NEWS  |  Deep Learning-Based Photonic Circuit Design in Scientific Reports

Date released: March 22, 2019


  •  NEWS   Deep Learning-Based Photonic Circuit Design in Scientific Reports
  • Date:

    February 4, 2019

  • Description:

    MERL researchers developed a novel design method enhanced by modern deep learning techniques for optimizing photonic integrated circuits (PIC). The developed technique employs residual deep neural networks (DNNs) to understand physics underlaying complicated lightwave propagations through nano-structured photonic devices. It was demonstrated that the trained DNN achieves excellent prediction to design power splitting nanostructures having various target power ratios. The work was published in Scientific Reports, which is an online open access journal from Nature Research, having high-impact articles in the research community.

  • Where:

    Scientific Reports, open-access journal from Nature Research

  • MERL Contacts:
  • External Link:

    https://www.nature.com/articles/s41598-018-37952-2

  • Research Areas:

    Artificial Intelligence, Electronic and Photonic Devices, Machine Learning

    •  TaherSima, M., Kojima, K., Koike-Akino, T., Jha, D., Wang, B., Lin, C., Parsons, K., "Deep Neural Network Inverse Design of Integrated Photonic Power Splitters", Nature Scientific Reports, December 2018.
      BibTeX TR2018-180 PDF
      • @article{TaherSima2018dec,
      • author = {TaherSima, Mohammad and Kojima, Keisuke and Koike-Akino, Toshiaki and Jha, Devesh and Wang, Bingnan and Lin, Chungwei and Parsons, Kieran},
      • title = {Deep Neural Network Inverse Design of Integrated Photonic Power Splitters},
      • journal = {Nature Scientific Reports},
      • year = 2018,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2018-180}
      • }