Experiment Calibrated and Uncertainty Aware Benchmarking Framework for Lead Tin Perovskite Solar Cells

Abstract

Pb–Sn mixed perovskite solar cells offer narrow bandgaps suitable for high efficiency photovoltaic applications, yet numerical simulations often overestimate experimental performance due to neglected defect and interface losses. In this work, an experiment calibrated and uncertainty aware benchmarking framework for Pb–Sn perovskite solar cells is developed using drift diffusion modeling combined with Monte Carlo sampling. Series resistance and interfacial recombination parameters are calibrated against twelve experimentally reported devices to ensure quantitative agreement. Global sensitivity analysis based on Sobol indices and partial rank correlation coefficients identifies bulk trap density as the dominant efficiency limiting factor, contributing more than 70% of the total performance variance, followed by interfacial recombination. Statistical yield analysis reveals that efficiencies above 20% are only probabilistically achievable under strict trap suppression and optimized band alignment conditions. The framework provides reproducible, population level design rules for Pb–Sn perovskite devices and establishes a reliable modeling platform for performance prediction and manufacturability assessment.