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Proceedings Paper

Carbon nanotube noise characterization
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Paper Abstract

Without relying on the cumbersome liquid Nitrogen coolant, necessary for the conventional mid IR (3~5 μm wavelength) cameras, we designed a new mid wave IR camera, according to biomimetic human vision 2 color receptor system. We suspended over the non-cryogenic long wave IR (HgCdTe) CCD backplane with Single Wall Carbon NanoTubes (SWNT) pixels, which have the band gap energy εBG ~1/d tuned at the few nanometer diameter d for the mid wave. To ascertain noise contribution, in this paper, we provided a simple derivation of frequency-dependent Einstein transport coefficient D(k) = PSD(k), based on Kubo-Green (KG) formula, which is convenient to accommodate experimental data. We conjectured a concave shape of convergence 1/kα at α=-2 power law at optical frequency against the overly simplest 1-D noise model about 1/2 KBT, and the ubiquitous power law 1/kα where α=1 gave a convex shape of divergence. Our formula is based on the Cauchy distribution [1+(kd)2]-1 derived from the Fourier Transform of the correlation of charge-carrier wave function been scattered against lattice phonons spreading over the tubular surface of the diameter d, similar to the Lorentzian line shape in molecular spectral exp(-|x|/d). According to the band gap formula of SWNT, a narrower tube of SWNT worked similarly as Field Emission Transistor (FET) can be tuned at higher optical frequencies revealing finer details of lattice spacing, a and b. Experimental determination of our proposed multiple scales responses formula remained to be confirmed.

Paper Details

Date Published: 17 April 2006
PDF: 12 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470S (17 April 2006); doi: 10.1117/12.670047
Show Author Affiliations
Harold Szu, The George Washington Univ. (United States)
Bassam Noaman, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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