Optoelectronics Letters, Volume. 17, Issue 12, 705(2021)

Deep learning enables temperature-robust spectrometer with high resolution

Jiaan GAN, Mengyan SHEN, Xin XIAO, Jinpeng NONG*, and Fu FENG
Author Affiliations
  • Nanophononics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen University, Shenzhen 518060, China
  • show less

    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.

    Tools

    Get Citation

    Copy Citation Text

    GAN Jiaan, SHEN Mengyan, XIAO Xin, NONG Jinpeng, FENG Fu. Deep learning enables temperature-robust spectrometer with high resolution[J]. Optoelectronics Letters, 2021, 17(12): 705

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Received: Aug. 9, 2021

    Accepted: Aug. 23, 2021

    Published Online: Jan. 10, 2022

    The Author Email: Jinpeng NONG (nongjp@cqu.edu.cn)

    DOI:10.1007/s11801-021-1126-y

    Topics