Broadband ultra-flat optics with experimental efficiency up to 99% in the visible via convolutional neural network

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

doi:10.1117/12.2582787

5 March 2021 Broadband ultra-flat optics with experimental efficiency up to 99% in the visible via convolutional neural network

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

Author Affiliations +

Proceedings Volume 11703, AI and Optical Data Sciences II; 117031P (2021) https://doi.org/10.1117/12.2582787
Event: SPIE OPTO, 2021, Online Only

Conference Poster

Abstract

Flat optics allow the production of integrated, lightweight, portable and wearable optical devices. In this work we propose a flat optics design platform that employs concepts from evolutionary algorithms to deep learning with convolutional neural networks, and demonstrate a general design framework that can furnish an arbitrarily designed system response in as little as 50nm of silicon. The proposed framework is fundamental for our most recent experimental paper, in which we present a plethora of high efficiency devices, including, but not limited to: polarizing beam splitters, dichroic mirrors and metasurfaces for a novel 2-pixel display technology.

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Fedor Getman, Maksim Makarenko, Arturo Burguete-Lopez, and Andrea Fratalocchi "Broadband ultra-flat optics with experimental efficiency up to 99% in the visible via convolutional neural network", Proc. SPIE 11703, AI and Optical Data Sciences II, 117031P (5 March 2021); https://doi.org/10.1117/12.2582787

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