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Ohmic and Schottky Contact CNTFET: Transport Properties and Device Performance Using Semi-classical and Quantum Particle Simulation

  • Huu-Nha Nguyen
  • Damien Querlioz
  • Arnaud Bournel
  • Sylvie Retailleau
  • Philippe DollfusEmail author
Chapter
Part of the Engineering Materials book series (ENG.MAT.)

Abstract

In this chapter, we investigate the device operation and performance of carbon nanotube-based field-effect transistors (CNTFETs) by means of particle Monte Carlo simulation including electron–phonon scattering. Within semi-classical approach of transport, both Ohmic- and Schottky-type source and drain contacts are considered and the effect of ambipolar transport inherent in Schottky barriers is analysed. This effect degrades the on/off current ratio and impedes the current saturation at high V DS, which makes the transition frequency strongly dependent on the drain voltage. However, by lowering the Schottky barrier height for electrons, the ambipolar behavior is reduced, which makes the device characteristics closer to that of ohmic-contact transistors. In both cases the intrinsic current-gain cutoff frequency is shown to reach about 800 GHz for a gate length of 100 nm. The detailed analysis of scattering events in the channel shows that the fraction of ballistic electrons in the Ohmic contacts devices increases from 80 to 95% when reducing the gate length from 100 to 10 nm. Hence, the transport in the channel is likely to be strongly coherent, which is analyzed by means of quantum Wigner Monte Carlo simulation, whose algorithm is fully compatible with the semi-classical one. It is shown that in spite of significant quantum transport effects such as source-drain tunneling through the gate-induced barrier and reflection by the sharp potential drop, the microscopic quantum features, e.g. the oscillations of the Wigner function, are not strongly reflected at the macroscopic level of terminal current.

Keywords

Monte Carlo Wigner Function Schottky Barrier Height Schottky Contact Gate Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partially supported by the European Community, through Network of Excellence NANOSIL (ICT-216171), and by the French ANR, through project ACCENT (ANR-06-NANO-069).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Huu-Nha Nguyen
    • 1
  • Damien Querlioz
    • 1
  • Arnaud Bournel
    • 1
  • Sylvie Retailleau
    • 1
  • Philippe Dollfus
    • 1
    Email author
  1. 1.Institut d’Electronique FondamentaleCNRS, Univ. Paris-Sud, UMR 8622OrsayFrance

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