Optimization of perovskite solar cell with MoS2-based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm

Abstract

This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS₂) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L₂₇ Taguchi grey relational analysis (GRA) based genetic algorithm (GA). Numerical simulation of the device is carried out by employing one-dimensional solar cell capacitance simulator (SCAPS-1D) while the optimization procedures are developed based on combination of multiple methods; L₂₇ Taguchi orthogonal array, GRA, multiple linear regression (MLR), and GA. The results of post-optimization reveal that the most optimum layer parameters for improved FF and PCE are predicted as follows; SnO₂F thickness (0.855 μm), SnO₂F donor concentration (9.206×10¹⁸ cm^-3), TiO₂ thickness (0.011 μm), TiO₂ donor concentration (9.306×10¹⁶ cm^-3), CH₃NH₃PbI₃ thickness (0.897 μm), CH₃NH₃PbI₃ donor concentration (0.906×10¹³ cm^-3), MoS₂ thickness (0.154 μm), and MoS₂ acceptor concentration (9.373×10¹⁷ cm^-3). Both FF and PCE of the device are improved by ~1.1% and ~12.6% compared to the pre-optimization.