Deep learning for accelerated all-dielectric metasurface design

Christian C. Nadell, Bohao Huang, Jordan M. Malof, Willie J. Padilla

Research output: Contribution to journalArticlepeer-review

Abstract

Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field, but significant challenges remain, including the requirement of large labeled datasets and one-to-many mapping that arises in solving the inverse problem. Here we demonstrate modeling of complex all-dielectric metasurface systems with deep neural networks, using both the metasurface geometry and knowledge of the underlying physics as inputs. Our deep learning network is highly accurate, achieving an average mean square error of only 1.16 × 10−3 and is over five orders of magnitude faster than conventional electromagnetic simulation software. We further develop a novel method to solve the inverse modeling problem, termed fast forward dictionary search (FFDS), which offers tremendous controls to the designer and only requires an accurate forward neural network model. These techniques significantly increase the viability of more complex all-dielectric metasurface designs and provide opportunities for the future of tailored light matter interactions.

Original languageEnglish
Pages (from-to)27523-27535
Number of pages13
JournalOptics Express
Volume27
Issue number20
DOIs
StatePublished - Sep 30 2019

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