| CONDENSED MATTER: STRUCTURE, MECHANICAL AND THERMAL PROPERTIES |
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Diffraction-Limited Imaging with a Graphene Metalens |
| Xueyan Li1,2, Han Lin2*, Yuejin Zhao1*, and Baohua Jia2,3* |
1Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
2Centre for Translational Atomaterials, Swinburne University of Technology. P.O. Box 218, Hawthorn, VIC 3122, Australia
3The Australian Research Council (ARC) Industrial Transformation Training Centre in Surface Engineering for Advanced Materials (SEAM), Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia
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| Cite this article: |
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Xueyan Li, Han Lin, Yuejin Zhao et al 2020 Chin. Phys. Lett. 37 106801 |
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Abstract Planar graphene metalens has demonstrated advantages of ultrathin thickness (200 nm), high focusing resolution (343 nm) and efficiency ($>$32%) and robust mechanical strength and flexibility. However, diffraction-limited imaging with such a graphene metalens has not been realized, which holds the key to designing practical integrated imaging systems. In this work, the imaging rule for graphene metalenses is first derived and theoretically verified by using the Rayleigh-Sommerfeld diffraction theory to simulate the imaging performance of the 200 nm ultrathin graphene metalens. The imaging rule is applicable to graphene metalenses in different immersion media, including water or oil. Based on the theoretical prediction, high-resolution imaging using the graphene metalens with diffraction-limited resolution (500 nm) is demonstrated for the first time. This work opens the possibility for graphene metalenses to be applied in particle tracking, microfluidic chips and biomedical devices.
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Received: 12 July 2020
Published: 29 September 2020
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| PACS: |
68.65.Pq
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(Graphene films)
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78.67.Wj
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(Optical properties of graphene)
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78.68.+m
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(Optical properties of surfaces)
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78.67.Pt
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(Multilayers; superlattices; photonic structures; metamaterials)
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| Fund: Supported by the Scholarship of China Scholarship Council (Grant No. 201706030189), the Industrial Transformation Training Centres Scheme (Grant No. IC180100005), and the National Natural Science Foundation of China (Grant No. 61935001). |
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