Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Cylindrical Silicon Nanowire Transistor Modeling Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

  • Received : 2012.10.27
  • Accepted : 2013.04.09
  • Published : 2013.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for modeling and simulation of DC characteristic of cylindrical Silicon Nanowire Transistor (SNWT). Device Geometry parameters, terminal voltages, temperature and output current were selected as the main factors of modeling. The results obtained are compared with numerical method and a good match has been observed between them, which represent accuracy of model. Finally, we imported the ANFIS model as a voltage controlled current source in a circuit simulator like HSPICE and simulated a SNWT inverter and common-source amplifier by this model.

Keywords

References

  1. Toshiro Hiramoto, Kousuke Miyaji and Masaharu Kobayashi, "Nanoscale Silicon Devices Using Nanostructure Physics for VLSI Applications", Institute of Industrial Science, University of Tokyo.
  2. Y. Taur and T. H. Ning, "Fundamentals of Modern VLSI Devices", Cambridge, UK: Cambridge University Press, 1998.
  3. J. Wang, "Device physics and simulation of silicon nanowire transistors", a preliminary report, Purdue University, 2004.
  4. J. Wang, E. Polizzi, and M. Lundstrom, "A computational study of ballistic silicon nanowire transistors" in IEDM Tech, pp, 29.5.1-29.5.4, 2003.
  5. Mohammad Taghi Ahmadi, Hui Houg Lau, Razali Ismail, Vijay K. Arora, "Current voltage characteristics of a silicon nanowire transistor", Microelectronic Journal, 2008.
  6. H. Sakaki, "Scattering suppression and high mobility effect of size quantized electron in ultrafin semiconductor wire structure", Jpn. J. Appl. Phys., Vol. 19, No. 12, pp. L735-L738, Dec. 1980. https://doi.org/10.1143/JJAP.19.L735
  7. Andrea Ghetti, GianPietro Carnevale and Denis Rideau, "Coupled Mechanical and 3-D Monte Carlo Simulation of Silicon Nanowire MOSFETs", IEEE Tranactions on nanotechnology, Vol. 6, No. 6, November 2007.
  8. Jinghong Chen, "A circuit-compatible analytical device model for ballistic nanowire transistors", Microelectronics Journal 39, 2008.
  9. Andrea Ghetti and Denis Rideaut, "3D Monte Carlo Device Simulation of NanoWire MOSFETs including Quantum Mechanical and Strain Effects", STMicroelectronics 2004.
  10. Mincheol Shin, "Three-dimensional quantum simulation of multigate nanowire field effect transistors", sciencedirect, 2007.
  11. http://www.nanohub.org/resources/Downloads/FETToy 2.0.
  12. A. Rahman, J. Guo, S. Datta, and M. Lundstrom, "Theory of Ballistic Nanotransistors", IEEE Trans. on Electron Device 50, 1853, 2003.
  13. J.-S. Roger Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993. https://doi.org/10.1109/21.256541
  14. J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy modeling and control", Proc. IEEE, Vol. 83, pp. 378-406, Mar. 1995. https://doi.org/10.1109/5.364486
  15. Xiao-Juan Wu, Xin-Jian Zhu, Guang-Yi Cao, Heng-Yong Tu, "Nonlinear modeling of a SOFC stack based on ANFIS identification", Simulation Modelling Practice and Theory, sciencedirect, 399-409, 2008.
  16. J.-S.R. Jang, "Neuro-Fuzzy and Soft Computing", Prentice-Hall, New Jersey (1997).
  17. "HSPICE Quick Reference Guide", Synopsys, Inc., 2003.
  18. Hanene Ben Hammouda, Mongia Mhiri, Zied Gafsi and Kamel Besbes, "Neural-Based Models of Semiconductor Devices for SPICE Simulator", American Journal of Applied Sciences 5 (4): 385-391, 2008. https://doi.org/10.3844/ajassp.2008.385.391
  19. Mohsen Hayati, Abbas Rezaei, Majid Seifi, Besbes, "CNT-MOSFET modeling based on artificial neural network: Application to simulation of nanoscale circuits", sciencedirect, 2009.