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Unlabeled Far?Field Deeply Subwavelength Topological Microscopy (DSTM)

ABSTRACT: 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 ?/200, i.e., two orders of magnitude beyond the conventional “diffraction limit” of ?/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. Nonintrusive, label?free, far?field optical microscopy is introduced based on illumination of an object with topologically structured light that contains multiple deeply subwavelength singularity features. This new type of optical microscopy allows to resolve the fine structure of an object with resolution that can exceed the conventional diffraction limit by two orders of magnitude.

SUBMITTER: Pu T

PROVIDER: S-EPMC7788582 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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