Presentation
5 March 2021 Breaking the geometric complexity of nanostructures using manifold learning
Author Affiliations +
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
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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KEYWORDS
Nanostructures

Artificial intelligence

Nanolithography

Structural design

Fabrication

Nanophotonics

Nanotechnology

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