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-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 versionThe 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)