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Optimization of absorber layer thicknesses for enhanced efficiency and stability in lead-free perovskite/silicon tandem solar cells

Author

Listed:
  • Subash, T.D.
  • Ganesh Babu, R.
  • Vimala Josphine, C.
  • Subha, T.D.

Abstract

Perovskite-based tandem solar cells have become attractive options for effective photovoltaic applications due to their strong optical absorption and adjustable bandgap. Nonetheless, challenges like instability, scalability issues, and material degradation hinder their commercial deployment. This study proposes a new approach to develop the stability and efficiency of lead-free perovskite/silicon tandem solar cells by optimizing absorber layer thickness, material selection, and key performance parameters. The proposed method integrates the Quantum Kernel Self-Attention Network with the Narwhal Optimizer, where the neural network model predicts the perovskite bandgap and the algorithm optimizes absorber layer thickness for maximum performance. Implemented in MATLAB, the approach is benchmarked against existing techniques, such as the Artificial Neural Network and the Chaotic African Vultures Optimization Algorithm, and Genetic Algorithm-Particle Swarm Optimization, demonstrating superior outcomes in terms of power conversion efficiency and device reliability. The proposed approach achieves a conversion efficiency of 26.68 %, outperforming benchmark methods. By optimizing parameters such as bandgap alignment, doping, and thermal stability, the tandem configuration achieves a Voc of 1.87 V, a Jsc of 16.44 mA/cm2, and a fill factor of 86.8 %, indicating its potential for efficient and stable solar energy applications.

Suggested Citation

  • Subash, T.D. & Ganesh Babu, R. & Vimala Josphine, C. & Subha, T.D., 2026. "Optimization of absorber layer thicknesses for enhanced efficiency and stability in lead-free perovskite/silicon tandem solar cells," Renewable Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:renene:v:257:y:2026:i:c:s0960148125023067
    DOI: 10.1016/j.renene.2025.124642
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    References listed on IDEAS

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