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Nanoscale memory cell based on a nanoelectromechanical switched capacitor.

Abstract:
The demand for increased information storage densities has pushed silicon technology to its limits and led to a focus on research on novel materials and device structures, such as magnetoresistive random access memory and carbon nanotube field-effect transistors, for ultra-large-scale integrated memory. Electromechanical devices are suitable for memory applications because of their excellent 'ON-OFF' ratios and fast switching characteristics, but they involve larger cells and more complex fabrication processes than silicon-based arrangements. Nanoelectromechanical devices based on carbon nanotubes have been reported previously, but it is still not possible to control the number and spatial location of nanotubes over large areas with the precision needed for the production of integrated circuits. Here we report a novel nanoelectromechanical switched capacitor structure based on vertically aligned multiwalled carbon nanotubes in which the mechanical movement of a nanotube relative to a carbon nanotube based capacitor defines 'ON' and 'OFF' states. The carbon nanotubes are grown with controlled dimensions at pre-defined locations on a silicon substrate in a process that could be made compatible with existing silicon technology, and the vertical orientation allows for a significant decrease in cell area over conventional devices. We have written data to the structure and it should be possible to read data with standard dynamic random access memory sensing circuitry. Simulations suggest that the use of high-k dielectrics in the capacitors will increase the capacitance to the levels needed for dynamic random access memory applications.
Publication status:
Published

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Publisher copy:
10.1038/nnano.2007.417

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


Journal:
Nature nanotechnology More from this journal
Volume:
3
Issue:
1
Pages:
26-30
Publication date:
2008-01-01
DOI:
EISSN:
1748-3395
ISSN:
1748-3387


Language:
English
Keywords:
Pubs id:
pubs:332134
UUID:
uuid:01680451-1ee9-49ef-a621-2edec18f8475
Local pid:
pubs:332134
Source identifiers:
332134
Deposit date:
2013-11-16
ARK identifier:

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