Abdelraouf, O. A. M., Wang, Z., Liu, H., Dong, Z., Wang, Q., Ye, M., Wang, X. R., Wang, Q. J., & Liu, H. (2022). Recent Advances in Tunable Metasurfaces: Materials, Design, and Applications. ACS Nano, 16(9), 13339–13369. https://doi.org/10.1021/acsnano.2c04628
Abstract:
Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by planar meta-atoms, exhibit exotic abilities to tailor electromagnetic (EM) waves freely. Over the past decade, tremendous efforts have been made to develop various active materials and incorporate them into functional devices for practical applications, pushing the research of tunable metasurfaces to the forefront of nanophotonics. Those active materials include phase change materials (PCMs), semiconductors, transparent conducting oxides (TCOs), ferroelectrics, liquid crystals (LCs), atomically thin material, etc., and enable intriguing performances such as fast switching speed, large modulation depth, ultracompactness, and significant contrast of optical properties under external stimuli. Integration of such materials offers substantial tunability to the conventional passive nanophotonic platforms. Tunable metasurfaces with multifunctionalities triggered by various external stimuli bring in rich degrees of freedom in terms of material choices and device designs to dynamically manipulate and control EM waves on demand. This field has recently flourished with the burgeoning development of physics and design methodologies, particularly those assisted by the emerging machine learning (ML) algorithms. This review outlines recent advances in tunable metasurfaces in terms of the active materials and tuning mechanisms, design methodologies, and practical applications. We conclude this review paper by providing future perspectives in this vibrant and fast-growing research field.
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This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP50120-0009, MOET2EP50120- 0006 and MOE-T2EP50220-0005
This research / project is supported by the A*STAR - AME programmatic grant
Grant Reference no. : A18A7b0058
This research / project is supported by the A*STAR - AME Individual Research Grants (IRG)
Grant Reference no. : A20E5c0093, A20E5c0094, and A20E5c0095, M21K2c0116
This research / project is supported by the A*STAR - Career Development Award (CDA)
Grant Reference no. : C210112019 and C210112044
This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP19-2017-01 and NRF-CRP22-2019-0007