Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 61, Issue 1, 0123002(2024)

Efficient Photoelectric Coupling Simulation and Machine Learning Study of Perovskite Solar Cells (Invited)

Ruiying Kong1,2, Yijun Wei1,2, Jiacheng Chen1,2, Tianshu Ma1,2, Yaohui Zhan1,2、*, and Xiaofeng Li1,2、**
Author Affiliations
  • 1School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215006, Jiangsu , China
  • 2Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province & Key Lab of Modern Optical Technologies of Education Ministry of China, Suzhou 215006, Jiangsu , China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
    Equations (7)
    References (48)
    Cited By (1)
    Tools
    Paper Information
    Topics
    Figures & Tables(9)
    Combination of intelligent algorithms such as machine learning and multi-physical field design of photovoltaic devices. Neural networks are not only used for forward prediction (blue circles), but also for reverse design (red circles), achieving a comprehensive design from the material, structure, and other parameters to the photoelectric response

    Fig. 1. Combination of intelligent algorithms such as machine learning and multi-physical field design of photovoltaic devices. Neural networks are not only used for forward prediction (blue circles), but also for reverse design (red circles), achieving a comprehensive design from the material, structure, and other parameters to the photoelectric response

    Download full size
    Structure and energy level diagram of perovskite solar cells. (a) Structural diagram; (b) energy level diagram

    Fig. 2. Structure and energy level diagram of perovskite solar cells. (a) Structural diagram; (b) energy level diagram

    Download full size
    Influence of the parameters such as the thickness of each layer of PSCs on their performance. (a) Absorption contour map within the working band, as the thickness of the perovskite layer changes from 450 nm to 900 nm; (b) plot of the percentage stacking area of absorption and reflection of each layer under illumination; (c) contour map of the macroscopic changes in the current-voltage curve, as the electronic lifetime of the perovskite layer increases from 0.0005 μs to 20 μs; (d) effect of the electronic lifetime of the perovskite layer on the device temperature

    Fig. 3. Influence of the parameters such as the thickness of each layer of PSCs on their performance. (a) Absorption contour map within the working band, as the thickness of the perovskite layer changes from 450 nm to 900 nm; (b) plot of the percentage stacking area of absorption and reflection of each layer under illumination; (c) contour map of the macroscopic changes in the current-voltage curve, as the electronic lifetime of the perovskite layer increases from 0.0005 μs to 20 μs; (d) effect of the electronic lifetime of the perovskite layer on the device temperature

    Download full size
    Machine learning models and their predictive effects. (a) Importance ranking of the input features in the decision tree model; (b) underlying decision tree; (c) cross validation results of 50 random forests; (d) learning curves of the random forests during 200 iteration times

    Fig. 4. Machine learning models and their predictive effects. (a) Importance ranking of the input features in the decision tree model; (b) underlying decision tree; (c) cross validation results of 50 random forests; (d) learning curves of the random forests during 200 iteration times

    Download full size
    Neural network model and its prediction results. (a) Neural network structure diagram; (b) loss value measured by MSE during the training and testing process; (c) comparison between the predicted current-voltage curves and those calculated by the optoelectronic coupling model; (d) comparison of current-voltage curves in the literature and current-voltage curves predicted by machine learning models

    Fig. 5. Neural network model and its prediction results. (a) Neural network structure diagram; (b) loss value measured by MSE during the training and testing process; (c) comparison between the predicted current-voltage curves and those calculated by the optoelectronic coupling model; (d) comparison of current-voltage curves in the literature and current-voltage curves predicted by machine learning models

    Download full size
    Regression results between predicted and true values of four important parameters of perovskite solar cells. (a) Short circuit current; (b) open circuit voltage; (c) fill factor; (d) conversion efficiency

    Fig. 6. Regression results between predicted and true values of four important parameters of perovskite solar cells. (a) Short circuit current; (b) open circuit voltage; (c) fill factor; (d) conversion efficiency

    Download full size
    Comparison between the current-voltage curve fitted by the microscopic parameters of the optimized algorithm design and the actual curve

    Fig. 7. Comparison between the current-voltage curve fitted by the microscopic parameters of the optimized algorithm design and the actual curve

    Download full size

    • Table 1. Key material parameters for the optoelectronic coupling simulation

      View table

      Table 1. Key material parameters for the optoelectronic coupling simulation

      ParameterSymbolUnitPerovskiteETLHTL
      Electron affinityχeV3.9342.5
      Band gapEgeV1.553.22.7
      Relative permittivityεr6.593
      Effective DOS for electronNCcm-30.23m09m01×1019
      Effective DOS for holeNVcm-30.29m03m01×1019
      Radiative recombination coefficientCcm3·s-13.27×10-1100

    • Table 2. Comparison between reverse deduction and true values of five structural parameters and nine electrical parameters

      View table

      Table 2. Comparison between reverse deduction and true values of five structural parameters and nine electrical parameters

      ParameterLayerInputInverse design output
      Thickness /nmGlass9793
      FTO342319
      ETL3439
      Perovskite564572
      HTL153142
      Electron mobilityETL0.4580.452
      Perovskite1.611.81
      HTL0.9850.93
      Electronic lifetime /μsETL0.00050.0005
      Perovskite10.115.76
      HTL0.00750.01
      Doping concentrationETL6.79×10126.83×1012
      Perovskite1.71×10161.99×1016
      HTL6.5×10186.12×1018
    Tools

    Get Citation

    Copy Citation Text

    Ruiying Kong, Yijun Wei, Jiacheng Chen, Tianshu Ma, Yaohui Zhan, Xiaofeng Li. Efficient Photoelectric Coupling Simulation and Machine Learning Study of Perovskite Solar Cells (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(1): 0123002

    Download Citation

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

    Category: Optical Devices

    Received: Oct. 30, 2023

    Accepted: Dec. 18, 2023

    Published Online: Feb. 6, 2024

    The Author Email: Yaohui Zhan (yhzhan@suda.edu.cn), Xiaofeng Li (xfli@suda.edu.cn)

    DOI:10.3788/LOP232375

    CSTR:32186.14.LOP232375

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology