In Situ, High-Throughput Optical Monitoring of Spray-Coated Perovskite Photovoltaics Under Thermal Stress

Hybrid organic-inorganic perovskite thin-film solar cells are of great interest as the light-absorbing perovskite layer can be easily deposited via simple solution processing, allowing for low-cost photovoltaic manufacturing. Under operation, prolonged exposure to UV light and elevated temperatures are known to cause premature device failure. Failure mechanisms include the degradation of the perovskite absorber itself and chemical reactions between device layers. Current research focuses on tracking device performance over time which provides an estimate of the degradation timescale. This approach is slow and requires numerous tedious measurements. Therefore, a high throughput characterization method is needed to quantify rapidly the thermal stability of perovskite devices. Optical imaging is an efficient method to screen for stable perovskite absorber compositions but has not yet been applied at a device or module scale.<br/><br/>In this work, we developed an optical imaging platform inspired by previous work [1] to monitor the thermal aging (85°C) of unencapsulated spray-coated perovskite solar cells (glass/ITO/NiO/CsFAPbI₃/C₆₀/BCP/Ag) in an inert atmosphere. The importance of studying full cell structures that include all charge transport and electrode layers, together with the constraint on both sides of the cell are included. Our primary goal is to identify the device structures and processing parameters of scalable deposition methods such as spray-coating that minimize photovoltaic devices degradation under thermal stress. This work includes the design of the system and the development of an automated Python image processing algorithm. During aging of devices at 85°C, the RGB and LAB (lightness, red-green and yellow-blue scales respectively) color space values were extracted and averaged over the device active area. This analysis was applied to time series of images acquired during thermal aging to determine the evolution of RGB and LAB values over time.<br/><br/>Upon aging, we observed an overall device lightening and yellowing in certain areas with rates quantified by the respective increase of the L and B mean values. X-Ray diffraction (XRD) confirmed that the yellowing was linked to an increased quantity of lead iodide (PbI₂) or of the non-photoactive FAPbI₃ tetragonal (yellow) phase. The monitoring of the L and B distributions over each active area also informed us of the spatial homogeneity of the degradation process. We distinguished homogeneous degradation and defect-induced degradation using the distribution skewness values. We compared the color evolution of devices fabricated using different plasma curing parameters (height, duty cycle), as well as the effect of including additional barrier layers (e.g. phenylammonium iodide) in the device structure. Finally, we demonstrated the scalability of our imaging method through the thermal aging of perovskite mini-modules. This work provides a high throughput optical imaging platform to quantify the effect of thermal stress on perovskite photovoltaics. The resulting high-fidelity visual data set provides insights on perovskite degradation mechanisms with high temporal and spatial resolutions.<br/><br/><br/>[1] Hartono, N.T.P., Thapa, J., Tiihonen, A. et al. How machine learning can help select capping layers to suppress perovskite degradation. Nat Commun 11, 4172 (2020).