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We develop an approach that enables characterization of wavelength-scale objects with deep subwavelength resolution. The technique combines diffractive imaging that out-couples the information about the subwavelength features of the object into the far-field zone with machine learning that analyzes the resulting patterns. Recovery of complex objects with 120-nm resolution with ~530-nm light is demonstrated experimentally. Our theoretical analysis suggests that the same objects can be recovered with up to 2-micron-wavelength light. Our work opens the door for new characterization tools that combine high spatial resolution, fast data acquisition, and artificial intelligence
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Viktor A. Podolskiy, Abantika Ghosh, Diane J. Roth, Luke H. Nicholls, William P. Wardley, Anatoly V. Zayats, "Machine-learning-assisted diffractive imaging with subwavelength resolution," Proc. SPIE 11460, Metamaterials, Metadevices, and Metasystems 2020, 1146019 (20 August 2020); https://doi.org/10.1117/12.2568122