Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks

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.