Laser & Optoelectronics Progress, Volume. 62, Issue 5, 0530001(2025)

Deep Learning-Based Disordered-Dispersion Miniature Spectrometer

Jiajia Wang1、*, Qianqian Mo1, and Tao Yang1,2
Author Affiliations
  • 1Institute of Advanced Materials, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu , China
  • 2Henan Institute of Flexible Electronics, Zhengzhou 450046, Henan , China
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    Figures & Tables(7)
    DL-based disordered-dispersion miniature spectrometer
    Rough surface of frosted glass
    Speckle intensity under different wavelength monochromatic light illumination. (a) 514 nm; (b) 516 nm; (c) their normalized intensity difference
    Normalized spectra of 8 LEDs
    MLP neural network framework
    Loss function (MSE) decline curve with increasing training epochs
    Spectral reconstruction results of the spectrometer. (a) Reconstruction of 6 narrowband spectra with different wavelength; (b) reconstruction of 2 broadband spectra at different times; (c) reconstruction of 2 close narrowband spectra; (d) comparison of spectral reconstruction using Tikhonov regularization and MLP neural network
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    Jiajia Wang, Qianqian Mo, Tao Yang. Deep Learning-Based Disordered-Dispersion Miniature Spectrometer[J]. Laser & Optoelectronics Progress, 2025, 62(5): 0530001

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    Paper Information

    Category: Spectroscopy

    Received: May. 20, 2024

    Accepted: Jun. 27, 2024

    Published Online: Feb. 21, 2025

    The Author Email:

    DOI:10.3788/LOP241318

    CSTR:32186.14.LOP241318

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