Multimodal Photoluminescence Imaging of Halide Perovskite Materials and Solar Cells
Stefania Cacovich a
a Institut Photovoltaïque d'Ile-de-France (IPVF), UMR 9006, CNRS, Ecole Polytechnique, IP Paris, Chimie Paristech, PSL, 91120 Palaiseau, France
Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)
G3 Stability Challenges and Solutions in metal halide Perovskites materials
Barcelona, Spain, 2026 March 23rd - 27th
Organizers: Andres Fabian Gualdron Reyes, Sofia Masi and Teresa S. Ripolles
Invited Speaker, Stefania Cacovich, presentation 192
Publication date: 15th December 2025

Luminescence-based optical imaging is a powerful, contactless, and non-destructive tool for investigating halide perovskite materials and solar cells across all stages of their development. In this talk, I will present how multimodal photoluminescence techniques enable microscopic, quantitative insights into perovskite stability, optoelectronic properties, and device performance. First, I will examine the response of halide perovskites to external stressors such as humidity and X-ray illumination, highlighting the emergence of self-healing behaviour after irradiation. I will then demonstrate how quantitative photoluminescence imaging links fundamental optoelectronic properties to device-level performance, with particular emphasis on interfacial passivation using organic cations to suppress non-radiative recombination. These methods are also extensible to the characterization of larger devices. We demonstrate this by applying a quantitatively calibrated hyperspectral PL/EL imaging approach to a 64 cm² perovskite mini-module, enabling spatially resolved EQE maps derived from EL measurements. This multimodal method reveals recombination hotspots, transport bottlenecks, and series-resistance variations that drive FF and PCE heterogeneity, highlighting key upscaling losses in interconnected modules. Finally, I will introduce an unsupervised deep-learning framework that overcomes noisy data and long acquisition times of time-resolved fluorescence imaging (TR-FLIM). By combining Noise2Noise training with physics-informed modelling, the method yields high-fidelity lifetime maps from short exposures, reducing sample degradation while enabling accurate extraction of bulk and surface recombination parameters. It is therefore suitable to performe operando experiment of fragile materials. 

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