Neural network model for ballistic carbon nanotube transistors

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.