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-fie...

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Main Authors: Pu, T., Ou, J. Y., Papasimakis, N., Zheludev, Nikolay I.
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/143948
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1439482023-02-28T19:51:50Z Label-free deeply subwavelength optical microscopy Pu, T. Ou, J. Y. Papasimakis, N. Zheludev, Nikolay I. School of Physical and Mathematical Sciences Centre for Disruptive Photonic Technologies The Photonics Institute Science::Physics Deep Learning 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. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Published version 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). 2020-10-02T06:22:54Z 2020-10-02T06:22:54Z 2020 Journal Article Pu, T., Ou, J. Y., Papasimakis, N., & Zheludev, N. I. (2020). Label-free deeply subwavelength optical microscopy. Applied Physics Letters, 116(13), 131105-. doi:10.1063/5.0003330 0003-6951 https://hdl.handle.net/10356/143948 10.1063/5.0003330 13 116 en MOE2016-T3-1-006 SERC A1685b0005 Applied Physics Letters © 2020 The Author(s). All rights reserved. This paper was published by AIP Publishing in Applied Physics Letters and is made available with permission of The Author(s). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Physics
Deep Learning
Optical Microscopy
spellingShingle Science::Physics
Deep Learning
Optical Microscopy
Pu, T.
Ou, J. Y.
Papasimakis, N.
Zheludev, Nikolay I.
Label-free deeply subwavelength optical microscopy
description 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.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Pu, T.
Ou, J. Y.
Papasimakis, N.
Zheludev, Nikolay I.
format Article
author Pu, T.
Ou, J. Y.
Papasimakis, N.
Zheludev, Nikolay I.
author_sort Pu, T.
title Label-free deeply subwavelength optical microscopy
title_short Label-free deeply subwavelength optical microscopy
title_full Label-free deeply subwavelength optical microscopy
title_fullStr Label-free deeply subwavelength optical microscopy
title_full_unstemmed Label-free deeply subwavelength optical microscopy
title_sort label-free deeply subwavelength optical microscopy
publishDate 2020
url https://hdl.handle.net/10356/143948
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