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Label-free deeply subwavelength optical microscopy

Label-free deeply subwavelength optical microscopy
Label-free deeply subwavelength optical microscopy
We report the experimental demonstration of deeply subwavelength far-field optical microscopy of unlabeled samples. We beat the ∼λ/2 diffraction limit of conventional optical microscopy several times over by recording the intensity pattern of coherent light scattered from the object into the far-field. We retrieve information about the object with a deep learning neural network trained on scattering events from a large set of known objects. The microscopy retrieves dimensions of the imaged object probabilistically. Widths of the subwavelength components of the dimer are measured with a precision of λ/10 with the probability higher than 95% and with a precision of λ/20 with the probability better than 77%. We argue that the reported microscopy can be extended to objects of random shape and shall be particularly efficient on object of known shapes, such as found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.
The authors acknowledge the support of the Singapore Ministry of Education (Grant No. MOE2016-T3-1-006), the Agency for Science, Technology and Research (A*STAR) Singapore (Grant No. SERC A1685b0005), the Engineering and Physical Sciences Research Council UK (Grant Nos. EP/N00762X/1 and EP/M009122/1), and the European Research Council (Advanced Grant No. FLEET-786851). T.P. acknowledges the support of the Chinese Scholarship Council (CSC No. 201804910540). Following a period of embargo, the data from this paper can be obtained from the University of Southampton ePrints research repository (https://doi.org/10.5258/SOTON/D1301).
0003-6951
1-4
Pu, Tanchao
43bd2c3e-95a0-4ee3-ab74-dfba1e2c1721
Ou, Jun-Yu
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6
Pu, Tanchao
43bd2c3e-95a0-4ee3-ab74-dfba1e2c1721
Ou, Jun-Yu
3fb703e3-b222-46d2-b4ee-75f296d9d64d
Papasimakis, Nikitas
f416bfa9-544c-4a3e-8a2d-bc1c11133a51
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6

Pu, Tanchao, Ou, Jun-Yu, Papasimakis, Nikitas and Zheludev, Nikolai (2020) Label-free deeply subwavelength optical microscopy. Applied Physics Letters, 116 (13), 1-4, [131105]. (doi:10.1063/5.0003330).

Record type: Article

Abstract

We report the experimental demonstration of deeply subwavelength far-field optical microscopy of unlabeled samples. We beat the ∼λ/2 diffraction limit of conventional optical microscopy several times over by recording the intensity pattern of coherent light scattered from the object into the far-field. We retrieve information about the object with a deep learning neural network trained on scattering events from a large set of known objects. The microscopy retrieves dimensions of the imaged object probabilistically. Widths of the subwavelength components of the dimer are measured with a precision of λ/10 with the probability higher than 95% and with a precision of λ/20 with the probability better than 77%. We argue that the reported microscopy can be extended to objects of random shape and shall be particularly efficient on object of known shapes, such as found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.
The authors acknowledge the support of the Singapore Ministry of Education (Grant No. MOE2016-T3-1-006), the Agency for Science, Technology and Research (A*STAR) Singapore (Grant No. SERC A1685b0005), the Engineering and Physical Sciences Research Council UK (Grant Nos. EP/N00762X/1 and EP/M009122/1), and the European Research Council (Advanced Grant No. FLEET-786851). T.P. acknowledges the support of the Chinese Scholarship Council (CSC No. 201804910540). Following a period of embargo, the data from this paper can be obtained from the University of Southampton ePrints research repository (https://doi.org/10.5258/SOTON/D1301).

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Label-free Deeply Subwavelength Optical Microscopy - Accepted Manuscript
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Accepted/In Press date: 12 March 2020
e-pub ahead of print date: 31 March 2020
Published date: March 2020

Identifiers

Local EPrints ID: 440957
URI: http://eprints.soton.ac.uk/id/eprint/440957
ISSN: 0003-6951
PURE UUID: 93dc1df2-2df3-4938-8ae8-92f4af0f20cf
ORCID for Jun-Yu Ou: ORCID iD orcid.org/0000-0001-8028-6130
ORCID for Nikitas Papasimakis: ORCID iD orcid.org/0000-0002-6347-6466
ORCID for Nikolai Zheludev: ORCID iD orcid.org/0000-0002-1013-6636

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Date deposited: 26 May 2020 16:30
Last modified: 27 May 2020 00:32

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