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Halide perovskite materials have promising performance characteristics for low-cost optoelectronic applications. Photovoltaic devices fabricated from perovskite absorbers have reached power conversion efficiencies above 25 per cent in single-junction devices and 28 per cent in tandem devices. Though widely considered defect tolerant materials, perovskites still exhibit a sizeable density of deep sub-gap non-radiative trap states, which create local variations in photoluminescence that fundamentally limit device performance. These trap states have also been associated with light-induced halide segregation in mixed halide perovskite compositions and local strain, both of which can detrimentally impact device stability5. Understanding the nature of these traps will be critical to ultimately eliminate losses and yield devices operating at their theoretical performance limits with optimal stability.
In this talk we outline the distribution and compositional and structural origins of non-radiative recombination sites in (Cs0.05FA0.78MA0.17)Pb(I0.83Br0.17)3 thin films (Doherty, Winchester, et al., Nature, 2020). By combining scanning electron and synchrotron X-Ray microscopy techniques with photoemission electron microscopy (PEEM) measurements we reveal that nanoscale trap clusters are distributed non-homogenously across the surface of high performing perovskite films and that there are distinct structural and compositional fingerprints associated with the generation of these detrimental sites. Finally, we will show how combining our scanning electron diffraction with convolutional neural networks can enable low-dose (~6 e/Å2), high-resolution (4nm) automated structural phase identification in beam sensitive thin-film perovskites. This nanoscale insight will help answer ongoing open questions in the field such as “What are the nanoscale origins of instability in perovskite devices?”, “how important is phase purity for performance?”
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