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Proceedings Paper

Deep-learning-enabled generative models for plasmonic metastructures (Conference Presentation)
Author(s): Wenshan Cai

Paper Abstract

The need for light manipulation on the nanoscale has prompted the recent advent and prosperity of plasmonic metastructures. To fully unlock the potential of such engineered optical media, plasmonic structures are exploited with progressively greater complexity, including those with arbitrarily complicated topology, spatially variant building blocks, and multi-layered configurations. The astronomical degrees of freedom associated with such structures have obstructed effective design and implementation schemes based on the conventional wisdom. We have developed a series of deep-learning enabled generative frameworks for the inverse design of plasmonic structures in response to on-demand optical properties, with extended case studies and experimental demonstrations.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11283, Integrated Optics: Devices, Materials, and Technologies XXIV, 1128307 (10 March 2020); doi: 10.1117/12.2551508
Show Author Affiliations
Wenshan Cai, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 11283:
Integrated Optics: Devices, Materials, and Technologies XXIV
Sonia M. García-Blanco; Pavel Cheben, Editor(s)

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