Deep Neural Networks for Inverse Design of Nanophotonic Devices


Deep learning is now playing a major role in designing photonic devices, including nanostructured photonics. In this paper, we investigate three models for designing nanophonic power splitters with multiple splitting ratios. The first model is a forward regression model, wherein the trained deep neural network (DNN) is used within the optimization loop. The second is an inverse regression model, in which the trained DNN constructs a structure with the desired target performance given as input. The third model is a generative network, which can randomly produce a series of optimized designs for a target performance. Focusing on the nanophotonic power splitters, we show how the devices can be designed by these three types of
DNN models.


  • Related Publication

  •  Kojima, K., Koike-Akino, T., "ニューラルネットワークを⽤いたナノ光素⼦設計", OplusE, No. 475, September 2020.
    BibTeX OplusE
    • @article{Kojima2020sep2,
    • author = {Kojima, Keisuke and Koike-Akino, Toshiaki},
    • title = {ニューラルネットワークを⽤いたナノ光素⼦設計},
    • journal = {OplusE},
    • year = 2020,
    • number = 475,
    • month = sep,
    • url = {}
    • }