Journal
NANO LETTERS
Volume 18, Issue 10, Pages 6570-6576Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.8b03171
Keywords
Metasurface; nanophotonics; inverse design; neural networks
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Funding
- Office of Naval Research [N00014-17-1-2555]
- National Science Foundation [ECCS-1609567]
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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 systematic, inverse-design manner.
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