Photovoltaics is and will further become a major pillar for a clean and sustainable energy supply, a necessity given the rapidly progressing negative impacts of climate change. Beyond the conventional silicon solar cells, thin film technologies are highly promising due to reduced use of material and energy during production. Here, perovskite solar cells, which emerged just less than 15 years ago appear to be highly promising. Despite simple processing from solution, solar cells with efficiencies larger than 25% have been realized. One main reason is the material’s tolerance against defects and further unprecedented outstanding optoelectronic properties. However, both solar cells and light-emitting diodes suffer from “instabilities”, which are the main hurdle for their commercialization. Intriguingly, some of these “instabilities” are reversible, meaning that for instance degradation under illumination is followed by (partial) recovery when the solar cell rests in the dark.
These reversible effects mainly originating from ionic defects in the material are topic of OptEIon. The project focusses on advancing characterization and modelling of these devices. Here, one major challenge is the ambiguity of reported results regarding the quantity of these ionic defects. Since the feature sizes of the involved structures are below one micron but dominating the overall performance, nanoscale characterization is employed based on advanced microscopy. The goal is to establish a link between processes on the nanoscale and device performance. On the modeling front, we intend to merge materials science with data science by employing machine learning approaches to complement physics-based modeling. The goal here is to provide a smart tool that assists scientists working in the lab.
In a final step, we want to investigate how these commonly undesired reversible effects can be exploited in novel devices such as memristors, which show features similar to synapses. Such memristors are supposed to be essential for efficient future processors based on neuromorphic computing.
The insights gained in this project will contribute to more stable perovskite solar cells and light emitting diodes. Therefore, they contribute to the chapter Secure, clean and efficient energy in the Horizon 2020 work program. Furthermore, the new AI-based approaches in modelling and the novel devices fabricated are key ingredients to move the digitization in science and society forward.
