• Chinese Journal of Lasers
  • Vol. 46, Issue 7, 0701001 (2019)
Pei Feng and Yu Li*
Author Affiliations
  • School of Information Science and Engineering, Shandong University, Qingdao, Shandong 266237, China
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    DOI: 10.3788/CJL201946.0701001 Cite this Article Set citation alerts
    Pei Feng, Yu Li. Semiconductor Laser Parameter Inverse Design Method Based on Artificial Neural Network and Particle Swarm Optimization[J]. Chinese Journal of Lasers, 2019, 46(7): 0701001 Copy Citation Text show less
    Structural diagram of BP network with hidden layer
    Fig. 1. Structural diagram of BP network with hidden layer
    Schematic of overall inverse design process
    Fig. 2. Schematic of overall inverse design process
    Training error curve
    Fig. 3. Training error curve
    Output power spectra obtained by TWM numerical simulation and neural network for sample data
    Fig. 4. Output power spectra obtained by TWM numerical simulation and neural network for sample data
    Comparison of output power spectra obtained by TWM simulation and neural network for test data
    Fig. 5. Comparison of output power spectra obtained by TWM simulation and neural network for test data
    Schematic of searching process in PSO algorithm
    Fig. 6. Schematic of searching process in PSO algorithm
    Comparison between inverse design power spectra and target power spectrum. (a) Comparison among two sets of inverse design power spectra, numerical inverse power spectrum, and target power spectrum; (b) deviation of two sets of inverse design power spectra and numerical inverse power spectrum compared with target power spectrum
    Fig. 7. Comparison between inverse design power spectra and target power spectrum. (a) Comparison among two sets of inverse design power spectra, numerical inverse power spectrum, and target power spectrum; (b) deviation of two sets of inverse design power spectra and numerical inverse power spectrum compared with target power spectrum
    n10152025303540
    MSE /mW3.201.810.550.490.340.280.17
    Table 1. Comparison of training error between different network structures
    ParemeterValue
    I /mA28486888108128
    WO /mW6.8112.1317.1121.3523.7321.32
    WN /mW7.2312.5516.9921.1323.9021.53
    E /%6.03.40.71.00.70.9
    Table 2. Comparison of partial output data between TWM simulation and neural network for sample data
    ParemeterValue
    I /mA28486888108128
    WT /mW6.6112.4517.6721.4622.0115.91
    WN /mW6.5712.7818.3322.0621.7816.35
    E /%0.62.73.72.81.02.8
    Table 3. Comparison of partial output data between TWM simulation and neural network for test data
    ParameterValue
    I /mA28486888108128
    WT /mW6.0110.4915.3418.4418.7211.24
    WS /mW6.6312.2017.0019.8319.2413.08
    W1 /mW6.6111.0615.2218.4118.5512.32
    W2 /mW6.0411.1315.3318.4918.6112.13
    Table 4. Comparison of partial data between target power spectrum and inverse design power spectra
    ParameterηeffcRt /(K·J-1)RsKe /KKg /KKn /Kl /cm-1
    ST0.596.69×10815.19304.58105.9118.2523.80
    S10.807.68×10815.69317.8594.0869.9846.22
    S20.787.08×10820.00325.6196.49108.7345.31
    Table 5. Comparison of parameters between target power spectrum and two sets of inverse design power spectra
    Pei Feng, Yu Li. Semiconductor Laser Parameter Inverse Design Method Based on Artificial Neural Network and Particle Swarm Optimization[J]. Chinese Journal of Lasers, 2019, 46(7): 0701001
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