• Optoelectronics Letters
  • Vol. 17, Issue 12, 705 (2021)
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
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    DOI: 10.1007/s11801-021-1126-y Cite this Article
    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 Copy Citation Text show less

    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.
    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
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