Boosting the optimization process of perovskite solar cells by partial sampling and kriging method

Lead halide perovskite solar cells (PVSCs) have been exhibiting high efficiency by using abundant materials and availability on flexible substrates with easy fabrication process. However, achieving the high-efficiency PVSCs is not easy due to multi-layered structure, difficulty to control the surface uniformity and crossover effects of controlling parameters, so the fast optimization process is very important for improving them. Here, we report the way for improving the optimum condition of perovskite layer by using a combination of the design of experiment (DoE), the interpolation prediction method and genetic algorithm (GA) optimization, which can reduce the cost and time consumed for experiments. To understand the effect of parameters we also characterized the property of materials such as crystalline structure. After the optimization and the characterization, we found the important factor to increase the efficiency of PVSCs and obtained efficiency at 8.87% through only 12 experimental samplings.