Breaking the geometric complexity of nanostructures using manifold learning

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

doi:10.1117/12.2590200

5 March 2021 Breaking the geometric complexity of nanostructures using manifold learning

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

Proceedings Volume 11694, Photonic and Phononic Properties of Engineered Nanostructures XI; 116940S (2021) https://doi.org/10.1117/12.2590200
Event: SPIE OPTO, 2021, Online Only

Abstract

We present a new approach based on manifold learning for breaking the geometrical complexity of the photonic nanostructures during solving the inverse design problem. By encoding the high-dimensional spectral responses of a class of nanostructures into the latent space, we provide intuitive information about the underlying physics of these structures. We discuss the relations between the non-Euclidean distances in the latent space and changes in the optical responses and relate the movements in the latent space to the modifications of the optical responses for a class of nanostructures. Finally, we provide a new approach to use the insight about the role of design parameters to design nanostructures with minimal design complexity for a given functionality.

Conference Presentation

Citation Download Citation

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Hossein Maleki, Tyler Brown, and Ali Adibi "Breaking the geometric complexity of nanostructures using manifold learning", Proc. SPIE 11694, Photonic and Phononic Properties of Engineered Nanostructures XI, 116940S (5 March 2021); https://doi.org/10.1117/12.2590200

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