Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143948
Title: Label-free deeply subwavelength optical microscopy
Authors: Pu, T.
Ou, J. Y.
Papasimakis, N.
Zheludev, Nikolay I.
Keywords: Science::Physics
Issue Date: 2020
Source: 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
Project: MOE2016-T3-1-006
SERC A1685b0005
Journal: Applied Physics Letters
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.
URI: https://hdl.handle.net/10356/143948
ISSN: 0003-6951
DOI: 10.1063/5.0003330
Rights: © 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).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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