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Deep Neural Network Inverse Design of Integrated Photonic Power Splitters

Scientific reports, 2019-02, Vol.9 (1), p.1368, Article 1368 [Peer Reviewed Journal]

This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2019 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-37952-2 ;PMID: 30718661

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  • Title:
    Deep Neural Network Inverse Design of Integrated Photonic Power Splitters
  • Author: Tahersima, Mohammad H ; Kojima, Keisuke ; Koike-Akino, Toshiaki ; Jha, Devesh ; Wang, Bingnan ; Lin, Chungwei ; Parsons, Kieran
  • Subjects: Neural networks ; Splitting
  • Is Part Of: Scientific reports, 2019-02, Vol.9 (1), p.1368, Article 1368
  • Description: Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm ) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to smaller than ~ -20 dB) while achieving maximum transmission efficiency above 90% and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-018-37952-2
    PMID: 30718661
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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