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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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.).
The authors declare no competing interests.
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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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