{"id":"doi:10.1038/s41377-018-0060-7","kind":"article","title":"Plasmonic nanostructure design and characterization via Deep Learning","authors":["Itzik Malkiel","Michael Mrejen","Achiya Nagler","Uri Arieli","Lior Wolf","Haim Suchowski"],"author_count":6,"year":2018,"published":"2018-09-04","venue":"Light Science & Applications","cited_by":722,"doi":"10.1038/s41377-018-0060-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41377-018-0060-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41377-018-0060-7","updated":null,"pmid":"30863544","pmcid":null,"openalex_id":"W2891797827","pdf_url":"https://www.nature.com/articles/s41377-018-0060-7.pdf","abstract":"Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Metamaterials and Metasurfaces Applications"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Plasmonic nanostructure design and characterization via Deep Learning\" by Itzik Malkiel, Michael Mrejen, Achiya Nagler et al., https://doi.org/10.1038/s41377-018-0060-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"metamaterials-photonics","name":"Metamaterials, Nanophotonics & Integrated Photonics","domain_slug":"materials"}],"cited_in_dossiers":[]}