Machine-learning-assisted diffractive imaging with subwavelength resolution

Viktor A. Podolskiy; Abantika Ghosh; Diane J. Roth; Luke H. Nicholls; William P. Wardley; Anatoly V. Zayats

doi:10.1117/12.2568122

20 August 2020 Machine-learning-assisted diffractive imaging with subwavelength resolution

Viktor A. Podolskiy, Abantika Ghosh, Diane J. Roth, Luke H. Nicholls, William P. Wardley, Anatoly V. Zayats

Proceedings Volume 11460, Metamaterials, Metadevices, and Metasystems 2020; 1146019 (2020) https://doi.org/10.1117/12.2568122
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

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, and 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

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KEYWORDS

Image resolution

Machine learning

Artificial intelligence

Data acquisition

Diffraction

Evolutionary algorithms

Geometrical optics

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