Perovskite Plasticity: Exploiting Instability for Self-Optimized Performance

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.