Intensity Dependent Photoluminescence Imaging for In-Line Quality Control of Perovskite Thin Film Processing

Large area fabrication of high-quality polycrystalline perovskite thin films remains one of the key challenges for the commercial readiness of perovskite photovoltaic (PV). To enable high-throughput and high-yield processing, reliable and fast in-line characterization methods are required. The present work reports on a non-invasive characterization technique based on intensity-dependent photoluminescence (PL) imaging. The change in PL intensity as a function of excitation power density can be approximated by a power-law with exponent k, which is a useful quality indicator for the perovskite layer, providing information about the relative magnitudes of radiative and non-radiative recombination. By evaluating k-parameter maps instead of more established PL intensity images, 2D information is obtained that is robust to optically induced artifacts such as intensity variations in excitation and reflection. Application to various half stacks of a perovskite solar cell showcase its ability to determine the importance of the interface between the charge transporting and perovskite layers. In addition, the k-parameter correlates to the bulk passivation concentration, enabling rapid assessment of open-circuit voltage variations in the range of 20 mV. Considering expected improvements in data acquisition speed, the presented k-imaging method will possibly be obtained in real-time, providing large-area quality control in industrial-scale perovskite PV production. Perovskite photovoltaic (PV) show promise as an alternative to silicon solar cells. However, an obstacle that remains to be mastered is the transition from laboratoryscale to manufacturing-scale of PV devices without compromising power conversion efficiency. In this context, a fast, non-invasive, quantitative in-line mapping technique is presented based on intensity-dependent photoluminescence, offering insights into perovskite quality for large-scale production.image