Corpus ID: 212718130

Elucidating the Design and Behavior of Nanophotonic Structures through Explainable Convolutional Neural Networks

@article{Yeung2020ElucidatingTD,
  title={Elucidating the Design and Behavior of Nanophotonic Structures through Explainable Convolutional Neural Networks},
  author={Christopher Yeung and Ju-Ming Tsai and Yusaku Kawagoe and Brian King and David Ho and Aaswath Raman},
  journal={arXiv: Optics},
  year={2020}
}
  • Christopher Yeung, Ju-Ming Tsai, +3 authors Aaswath Raman
  • Published 2020
  • Physics
  • arXiv: Optics
  • A central challenge in the development of nanophotonic structures and metamaterials is identifying the optimal design for a sought target functionality, and understanding the physical mechanisms that enable the optimized device's capabilities. To this end, previously investigated design methods for nanophotonic structures have encompassed both conventional forward and inverse optimization approaches as well as nascent machine learning (ML) strategies. While in principle more computationally… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES

    SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning

    • C. Molnar
    • ​Christophm.github.io​
    • 2020

    ​et al.​ Free-form diffractive metagrating design based on generative adversarial networks. ​ACS Nano​

    • J. Jiang
    • 2019

    A Generative Model for Inverse Design of Metamaterials

    ​et al.​ Inverse design in nanophotonics

    • S. Molesky
    • ​Nature Photonics​
    • 2018