Machine Learning-Driven SCAPS Modeling for Optimizing CH3NH3SnBr3 Perovskite Solar Cells: Analytical Insights into
Avijit Ghosh1, Mahbuba Moumita2, Md Aliahsan Bappy3
1Department of Electrical and Electronic Engineering, Begum Rokeya University, Rangpur 5400, Bangladesh.
Abstract:
This work explores the potential for integrating organic compounds, which serve as absorbers, with HTL to achieve steady, efficient PSCs. This study's proposed architecture is made up of ETL, HTL, and a CH3NH3SnBr3 absorber. The effect of thickness, doping, and defect densities of absorber, HTL, and ETL layers and interface defect densities on a solar device's output is investigated utilizing the SCAPS-1D model. The FTO/SnS2/CH3NH3SnBr3/Ni structure has a VOC of 0.991 V, a JSC of 28.796 mA cm-2, a PCE of 23.88%, and an FF of 83.69%. Concerns about stability, rapid oxidation of Sn2+ to Sn4+, and high defect density limit the efficiency of CH3NH3SnBr3-based solar cells. The FTO/SnS2/CH3NH3SnBr3/HTL/Ni structure is investigated to prevent Sn oxidation, increase stability, and improve charge transport for improved performance. The analyzed structure is integrated with BiI3/SnS/WSe2/PTAA/CuS/CuI/C6TBTAPH2/CBTS layers as an HTL, resulting in a maximum VOC of 1.128 V, a JSC of 34.014 mA cm-2, a PCE of 33.70%, and an FF of 87.83% with the FTO/SnS2/CH3NH3SnBr3/CBTS/Ni structure. The performance matrix of the investigated best optimum solar cell was predicted by ML with an accuracy rate of roughly 83.75%. This study's useful design and important discoveries could result in the creation of an inexpensive CH3NH3SnBr3 thin-film solar cell.
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