Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147366
Title: Unlabeled far-field deeply subwavelength topological microscopy (DSTM)
Authors: Pu, Tanchao
Ou, Jun-Yu
Savinov, Vassili
Yuan, Guanghui
Papasimakis, Nikitas
Zheludev, Nikolay I.
Keywords: Science::Physics
Issue Date: 2020
Source: Pu, T., Ou, J., Savinov, V., Yuan, G., Papasimakis, N. & Zheludev, N. I. (2020). Unlabeled far-field deeply subwavelength topological microscopy (DSTM). Advanced Science, 8(1). https://dx.doi.org/10.1002/advs.202002886
Project: MOE2016-T3-1-006
SERC A1685b0005
Journal: Advanced Science 
Abstract: A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolutionize biomedicine and nanotechnology but is not yet available. Here, a new type of microscopy is introduced, which reveals the fine structure of an object through its far-field scattering pattern under illumination with light containing deeply subwavelength singularity features. The object is reconstructed by a neural network trained on a large number of scattering events. In numerical experiments on imaging of a dimer, resolving powers better than λ/200, i.e., two orders of magnitude beyond the conventional "diffraction limit" of λ/2, are demonstrated. It is shown that imaging is tolerant to noise and is achievable with low dynamic range light intensity detectors. Proof-of-principle experimental confirmation of DSTM is provided with a training set of small size, yet sufficient to achieve resolution five-fold better than the diffraction limit. In principle, deep learning reconstruction can be extended to objects of random shape and shall be particularly efficient in microscopy of a priori known shapes, such as those found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.
URI: https://hdl.handle.net/10356/147366
ISSN: 2198-3844
DOI: 10.1002/advs.202002886
Rights: © 2020 The Author(s). Published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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