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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kazmierski, T.J. Dafeng Zhou Al-Hashimi, B.M. |
| Copyright Year | 2007 |
| Description | Author affiliation: Sch. of Electron. & Comput. Sci., Southampton Univ., Southampton (Kazmierski, T.J.; Dafeng Zhou; Al-Hashimi, B.M.) |
| Abstract | Recently proposed circuit-level models of carbon nanotube transistor (CNT) for SPICE-like simulators suffer from numerical complexities as they rely on numerical evaluation of integrals or internal Newton-Raphson iterations to find solutions of non-linear dependencies or both. Recently an approach has been proposed which eliminates the need for numerical integration when calculating the charge densities in CNT through the use of piece-wise linear approximation. This paper extends the effective employment of numerical piece-wise approximation to the solution of the Fermi-Dirac integral to accelerate the CNT model speed when evaluating the source-drain current while maintaining high modeling accuracy. Our results show a speed up of more than three orders of magnitude compared with the theoretical CNT model implemented in FETToy, used as a reference for verifying newer models. Comparisons of drain-source current characteristics of the new model against that in FETToy are presented, confirming that the accuracy of the proposed approach is maintained within less than 5% in terms of RMS error. |
| Starting Page | 33 |
| Ending Page | 37 |
| File Size | 192744 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424417902 |
| DOI | 10.1109/NANOARCH.2007.4400855 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-21 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Piecewise linear techniques Neodymium Circuit simulation Integral equations Voltage Carbon nanotubes Energy states Numerical models Mathematical model Transistors |
| Content Type | Text |
| Resource Type | Article |
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