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Machine Learning-Assisted Defect Analysis and Optimization for P-I-N-Structured Perovskite Solar Cells

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Abstract

A solar cell capacitance simulator (SCAPS-1D) was used to prepare 3611 cell data with different defect densities in the bulk and interface of p-i-n-structured perovskite solar cells. The training was conducted using four machine learning algorithm models. The random forest algorithm had an accuracy and root mean square error of 0.999 and 0.00306, respectively. As per the explanatory Shapley additive explanations (SHAP) analysis, the bulk defects of the perovskite and the hole transfer layer/perovskite and perovskite/electron transfer layer interface defects greatly affected the power conversion efficiency of the solar cells. It was also confirmed that when the perovskite defect density was low, the cell performance was more sensitive to the interface defect densities. Based on the predictive analysis of machine learning, a strategy to improve the solar cell efficiency of the p-i-n structure was presented, and the efficiency was improved from 17.97% to 24.66% in the poly(triarylamine)/perovskite/phenyl-C61-butyric acid methyl ester structure by optimizing the defect density and resistance. It is expected that this methodology will not only help in identifying the factors affecting the efficiency of perovskite solar cells but also in optimizing the structure of solar cells during the manufacturing process.

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Acknowledgments

This work was supported by the Korea Institute of Industrial Technology (UR-22-0042)

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Authors and Affiliations

  1. Functional Materials and Components R&D Group, Korea Institute of Industrial Technology (KITECH), 47 Hojeo-ro, Wonju-si, Gangwon-do, 26336, Republic of Korea

    Seongtak Kim, Younghun Jeong, Dong-Woon Han & Chan Bin Mo

  2. Department of Materials Science and Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

    Younghun Jeong

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Contributions

SK developed the ML algorithms, wrote the original manuscript, and supervised the experiments. YJ fabricated and evaluated the PSCs. DWH performed the SCAPS simulations using the prepared parameters. CBM supervised all the experiments and simulations and performed an overall review of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Seongtak Kim or Chan Bin Mo.

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Kim, S., Jeong, Y., Han, DW. et al. Machine Learning-Assisted Defect Analysis and Optimization for P-I-N-Structured Perovskite Solar Cells. J. Electron. Mater. 52, 5861–5871 (2023). https://doi.org/10.1007/s11664-023-10533-4

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