# Plasmonic nanostructure design and characterization via Deep Learning

Article · Itzik Malkiel, Michael Mrejen, Achiya Nagler, Uri Arieli, Lior Wolf, Haim Suchowski

- Archive id: doi:10.1038/s41377-018-0060-7
- Published: 2018-09-04
- Venue: Light Science & Applications
- DOI: https://doi.org/10.1038/s41377-018-0060-7
- PMID: 30863544
- Citations: 722
- Source URL: https://doi.org/10.1038/s41377-018-0060-7
- PDF: https://www.nature.com/articles/s41377-018-0060-7.pdf
- Provenance: openalex

## 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 reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Metamaterials, Nanophotonics & Integrated Photonics](https://space-kitty.com/topics/metamaterials-photonics)

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