Terahertz (THz) radiation has become increasingly important in many scientific and commercial fields in recent years. It possesses many remarkable features resulting in an increased use of THz radiation for various applications, like biomedical imaging, security screening, and industrial quality control. The capability of these applications depends directly on the availability of powerful THz sources and high-responsivity, fast THz detectors. Current commercial products used to detect THz radiation, like Golay cells and pyroelectric detectors, have only slow detection rates and poor sensitivities. Other commercial THz detectors, like bolometers, are more sensitive but require liquid helium cooling. In this thesis, two types of room-temperature high-responsivity graphene-based THz detectors are presented, relying on the unique properties of graphene and the function of plasmonic antenna arrays which boost the interaction between THz waves and graphene. Graphene has been demonstrated as a promising material for THz detection. However, the challenge is its insufficient light absorption that largely limits the responsivity. The first design is based on an array of planar antennas arranged in series and shorted by graphene squares. Highly efficient photodetection can be achieved by using the metallic antenna to simultaneously improve both light absorption, as resonant elements, and photocarrier collection, as electrodes. The device has been characterized with quantum cascade lasers, yielding a maximum responsivity of ~2 mA/W at 2 THz. The second detector is based on an array of interdigitated bow-tie antennas connected in parallel and shunted by graphene squares. The arms of the bow-tie antennas were made of two metals with different work functions to create a built-in electric field and improve the responsivity. The device has been characterized and yields a maximum responsivity of ∼34 μA/W at 2 THz. Efficient THz imaging is presented by integrating the detector in a QCL-based THz imaging system.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:744668 |
| Date | January 2018 |
| Creators | Xiao, Long |
| Contributors | Hofmann, Stephan |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/274565 |
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