Manifold Learning for Reducing the Design Complexity of Photonic Nanostructures

Mohammadreza Zandehshahvar; Yashar Kiarashi; Muliang Zhu; Hossein Maleki; Tyler Brown; Ali Adibi

doi:10.1364/CLEO_AT.2021.JTu3A.115

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu3A.115
•https://doi.org/10.1364/CLEO_AT.2021.JTu3A.115

Manifold Learning for Reducing the Design Complexity of Photonic Nanostructures

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Hossein Maleki, Tyler Brown, and Ali Adibi

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session I (JTu3A)

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M. Zandehshahvar, Y. Kiarashi, M. Zhu, H. Maleki, T. Brown, and A. Adibi, "Manifold Learning for Reducing the Design Complexity of Photonic Nanostructures," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu3A.115.

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Abstract

We present a new manifold-learning-based approach to reduce the geometric complexity of the inverse design of photonic nanostructures and show how this approach can provide valuable insight about the underlying physics of their operation.

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