Laser & Optoelectronics Progress, Volume. 61, Issue 21, 2132001(2024)

Temporal Reconstruction of Femtosecond Pulses Based on Multi-Output Residual Neural Network

Weizhi Lü1,2, Yunfeng Ma1,2、*, Peng Zhao1, Zhe Wang1, Wang Cheng1, Guangyan Guo1, Xuebo Yang1, Chenxuan Yin1,2, Yongjian Zhu1,2, Fang Bai1, Zhixi Zhang1, and Yong Bai1
Author Affiliations
  • 1Optical Engineering Research Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China
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    Figures & Tables(17)
    Schematic of GRENOUILLE setup. (a) Top view; (b) side view
    Measurement experiment setup for SHG-FROG trace diagram
    Partial results measured by GRENOUILLE (from left to right are SHG-FROG trace diagrams, time domain inversion results, and frequency domain inversion results). (a) Central wavelength is 1030 nm, pulse width is 220 fs; (b) central wavelength is 1030 nm, pulse width is 360 fs; (c) central wavelength is 700 nm, pulse width is 350 fs; (d) central wavelength is 1030 nm, pulse width is 585 fs
    Complete process of proposed algorithm
    Effective and ineffective SHG-FROG trace diagrams (from left to right are SHG-FROG trace diagrams, retrieved trace diagrams, and algorithm inversion results). (a) Effective trace diagram; (b) ineffective trace diagram, exceeding the instrument's range; (c) ineffective trace diagram caused by excessive noise
    Pulse reconstruction structure using an optimized MO-ResNet
    Reconstruct results of SHG-FROG trace diagram. (a) SHG-FROG trace diagram; (b) temporal intensity and phase reconstructed by RANA; (c) temporal intensity and phase inverted by MO-ResNet
    SHG-FROG trace diagrams adding salt and pepper noise with different intensities
    Training loss of different models with training rounds. (a) Trace diagram recognition model; (b) MO-ResNet reconstruction model
    Performance comparison of pulse reconstruction algorithm on different trace diagrams. Original trace diagrams with actual pulse width of (a1) 284 fs, (a2) 300 fs, (a3) 408 fs, (a4) 515 fs; reconstruction results with actual pulse width of ( b1) 284 fs, (b2) 300 fs, (b3) 408 fs, (b4) 515 fs
    Comparison of reconstruction errors between two algorithms under different pulse widths
    • Table 1. Model building environment

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      Table 1. Model building environment

      Environmental nameContent
      Operating systemWindows 10
      Central processing unitIntel i7-12700H
      EnviromentJupyter
      FrameworkTorch 2.0.1
      GPUGeforce 3060
      LanguagePython 3.9
    • Table 2. SHG-FROG trace diagram recognition model performance index in test set

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      Table 2. SHG-FROG trace diagram recognition model performance index in test set

      ItemPrecision /%Recall /%F1-score /%Sample quantity
      Negative(ineffective trace diagram)891009425
      Positive (effective trace diagram)1009899136
      Macro average959997161
      Weighted average989898161
    • Table 3. SHG-FROG trace diagram recognition model confusion matrix

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      Table 3. SHG-FROG trace diagram recognition model confusion matrix

      Image classRecognize result
      NegativePositive
      Negative (ineffective trace diagram)25.000.00
      Positive (effective trace diagram)3.00133.00
    • Table 4. Comparison of SHG-FROG trace retrieval effects

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      Table 4. Comparison of SHG-FROG trace retrieval effects

      Trace diagramExecution time /sFull width at half-maximum /fsRelative error of full width at half-maximum /%RMSE between RANA and MO-ResNet
      RANAMO-ResNetIntensityPhase /rad
      Pulse 10.039240.9251.24.20.09402.013
      Pulse 20.036248.6260.54.80.08972.585
      Pulse 30.038275.3284.73.40.08793.730
      Pulse 40.033260.4276.35.40.09402.922
      Pulse 50.038262.0277.35.10.09504.024
    • Table 5. Comparison of average errors between two algorithms in test set

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      Table 5. Comparison of average errors between two algorithms in test set

      Pulse width /fsMean execution time of MO-ResNet /sRelative error of full width at half-maximum /%
      RANAMO-ResNet
      240~2800.0332.93.0
      280~3200.0353.72.6
      320~3600.0313.22.6
      360~4000.0363.53.5
    • Table 6. Comparison of inversion results for MO-ResNet and RANA before and after adding noise

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      Table 6. Comparison of inversion results for MO-ResNet and RANA before and after adding noise

      NoisyRMSE of intensity
      MO-ResNetRANA
      Amount 0.050.004320.0302
      Amount 0.100.004590.0325
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    Weizhi Lü, Yunfeng Ma, Peng Zhao, Zhe Wang, Wang Cheng, Guangyan Guo, Xuebo Yang, Chenxuan Yin, Yongjian Zhu, Fang Bai, Zhixi Zhang, Yong Bai. Temporal Reconstruction of Femtosecond Pulses Based on Multi-Output Residual Neural Network[J]. Laser & Optoelectronics Progress, 2024, 61(21): 2132001

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    Paper Information

    Category: Ultrafast Optics

    Received: Feb. 1, 2024

    Accepted: Mar. 21, 2024

    Published Online: Nov. 12, 2024

    The Author Email: Yunfeng Ma (mayf100612@aircas.ac.cn)

    DOI:10.3788/LOP240653

    CSTR:32186.14.LOP240653

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