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dc.contributor.authorAn, S
dc.contributor.authorZheng, B
dc.contributor.authorJulian, M
dc.contributor.authorWilliams, C
dc.contributor.authorTang, H
dc.contributor.authorGu, T
dc.contributor.authorZhang, H
dc.contributor.authorKim, HJ
dc.contributor.authorHu, J
dc.date.accessioned2022-06-13T23:30:50Z
dc.date.available2022-06-13T23:30:50Z
dc.date.issued2022
dc.identifier.issn2192-8606
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338046
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (<jats:italic>Q</jats:italic>-factors ∼ 10<jats:sup>2</jats:sup>), and large out-of-band rejection (optical density ≥ 3). The design consists of an optical phase-change material Ge<jats:sub>2</jats:sub>Sb<jats:sub>2</jats:sub>Se<jats:sub>4</jats:sub>Te (GSST) metasurface atop a silicon heater sandwiched between two distributed Bragg reflectors (DBRs). The proposed design approach can be generalized to the modeling and inverse design of arbitrary response photonic devices incorporating active metasurfaces.</jats:p>
dc.publisherWalter de Gruyter GmbH
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDeep neural network enabled active metasurface embedded design
dc.typeArticle
dc.publisher.departmentDepartment of Physics
dc.date.updated2022-06-13T15:19:20Z
prism.issueIdentifier0
prism.publicationNameNanophotonics
prism.volume0
dc.identifier.doi10.17863/CAM.85455
dcterms.dateAccepted2022-05-27
rioxxterms.versionofrecord10.1515/nanoph-2022-0152
rioxxterms.versionVoR
dc.contributor.orcidAn, S [0000-0003-4098-916X]
dc.contributor.orcidWilliams, C [0000-0002-6432-6515]
dc.contributor.orcidGu, T [0000-0003-3989-6927]
dc.identifier.eissn2192-8614
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-06-10
cam.depositDate2022-06-13
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International