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Proceedings Paper

Infrared imaging using carbon nanotube-based detector
Author(s): Hongzhi Chen; Ning Xi; Bo Song; Liangliang Chen; King W. C. Lai; Jianyong Lou
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Paper Abstract

Using carbon nanotubes (CNT), high performance infrared detectors have been developed. Since the CNTs have extraordinary optoelectronics properties due to its unique one dimensional geometry and structure, the CNT based infrared detectors have extremely low dark current, low noise equivalent temperature difference (NETD), short response time, and high dynamic range. Most importantly, it can detect 3-5 um middle-wave infrared (MWIR) at room temperature. This unique feature can significantly reduce the size and weight of a MWIR imaging system by eliminating a cryogenic cooling system. However, there are two major difficulties that impede the application of CNT based IR detectors for imaging systems. First, the small diameter of the CNTs results in low fill factor. Secondly, it is difficult to fabricate large scale of detector array for high resolution focal plane due to the limitations on the efficiency and cost of the manufacturing. In this paper, a new CNT based IR imaging system will be presented. Integrating the CNT detectors with photonic crystal resonant cavity, the fill factor of the CNT based IR sensor can reach as high as 0.91. Furthermore, using the compressive sensing technology, a high resolution imaging can be achieved by CNT based IR detectors. The experimental testing results show that the new imaging system can achieve the superb performance enabled by CNT based IR detectors, and, at the same time, overcame its difficulties to achieve high resolution and efficient imaging.

Paper Details

Date Published: 3 June 2011
PDF: 9 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580N (3 June 2011); doi: 10.1117/12.889122
Show Author Affiliations
Hongzhi Chen, Michigan State Univ. (United States)
Ning Xi, Michigan State Univ. (United States)
Bo Song, Michigan State Univ. (United States)
Liangliang Chen, Michigan State Univ. (United States)
King W. C. Lai, Michigan State Univ. (United States)
Jianyong Lou, Xian Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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