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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Bibliographic Details
Published in:Applied physics letters 2020-03, Vol.116 (13)
Main Authors: Pu, T., Ou, J. Y., Papasimakis, N., Zheludev, N. I.
Format: Article
Language:English
Subjects:
Applied physics
Coherent light
Coherent scattering
Dimers
Information retrieval
Luminous intensity
Machine learning
Machine vision
Microscopes
Microscopy
Neural networks
Object recognition
Optical microscopy
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Summary: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.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0003330