verschueren and C. Dekker, “Room-Temperature Transistor Based on a Single Carbon Nanotube”, Nature, Vol.393, pp.49-52, 1998.