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Deep learning for the design of photonic structures

Nature Photonics volume 15, pages 77–90 (2021)Cite this article

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Abstract

Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. Here, we review recent progress in deep-learning-based photonic design by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks. We also comment on the challenges and perspectives of this emerging research direction.

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Fig. 1: Applying deep learning to solve photonic design problems.
Fig. 2: Photonic designs enabled by an MLP model.
Fig. 3: Advanced deep-learning frameworks in optics and photonics.
Fig. 4: Deep-learning-assisted optimization methods.

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Acknowledgements

Y.L. acknowledges financial support from the US National Science Foundation (NSF) (ECCS-1916839) and the Office of Naval Research (N00014-16-1-2409). W.C. acknowledges support from the Office of Naval Research (N00014-17-1-2555) and the NSF (DMR-2004749). The Purdue team acknowledges financial support from DARPA/DSO (HR00111720032, Z.A.K.), the US National Science Foundation (ECCS-2029553, A.B.) and the Air Force Office of Scientific Research (AFOSR) (FA9550-20-1-0124, A.B.).

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA

    Wei Ma & Yongmin Liu

  2. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

    Wei Ma & Yongmin Liu

  3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Zhaocheng Liu & Wenshan Cai

  4. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Zhaocheng Liu & Wenshan Cai

  5. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Zhaxylyk A. Kudyshev & Alexandra Boltasseva

  6. Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA

    Zhaxylyk A. Kudyshev & Alexandra Boltasseva

  7. Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN, USA

    Zhaxylyk A. Kudyshev & Alexandra Boltasseva

  8. Center for Science of Information, Purdue University, West Lafayette, IN, USA

    Zhaxylyk A. Kudyshev

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Ma, W., Liu, Z., Kudyshev, Z.A. et al. Deep learning for the design of photonic structures. Nat. Photonics 15, 77–90 (2021). https://doi.org/10.1038/s41566-020-0685-y

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