Plasmonic nanostructure design and characterization via Deep Learning

By Itzik Malkiel, M. Mrejen, Achiya Nagler, U. Arieli, Lior Wolf, H. Suchowski. Light: Science & Applications, 2018

620 citations recorded by the source corpus.

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. Scientists have used the power of computing to design tiny structures capable of controlling light at the nanoscale, opening the door for new applications in sensing, imaging and spectroscopy. The emerging field of nano-photonics, which enables the manipulation of light-matter interactions using nanostructures, has revolutionized the field of optics. However, designing a nanostructure that produces a desired optical response is very challenging. Rising to the challenge, Haim Suchowski and colleagues from Tel Aviv University in Israel have developed an innovative technique that uses Deep Neural Networks to model the complex relationships between light-matter interactions, allowing them to characterise nanostructures based on their far-field optical responses. Their approach provides a rapid and efficient method for the designing the optical responses of nanostructures, and could be used in a range of applications, including sensing and imaging.

Subfields: Biomedical Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/e2ba2901ad1efe364ad3617e795bc86322902f46

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In Biomedical Engineering

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5. Wei Ma
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7. Peter R. Wiecha
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In Electronic, Optical and Magnetic Materials

1. Yongmin Liu
2. Wei Ma
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4. Rohit Unni
5. Kan Yao
6. Peter R. Wiecha
7. Yuebing Zheng
8. C. Girard

Citations in the corpus

• Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, background): “For example, I. Malkiel et al. experimentally demonstrated that a deep neural network trained with thousands of synthetic experiments can retrieve subwavelength meta-atoms from far-field measurements and address the inverse design problem [54].”
• Deep learning in nano-photonics: inverse design and beyond (2020, background): “Recently, the idea has been picked up and it has been shown, for instance, that scattering and extinction spectra can be predicted with high accuracy [52] and also that the phase can be included in the predictions [53], which is important for nano-structures in metasurfaces.”
• Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy (2019, background): “More recent works sought to deal with the inverse design by DL under various scenarios, like plasmonic waveguide [48], optical power splitter [49], plasmonic metamaterials [50], chiral metamaterials [51] and nanophotonic particles [52].”
• Deep Generative Models in Engineering Design: A Review (2021, methodology): “Plain NN [53]i [54]vi [55]p∗ [56]i [57]p [58]i [59]i”
• Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale (2018, methodology): “[20-22] (Figure 1).”