Performance analysis and optimization of perovskite solar cell using SCAPS-1D and genetic algorithm

Due to their excellent photovoltaic properties and compatibility with large-scale deposition processes, perovskite solar cells (PSCs) are gaining massive attention as next-generation photovoltaic devices. However, despite significant advances in their development, a non-radiative recombination loss remained a challenge to their progress. Optimization of the different layers' thickness and the defect density of the perovskite layer are among the critical factors for reducing non-radiative recombination loss. In the present paper, a typical PSC is modeled and optimized using one-dimensional solar cell capacitance simulation (SCAPS-1D) software along with a genetic algorithm (GA). The main aim of the study is to improve PCE as a function of four parameters in PSC, namely; perovskite layer thickness, perovskite layer defect density, TiO2 layer thickness, and Spiro-OMeTAD layer thickness using a novel approach, SCAPS-1D along with response surface methodology (RSM) and GA. The Box-Behnken Design (BBD) of RSM is used to develop an objective function for the GA based on the SCAPS-1D simulation results. GA is trained and tested on MATLAB software. The paper concludes that the maximum PCE for perovskite layer thickness, TiO2 layer thickness, Spiro-OMeTAD layer thickness, as well as the defect density of the perovskite layer, occurs at 985 nm, 40 nm, 148 nm, and (1.000e+12)1/cm3 respectively. The results obtained by the GA and SCAPS-1D software have shown an excellent agreement. Compared to other parameters, the defect density of the perovskite layer significantly affects the PCE.