Title :
Modeling carbon nanotube transistors using neural networks approach
Author :
Abo-Elhadeed, Ahmed F.
Author_Institution :
Mentor Graphics, Cairo, Egypt
Abstract :
A model for carbon nanotube field-effect transistors (CNTFETs) is developed using neural networks approach. This model accurately predicts the I-V characteristics for different structures of CNTFETs. The model is implemented inside the circuit simulator Eldo using its general user defined model (GUDM) template. To confirm the accuracy of the proposed model, the I-V characteristics are compared to device simulation results. The model is also validated using experimental data for both shottky barrier and conventional CNTFETs. The model shows excellent fitting for both the experimental and device simulation data with average percentage error doesn´t exceed 1%.
Keywords :
Schottky barriers; carbon nanotube field effect transistors; circuit simulation; neural nets; semiconductor device models; CNTFET structure; Eldo circuit simulator; GUDM template; I-V characteristics; Shottky barrier; average percentage error; carbon nanotube field-effect transistors; carbon nanotube transistor modelling; device simulation data; general user defined model; neural network approach; CNTFETs; Carbon nanotubes; Integrated circuit modeling; Mathematical model; Neural networks; Training; Shottky barrier; carbon nanotube; field effect transistor; modeling; neural networks;
Conference_Titel :
Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2012 International Conference on
Conference_Location :
Seville
Print_ISBN :
978-1-4673-0685-0
DOI :
10.1109/SMACD.2012.6339433
