Generative Model for the Inverse Design of Metasurfaces

The advent of metasurfaces in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively sol...

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Bibliographic Details
Published in:Nano letters 2018-10, Vol.18 (10), p.6570-6576
Main Authors: Liu, Zhaocheng, Zhu, Dayu, Rodrigues, Sean P, Lee, Kyu-Tae, Cai, Wenshan
Format: Article
Language:English
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Summary:The advent of metasurfaces in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell’s equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this conventional design procedure by means of a deep learning architecture. When fed an input set of customer-defined optical spectra, the constructed generative network generates candidate patterns that match the on-demand spectra with high fidelity. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.
ISSN:1530-6984
1530-6992
DOI:10.1021/acs.nanolett.8b03171