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Luttinger liquid theory as a model of the gigahertz electrical properties of carbon nanotubes
Abstract
Presents a technique to directly excite Luttinger liquid collective modes in carbon nanotubes at gigahertz frequencies. By modeling the nanotube as a nano-transmission line with distributed kinetic and magnetic inductance as well as distributed quantum and electrostatic capacitance, we calculate the complex frequency-dependent impedance for a variety of measurement geometries. Exciting voltage waves on the nano-transmission line is equivalent to directly exciting the yet-to-be observed one-dimensional plasmons, the low energy excitation of a Luttinger liquid. Our technique has already been applied to two-dimensional plasmons and should work well for one-dimensional plasmons. Tubes of length 100 microns must be grown for gigahertz resonance frequencies. Ohmic contact is not necessary with our technique; capacitive contacts can work. Our modeling has applications in potentially terahertz nanotube transistors and RF nanospintronics.
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Cited by
Deng, J., & Wong, H.-S. P. (2007) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2007.909043 · Cited by 746
Massobrio, G., Massobrio, P., & Martinoia, S. (2008) · Nano Letters · doi:10.1021/nl802341r · Cited by 32
Metadata
- Authors
- Burke, P. J.
- Venue
- IEEE Transactions on Nanotechnology
- Published
- 2002-09
- Volume
- 1
- Issue
- 3
- Pages
- 129-144
- ISSN
- 1536-125X (ISSN-L)
- 1941-0085
- OpenAlex
- W2153602935
- Cited by
- 595
- References
- 0
- FWCI
- 6.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Burke, P. J. (2002). Luttinger liquid theory as a model of the gigahertz electrical properties of carbon nanotubes. IEEE Transactions on Nanotechnology. https://doi.org/10.1109/tnano.2002.806823
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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.
