Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks

Authors

Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

Abstract

In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for ANNs were obtained by numerical ballistic FETToy model which is not directly applicable in circuit simulators like HSPICE. The ANN models were simulated in MATLAB R2010a software. In order to achieve more effective and consistent features, the UTA method was used and the overall performance of the models was tested in MATLAB. Finally the fast and accurate structure was introduced as a sub circuit for implementation in HSPICE simulator and then the implemented model was used to simulate a current source and an inverter circuit. Results indicate that the proposed ANN model is suitable for nanoscale circuits to be used in simulators like HSPICE.

Keywords


[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.