Lead-halide perovskite (LHP) nanocrystals have proven themselves as an interesting material platform due to their easy synthesis and compositional versatility, allowing for a tunable band gap, strong absorption, and high photoluminescence quantum yield (PLQY). This tunability and performance make LHP nanocrystals interesting for optoelectronic applications. Patterning active materials like these is a useful way to expand their tunability and applicability as it may allow more intricate designs that can improve efficiencies or increase functionality. Based on a technique for II–VI quantum dots, here we pattern colloidal LHP nanocrystals using electron-beam lithography (EBL). We create patterns of LHP nanocrystals on the order of 100s of nanometers to several microns and use these patterns to form intricate designs. The patterning mechanism is induced by ligand cross-linking, which binds adjacent nanocrystals together. We find that the luminescent properties are somewhat diminished after exposure, but that the structures are nonetheless still emissive. We believe that this is an interesting step toward patterning LHP nanocrystals at the nanoscale for device fabrication.

Halide perovskites display outstanding photoluminescence quantum yield, tunable emission, and simple deposition, which make them attractive for optoelectronics. At the same time, their facile ion migration and transformation under optical, electrical, and chemical stress are seen as a major limitation. Mixed halide perovskites are particularly problematic since optical excitation can cause changes in the bandgap that are detrimental for solar cell and light-emitting diode efficiency and stability. In this work, instead of preventing such changes, photo-induced halide segregation in perovskites is exploited to enable responsive, reconfigurable, and self-optimizing materials. The mixed halide perovskite film is trained to give directional light emission using a nanophotonic microlens; through a self-optimized process of halide photosegregation, the system mimics the training stimulus. Longer training leads to more highly directional emission, while different halide migration kinetics in the light (fast training) and dark (slow forgetting) allows for material memory. This self-optimized material performs significantly better than lithographically aligned quantum dots because it eliminates lens-emitter misalignment and automatically corrects for lens aberrations. The system shows a combination of mimicking, improving over time, and memory, which comprise the basic requirements for learning, and allow for the intriguing prospect of intelligent optoelectronic materials.

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