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Monte Carlo simulation of a 30 nm dual-gate MOSFET: how short can Si go?
Abstract
Monte Carlo simulation is used to explore the characteristics of an n-channel MOSFET at the presently perceived limits of scaling. This dual-gated 30 nm gate-length FET is found to have excellent characteristics for use in digital logic, including a transconductance as high as 2300 mS/mm and an estimated ring-oscillator delay of 1.1 ps. The various motivations for this device design are discussed, illuminating the reasons for claiming that it is at the limits of scaling.>
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Citations
Cited by
Bez, R., Camerlenghi, E., Modelli, A., & Visconti, A. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.811702 · Cited by 836
Low power microelectronics: retrospect and prospect
Meindl, J. D. (1995) · Proceedings of the IEEE · doi:10.1109/5.371970 · Cited by 316
Ultimately thin double-gate SOI MOSFETs
Ernst, T., Cristoloveanu, S., Ghibaudo, G., Ouisse, T., Horiguchi, S., Ono, Y., Takahashi, Y., & Murase, K. (2003) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2003.811371 · Cited by 245
Metadata
- Authors
- Frank, Laux, & Fischetti
- Venue
- International Technical Digest on Electron Devices Meeting
- Publisher
- IEEE
- Published
- 1992
- Pages
- 553-556
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2537621164
- Cited by
- 268
- References
- 0
- FWCI
- 23.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Monte Carlo simulation of a 30 nm dual-gate MOSFET: how short can Si go? https://app.cochat.ai/papers/10.1109/iedm.1992.307422
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- Frank, Laux, & Fischetti. (1992). Monte Carlo simulation of a 30 nm dual-gate MOSFET: how short can Si go? International Technical Digest on Electron Devices Meeting. https://doi.org/10.1109/iedm.1992.307422
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
