A neural computation to study the scaling capability of the undoped DG MOSFET
The DG MOSFET is one of the most promising candidates for further CMOS
 scaling beyond the year of 2010. It will be scaled down to various degrees upon a wide
 range of system/circuit requirements (such as high-performance, low standby power and
 low operating power). The key...
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| Published in: | Semiconductor Physics Quantum Electronics & Optoelectronics |
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| Date: | 2008 |
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України
2008
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| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/118884 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | A neural computation to study the scaling capability of the undoped DG MOSFET / F. Djeffal, S. Guessasma, A. Benhaya, T. Bendib // Semiconductor Physics Quantum Electronics & Optoelectronics. — 2008. — Т. 11, № 2. — С. 196-202. — Бібліогр.: 11 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| Summary: | The DG MOSFET is one of the most promising candidates for further CMOS
scaling beyond the year of 2010. It will be scaled down to various degrees upon a wide
range of system/circuit requirements (such as high-performance, low standby power and
low operating power). The key electrical parameter of the DG MOSFET is the
subthreshold swing (S). In this paper, we present the applicability of the artificial neural
network for the study of the scaling capability of the undoped DG MOSFET. The latter is
based on the development of a semi-analytical model of the subthreshold swing (S) using
the Finite Elements Method (FEM). Our results are discussed in order to draw some
useful information about the ULSI technology.
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| ISSN: | 1560-8034 |