4.6 Article

Machine learning aided inverse design for few-mode fiber weak-coupling optimization

Journal

OPTICS EXPRESS
Volume 28, Issue 15, Pages 21668-21681

Publisher

OPTICAL SOC AMER
DOI: 10.1364/OE.398157

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Funding

  1. National Key Research and Development Program of China [2018YFB1801004]
  2. National Natural Science Foundation of China [61675128, 61875049, 61875124, 61935011]
  3. Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20180507183418012, KQJSCX20180328165451777]

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Few-mode fiber (FMF) supporting many modes with weak-coupling is highly desired in mode division multiplexing (MDM) systems. The multi-parameter design of FMF becomes comparably difficult, inaccurate and time-consuming when it comes for complex fiber structures and many high order modes. In this work, we demonstrate a machine learning method using neural network to inversely design the desired FMF based on multiple-ring structure. By using the minimum index difference between adjacent modes as the weak-coupling optimization aim, we realize the inverse design of 4-ring step-index FMFs for supporting 4, 6 and 10 -mode operation, and 6-ring step-index FMF for supporting 20-mode operation. This method provides high-accuracy, high-efficiency and low-complexity for fast and reusable design of optical fibers, including particularly weak-coupling FMF in this work. It can be widely extended to a lot of fibers and has great potential for instantaneous applications in the optical fiber industry. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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