Plasmonic nanostructure design and characterization via Deep Learning

Itzik Malkiel; Michael Mrejen; Achiya Nagler; Uri Arieli; Lior Wolf; Haim Suchowski

doi:10.1038/s41377-018-0060-7

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Plasmonic nanostructure design and characterization via Deep Learning

Original Articles

- Plasmonic nanostructure design and characterization via Deep Learning
- Plasmonic nanostructure design and characterization via Deep Learning
- 光：科学与应用 2018年7卷第5期页码：555-562
- Affiliations：
  
  1.School of Computer Science, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
  2.School of Physics and Astronomy, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
- Author bio：
  
  Haim Suchowski (haimsu@post.tau.ac.il)
- Funds：
- DOI：10.1038/s41377-018-0060-7
  中图分类号：
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Itzik Malkiel, Michael Mrejen, Achiya Nagler, 等. Plasmonic nanostructure design and characterization via Deep Learning[J]. 光：科学与应用, 2018,7(5):555-562.

Malkiel, I. et al. Plasmonic nanostructure design and characterization via Deep Learning. Light: Science & Applications, 5, 555-562 (2018).
Itzik Malkiel, Michael Mrejen, Achiya Nagler, 等. Plasmonic nanostructure design and characterization via Deep Learning[J]. 光：科学与应用, 2018,7(5):555-562. DOI： 10.1038/s41377-018-0060-7.

Malkiel, I. et al. Plasmonic nanostructure design and characterization via Deep Learning. Light: Science & Applications, 5, 555-562 (2018). DOI： 10.1038/s41377-018-0060-7.

摘要

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.

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references

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