Deep learning enabled inverse design in nanophotonics

Abstract Deep learning has become the dominant approach in artificial intelligence to solve complex data‐driven problems. Originally applied almost exclusively in computer‐science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the…

• NR
  National Research Foundation

  Awards: 2019R1A5A8080290, 2018M3D1A1058998, NRF-2017H1A2A1043322, NRF-2018M3D1A1058998, NRF-2019R1A5A8080290, CAMM-2019M3A6B3030637
• MO
  Ministry of Science and ICT, South Korea

  Awards: 2019R1A5A8080290, NRF-2019R1A5A8080290, NRF-2019R1A2C3003129