Compound Meta-Optics for Complete and Loss-Less Field Control
Source abstract
Abstract
Optical metasurfaces offer a compact platform for manipulation of the amplitude, phase, and polarization state of light. Independent control over these properties, however, is hindered by the symmetric transmission matrix associated with single-layer metasurfaces. Here, we utilize multilayer birefringent meta-optics to realize high-efficiency, independent control over the amplitude, phase, and polarization state of light. High-efficiency control is enabled by redistributing the wavefront between cascaded metasurfaces, while end-to-end inverse design is used to realize independent complex-valued functions for orthogonal polarization states. Based on this platform, we demonstrate spatial mode division multiplexing, optical mode conversion, and universal vectorial holograms, all with diffraction efficiencies over 80%. This meta-optic platform expands the design space of flat optics and could lead to advances in optical communications, quantum entanglement, and information encryption.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Metamaterials and Metasurfaces Applications · Advanced Antenna and Metasurface Technologies · Orbital Angular Momentum in Optics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee 37212, United States
Department of Mechanical Engineering, Vanderbilt University, Nashville, Tennessee 37212, United States
Interdisciplinary Materials Science Program, Vanderbilt University, Nashville, Tennessee 37212, United States
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
Funding
Office of Naval Research
References (56)
Show source-provided references
1. 10.1038/nmat383910.1038/nmat3839 ↗
2. 10.1515/nanoph-2017-012910.1515/nanoph-2017-0129 ↗
3. 10.29026/oea.2020.20000210.29026/oea.2020.200002 ↗
4. 10.1126/science.abi686010.1126/science.abi6860 ↗
5. 10.1038/ncomms806910.1038/ncomms8069 ↗
6. 10.1126/science.aaf664410.1126/science.aaf6644 ↗
7. 10.1038/s41467-018-03831-710.1038/s41467-018-03831-7 ↗
8. 10.1039/D0NR03515G10.1039/D0NR03515G ↗
9. 10.1126/sciadv.abb666710.1126/sciadv.abb6667 ↗
10. 10.1002/adma.20200589310.1002/adma.202005893 ↗
11. 10.1002/adma.20210608010.1002/adma.202106080 ↗
12. 10.1126/sciadv.aba336710.1126/sciadv.aba3367 ↗
13. 10.1021/acsnano.1c0859710.1021/acsnano.1c08597 ↗
14. 10.1021/acs.nanolett.8b0301710.1021/acs.nanolett.8b03017 ↗
15. 10.1126/science.aax183910.1126/science.aax1839 ↗
16. 10.1117/1.AP.4.2.02400110.1117/1.AP.4.2.024001 ↗
17. 10.1038/s41566-019-0536-x10.1038/s41566-019-0536-x ↗
18. 10.1038/s41566-020-0591-310.1038/s41566-020-0591-3 ↗
19. 10.1021/acs.nanolett.0c0047110.1021/acs.nanolett.0c00471 ↗
20. 10.1021/acsphotonics.9b0018010.1021/acsphotonics.9b00180 ↗
21. 10.1038/s41467-018-07011-510.1038/s41467-018-07011-5 ↗
22. 10.1039/C7NR07154J10.1039/C7NR07154J ↗
23. 10.29026/oea.2021.21003010.29026/oea.2021.210030 ↗
24. 10.1002/inf2.1219110.1002/inf2.12191 ↗
25. 10.1002/lpor.20220009810.1002/lpor.202200098 ↗
26. 10.1126/sciadv.abg748810.1126/sciadv.abg7488 ↗
27. 10.1038/s41377-018-0091-010.1038/s41377-018-0091-0 ↗
28. 10.1021/acs.nanolett.8b0004710.1021/acs.nanolett.8b00047 ↗
29. 10.1126/science.aav963210.1126/science.aav9632 ↗
30. 10.1038/s41377-019-0201-710.1038/s41377-019-0201-7 ↗
31. 10.1002/adfm.20191061010.1002/adfm.201910610 ↗
32. 10.1021/acsphotonics.0c0104410.1021/acsphotonics.0c01044 ↗
33. 10.1038/srep3286710.1038/srep32867 ↗
34. 10.1038/s41377-019-0193-310.1038/s41377-019-0193-3 ↗
35. 10.1021/acsnano.0c0729510.1021/acsnano.0c07295 ↗
36. 10.1002/adom.20190026010.1002/adom.201900260 ↗
37. 10.1103/PhysRevLett.122.11390110.1103/PhysRevLett.122.113901 ↗
38. 10.1002/adpr.20210028510.1002/adpr.202100285 ↗
39. 10.1364/OPTICA.37478710.1364/OPTICA.374787 ↗
40. 10.1002/adom.20200160910.1002/adom.202001609 ↗
41. 10.1002/adom.20210217910.1002/adom.202102179 ↗
42. 10.1002/adom.20210259110.1002/adom.202102591 ↗
43. 10.1002/lpor.20180003110.1002/lpor.201800031 ↗
44. 10.1002/adom.20180167910.1002/adom.201801679 ↗
45. 10.1038/s41565-019-0611-y10.1038/s41565-019-0611-y ↗
46. 10.1002/adom.20210050610.1002/adom.202100506 ↗
47. Kingma, D. P.; Ba, J. L. Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations, San Diego, CA, May 7–9, 2015.
48. 10.1038/s41377-020-00439-910.1038/s41377-020-00439-9 ↗
49. 10.1103/PhysRevApplied.15.05403910.1103/PhysRevApplied.15.054039 ↗
50. 10.1021/acsphotonics.9b0067810.1021/acsphotonics.9b00678 ↗
51. 10.1021/acs.nanolett.0c0455510.1021/acs.nanolett.0c04555 ↗
52. 10.1126/sciadv.abo641010.1126/sciadv.abo6410 ↗
53. 10.1126/science.aat904210.1126/science.aat9042 ↗
54. 10.1364/CLEO_QELS.2021.FTu2M.410.1364/CLEO_QELS.2021.FTu2M.4 ↗
55. 10.1038/s41467-021-23814-510.1038/s41467-021-23814-5 ↗
56. Hugonin A. J. P. · ArXiv · 2012 · 1