Machine Learning Drives a Path to Defect Engineering for Suppressing Nonradiative Recombination Losses in CuZnSn(S,Se) Solar Cells.

Journal: ACS applied materials & interfaces
Published Date: Jun 6, 2025

Abstract

Recent kesterite developments encouraged researchers to use CuZnSn(S,Se) (CZTSSe)-based photoabsorber materials in diverse optoelectronic applications. However, the detrimental bulk and interface defects induced high carrier recombination at corresponding regions, stagnating further improvement in the performance of kesterite solar cells. In this work, a machine learning (ML)-guided strategy is employed to optimize the amount of germanium (Ge) incorporation to enhance the baseline performance of CZTSSe. The ML model indicates that incorporation of <5% Ge concentration is optimal for achieving higher device performance and reducing open-circuit voltage () loss. It also revealed that engineering defects controlling carrier density plays a crucial role in achieving high-quality devices. Building upon the optimized Ge incorporation, silver (Ag) is subsequently introduced to further passivate shallow-level copper (Cu)-related defects. Experimental validation confirms that Ge incorporation effectively improves the device performance by suppressing deep-level defects in both the space-charge region and the quasi-neutral region. As a result, an improved carrier separation process, minority carrier lifetime, and reduced nonradiative carrier recombination losses increased device performance by more than 20%. Finally, the champion device with double-cation incorporation of Ag and Ge in the vacuum-proceeded CZTSSe absorber layer delivers enhanced device performance from 9.11 to 11.32%.

Authors

  • Vijay C Karade
    Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju 61186, Republic of Korea.
  • Kuldeep Singh Gour
    Corrosion & Coating Group, Advanced Materials and Corrosion (AMC) Division, CSIR-National Metallurgical Laboratory, Jamshedpur, Jharkhand 831007, India.
  • Mingrui He
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia.
  • Junsung Jang
    Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju 61186, Republic of Korea.
  • Temujin Enkhbat
    Department of Physics, Incheon National University, Incheon 22012, Republic of Korea.
  • Minwoo Lee
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
  • Junho Kim
  • Santosh S Sutar
    Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur 416004, India.
  • Tukaram D Dongale
    Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India.
  • Randy J Ellingson
    Wright Center for Photovoltaics Innovation and Commercialization, Department of Physics and Astronomy, The University of Toledo, Toledo, Ohio 43606, United States.
  • Jae Sung Yun
    Department of Electrical and Electronic Engineering, Advanced Technology Institute (ATI), University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom.
  • Jin Hyeok Kim
    Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju 61186, Republic of Korea.
  • Jongsung Park
    Department of Energy Engineering, Department of Energy System Engineering, Gyeongsang National University, Jinju 52849, Republic of Korea.

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