Neural network modeling of voltage-dependent resistance of metallic carbon nanotube interconnects: An ab initio study

Authors:
Serhan Yamacli;Mutlu Avci
Affiliations:
Mersin University, Department of Electronics and Computer Education, 33480 Tarsus Mersin, Turkey;Cukurova University, Department of Computer Engineering, 01136 Balcali, Adana, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
Cited 0

Carbon nanotubes in interconnect applications

Microelectronic Engineering
Performance analysis of carbon nanotube interconnects for VLSI applications

ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Neural network-based design approach for submicron MOS integrated circuits

Mathematics and Computers in Simulation
Luttinger liquid theory as a model of the gigahertz electrical properties of carbon nanotubes

IEEE Transactions on Nanotechnology
DFT Modeling of Bulk-Modulated Carbon Nanotube Field-Effect Transistors

IEEE Transactions on Nanotechnology

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this work, development voltage-dependent resistance models of metallic carbon nanotubes for computer aided design tools is aimed. Firstly, the resistance of metallic carbon nanotube interconnects are obtained from first principles simulations and the voltage dependence of the resistance is modeled through neural networks. Self-consistent non-equilibrium Green's function formalism combined with density functional theory is used for calculating the voltage-dependent resistance of metallic carbon nanotubes. It is shown that voltage dependent resistances of carbon nanotubes obtained from ab initio simulations can be accurately modeled via neural networks which enable rapid integration of carbon nanotube interconnect models into electronic design automation tools.