Neural network-aided design and fabrication of deformation robust flexible flat optics

Arturo Burguete-Lopez; Maksim O. Makarenko; Fedor Getman; Qizhou Wang; Andrea Fratalocchi

doi:10.1117/12.2621246

30 May 2022 Neural network-aided design and fabrication of deformation robust flexible flat optics

Arturo Burguete-Lopez, Maksim O. Makarenko, Fedor Getman, Qizhou Wang, Andrea Fratalocchi

Proceedings Volume PC12130, Metamaterials XIII; PC121301D (2022) https://doi.org/10.1117/12.2621246
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Conference Poster

Abstract

In this work we make use of an inverse design methodology for the design of high efficiency deformation robust flat optics. Our approach leverages neural network predictors trained to quickly estimate the results of finite difference time domain (FDTD) simulations. By rapidly exploring the solution space, we find geometries that exhibit an optical response tolerant to dimensional errors. We validate our approach by fabricating and characterizing flat optics polarizers on top of polyamide tape. The devices exhibit a polarization efficiency of 85% over a 200 nm bandwidth and retain high performance when subjected to large deformations, in contrast to a control non-robust design.

Citation Download Citation

Arturo Burguete-Lopez, Maksim O. Makarenko, Fedor Getman, Qizhou Wang, and Andrea Fratalocchi "Neural network-aided design and fabrication of deformation robust flexible flat optics", Proc. SPIE PC12130, Metamaterials XIII, PC121301D (30 May 2022); https://doi.org/10.1117/12.2621246

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