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A silicon nanocrystals based memory
Abstract
A new memory structure using threshold shifting from charge stored in nanocrystals of silicon (≊5nm in size) is described. The devices utilize direct tunneling and storage of electrons in the nanocrystals. The limited size and capacitance of the nanocrystals limit the numbers of stored electrons. Coulomb blockade effects may be important in these structures but are not necessary for their operation. The threshold shifts of 0.2–0.4 V with read and write times less than 100’s of a nanosecond at operating voltages below 2.5 V have been obtained experimentally. The retention times are measured in days and weeks, and the structures have been operated in an excess of 109 cycles without degradation in performance. This nanomemory exhibits characteristics necessary for high density and low power.
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Research works printed but not linked: 2
- 1980, IEEE Electron Device Lett., EDL-1, 179
- 1986, IEEE Trans. Electron Devices, ED-33, 1809
Cited by
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Surface Charge Sensitivity of Silicon Nanowires: Size Dependence
Elfström, N., Juhasz, R., Sychugov, I., Engfeldt, T., Karlström, A. E., & Linnros, J. (2007) · Nano Letters · doi:10.1021/nl0709017 · Cited by 275
Microcontact Printing of Ultrahigh Density Gold Nanoparticle Monolayer for Flexible Flash Memories
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Metadata
- Authors
- Tiwari, S., Rana, F., Hanafi, H., Hartstein, A., Crabbé, E. F., & Chan, K.
- Venue
- Applied Physics Letters
- Publisher
- AIP Publishing
- Published
- 1996-03-04
- Volume
- 68
- Issue
- 10
- Pages
- 1377-1379
- ISSN
- 0003-6951 (ISSN-L)
- 1077-3118
- OpenAlex
- W2021122851
- Cited by
- 1,629
- References
- 0
- FWCI
- 33.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tiwari, S., Rana, F., Hanafi, H., Hartstein, A., Crabbé, E. F., & Chan, K. (1996). A silicon nanocrystals based memory. Applied Physics Letters. https://doi.org/10.1063/1.116085
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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.