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Neural network model for ballistic carbon nanotube transistors

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3 Author(s)
Yousefi, R. ; Electr. Eng. Dept., Islamic Azad Univ., Tehran, Iran ; Saghafi, K. ; Moravvej-Farshi, M.K.

In this paper we present a neural network (NN) model for the ballistic carbon nanotube transistors. In comparison with the state of the art theoretical reference CNT model implemented in FETToy, our proposed model is a SPICE-compatible model and has a faster speed while maintaining the accuracy within less than 2% in terms of RMS error. The results show that, NN model has smaller RMS errors in calculated current under various conditions such as the oxide thickness, the nanotube diameter, gate-source voltage, the oxide permittivity and the source Fermi level, than the existing analytical models published by others.

Published in:

Nanoelectronics Conference (INEC), 2010 3rd International

Date of Conference:

3-8 Jan. 2010