High-throughput Optimizing Halide Perovskite Solar Cells Processed by Photo-irradiation

Perovskite solar cells (PSCs) have leapfrogged many photovoltaics in the past decade due to their excellent optoelectronic properties and solution processability. With over 25% power conversion efficiency (PCE), PSCs have reached a laboratory performance suitable for commercial applications. Successful commercializing PSCs will, however, be contingent upon the development of a low-cost film processing method that is suitable for large-scale production and rigorous study of the materials’ properties processed by these unconventional processes. Despite much research has focused on scalable deposition technologies of halide perovskite, the post-deposition process, a critical step of converting the precursor into the photo-active phase and determining the film quality is often overlooked. This is because in laboratories, the postdeposition process is easily done on a hotplate, which takes 10 min at 100 °C. However, in a perovskite solar panel manufacturing line, conventional thermal annealing would lead to two problems: 1. The lengthy annealing time limits the throughput. 2. As the solar panel area increases, the oven size will become impractically large, and the energy loss would increase tremendously due to the equilibrium heating of the devices as well as the surrounding environment. Herein, we address these challenges by using photo-irradiance, rather than heat conduction or convection for post-deposition process. By using flash lamp annealing, often called intense pulsed light sintering or photonic curing, the conversion can be reduced from 10 min to 20 ms because of the higher annealing temperature based on the Arrhenius Law. More than that, the intense pulsed light is a selective heating process, only the perovskite precursor films absorb most of the radiant energy, reducing the energy loss. The goal is threefold. First step is to develop a high-throughput photo-irradiance method that delivers decent PSCs, which will be covered in Chapter 2. Second step is to study the crystallization mechanism during the photoirradiance and to make the PCE comparable to PSCs made by the conventional thermal annealing, which will be mainly discussed in Chapter 3 and 4. Third is to establish a machine learning optimization process for a high-dimensional process such as a photo-irradiance process to quickly transfer the knowledge from rigid PSCs fabrication to help make flexible PSCs, which will be focused in Chapter 5. After all, we have demonstrated that photonic curing is a viable tool for high-throughput fabrication of PSCs, which is a crucial advancement toward PSC commercialization. Additionally, with the assistance of machine learning, optimizing photonic curing on perovskite as well as many other materials is becoming much easier, streamlining the development of this technology on many fields.