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

Yamacli S., AVCI M.

EXPERT SYSTEMS WITH APPLICATIONS, vol.37, no.12, pp.8014-8018, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 12
  • Publication Date: 2010
  • Doi Number: 10.1016/j.eswa.2010.05.089
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.8014-8018
  • Çukurova University Affiliated: Yes

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. (C) 2010 Elsevier Ltd. All rights reserved.