Machine and deep Learning-Powered analysis of photovoltaic properties in 4-terminal FASnI3/CIGS tandem solar cells

Abstract

• A new SCAPS-MDL-based approach was proposed to investigate 4 T FASnI 3 /CIGS tandem TFSCs. • Key photovoltaic performance metrics were evaluated with different structural configurations. • The results show distinct contributions of the top and bottom cells to overall efficiency. • Machine-Deep Learning analysis is a powerful tool for guiding 4-T tandem TFSC fabrication. This work presents a comprehensive numerical and machine learning-based analysis of lead-free four-terminal (4 T) FASnI 3 /CIGS tandem thin-film solar cells. Using SCAPS-1D, we evaluated the photovoltaic performance of the top and bottom sub-cells under various material and structural configurations. The FASnI 3 top cell and CIGS bottom cell were optimized individually, achieving power conversion efficiencies (PCE) of 18.80 % and 15.47 %, respectively. Machine learning (ML) and deep learning (DL) approaches were employed to identify key performance-influencing parameters. Feature importance analysis revealed that the buffer layer donor density significantly impacts the Jsc and fill factor of the bottom sub-cell, while the top sub-cell’s performance is predominantly governed by the electron transport layer and perovskite properties. Despite higher complexity in the top cell’s behavior, attributed to environmental variability, the ML/DL framework effectively pinpointed the most critical design factors. These findings contribute to the accelerated development of high-efficiency and sustainable lead-free tandem solar cells.