Utilizing machine learning to enhance performance of thin-film solar cells based on Sb2(SxSe1−x)3: investigating the influence of material properties

Abstract

Antimony chalcogenides (Sb₂(S_xSe_1−x)₃) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb₂(S,Se)₃)-based thin-film solar cell (TFSC), where tungsten disulfide (WS₂) and cuprous oxide (Cu₂O) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage (V_oc), short-circuit current (J_sc), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu₂O/Sb₂(S,Se)₃/WS₂/FTO/Al TFSC. The optimized physical parameters for the Sb₂(S,Se)₃ TFSC lead to impressive PV outputs with an efficiency of 30.18%, V_oc of 1.02 V, J_sc of 33.65 mA cm⁻², and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient (R²) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.