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Deep learning enables temperature-robust spectrometer with high resolution

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Abstract

Traditional multi-mode fiber spectrometers rely on algorithms to reconstruct the transmission matrix of the fiber, facing the challenge that the same wavelength can lead to many totally de-correlated speckle patterns as the transfer matrix changes rapidly with environment fluctuations (typically temperature fluctuation). In this manuscript, we theoretically propose a multi-mode-fiber (MMF) based, artificial intelligence assisted spectrometer which is ultra-robust to temperature fluctuation. It has been demonstrated that the proposed spectrometer can reach a resolution of 0.1 pm and automatically reject the noise introduced by temperature fluctuation. The system is ultra-robust and with ultra-high spectral resolution which is beneficial for real life applications.

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Authors and Affiliations

  1. Nanophononics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen University, Shenzhen, 518060, China

    Jiaan Gan  (甘家安), Mengyan Shen  (沈孟延), Xin Xiao  (肖鑫), Jinpeng Nong  (农金鹏) & Fu Feng  (冯甫)

Authors
  1. Jiaan Gan  (甘家安)
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  2. Mengyan Shen  (沈孟延)
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  3. Xin Xiao  (肖鑫)
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  4. Jinpeng Nong  (农金鹏)
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  5. Fu Feng  (冯甫)
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Corresponding authors

Correspondence to Jinpeng Nong  (农金鹏) or Fu Feng  (冯甫).

Additional information

This work has been supported by the National Natural Science Foundation of China (Nos.91750205, U1701661, 61935013, 61975128, 61905147 and 61805165), the Leading Talents Program of Guangdong Province (No.00201505), the Natural Science Foundation of Guangdong Province (Nos.2016A030312010, 2019TQ05X750 and 2020A1515010598), the Science, Technology and Innovation Commission of Shenzhen Municipality (Nos.JCYJ20180507182035270, KQTD2017033011044403, ZDSYS201703031605029, KQTD20180412181324255 and JCYJ2017818144338999), and the Shenzhen University Starting Fund (No.2019073).

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Gan, J., Shen, M., Xiao, X. et al. Deep learning enables temperature-robust spectrometer with high resolution. Optoelectron. Lett. 17, 705–709 (2021). https://doi.org/10.1007/s11801-021-1126-y

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  • DOI: https://doi.org/10.1007/s11801-021-1126-y

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