Semiconductor Physics, Quantum Electronics & Optoelectronics, 26 (1), P. 114-119 (2023).

Utilizing machine learning algorithm in predicting the power conversion efficiency limit of a monolithically perovskites/silicon tandem structure

M. Ganoub1, O. Al-Saban2, S.O. Abdellatif 2* , K. Kirah3, H.A. Ghali2

1The Renewable Energy Postgraduate programme and the FabLab in the Centre for Emerging Learning Technologies
(CELT), The British University in Egypt (BUE), El-Sherouk 11837, Cairo, Egypt
2FabLab in the Centre for Emerging Learning Technologies (CELT), Electrical Engineering Department,
Faculty of Engineering, The British University in Egypt (BUE), El-Sherouk 11837, Cairo, Egypt
3Engineering Physics Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt
* Corresponding author e-mail:

Abstract. Tandem structures have been introduced to the photovoltaics (PV) market to boost power conversion efficiency (PCE). Single-junction cells’ PCE, either in a homojunction or heterojunction format, are clipped to a theoretical limit associated with the absorbing material bandgap. Scaling up the single-junction cells to a multi-junction tandem structure penetrates such limits. One of the promising tandem structures is the perovskite over silicon topology. Si junction is utilized as a counter bare cell with perovskites layer above, under applying the bandgap engineering aspects. Herein, we adopt BaTiO 3 /CsPbCl 3 /MAPbBr 3 /CH 3 NH 3 PbI 3 /c-Si tandem structure to be investigated. In tandem PVs, various input parameters can be tuned to maximize PCE, leading to a massive increase in the input combinations. Such a vast dataset directly reflects the computational requirements needed to simulate the wide range of combinations and the computational time. In this study, we seed our random-forest machine learning model with the 3?10 6 points’ dataset with our optoelectronic numerical model in SCAPS. The machine learning could estimate the maximum PCE limit of the proposed tandem structure at around 37.8%, which is more than double the bare Si-cell reported by 18%.

Keywords:tandem solar cells, numerical modeling, perovskites, random-forest algorithm, crystalline silicon.

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