Robust and scalable flat-optics on flexible substrates through evolutionary neural networks

Fedor Getman; Andrea Fratalocchi; Maksim Makarenko; Arturo Burguete-Lopez

doi:10.1117/12.2609805

5 March 2022 Robust and scalable flat-optics on flexible substrates through evolutionary neural networks

Fedor Getman, Andrea Fratalocchi, Maksim Makarenko, Arturo Burguete-Lopez

Proceedings Volume PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII; PC120100Y (2022) https://doi.org/10.1117/12.2609805
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

Over the past twenty years flat-optics and metasurfaces emerged as a promising light manipulation technology. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing. This problem becomes more severe in flexible structures. In this work, we present an inverse design platform that enables the fast design of flexible metasurfaces that maintain high performance under deformations. We validate this method by a series of experiments in which we realize broadband flexible light polarizers efficiency of 80% over 200 nm bandwidths.

Conference Presentation

Citation Download Citation

Fedor Getman, Andrea Fratalocchi, Maksim Makarenko, and Arturo Burguete-Lopez "Robust and scalable flat-optics on flexible substrates through evolutionary neural networks", Proc. SPIE PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII, PC120100Y (5 March 2022); https://doi.org/10.1117/12.2609805

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