Performance optimization of perovskite solar cells with an automated spin coating system and artificial intelligence technologies

Abstract

Organic–inorganic hybrid perovskite solar cells are promising candidates for application in next-generation solar technologies owing to their high power conversion efficiencies, suitability for deposition on flexible substrates, and low fabrication costs. Despite their potential, optimizing the relative proportions of organic and inorganic compounds in perovskite precursor solutions with appropriate process parameters, such as the coating speed and heating temperature, to achieve stable materials and high conversion efficiencies, remains challenging. Another issue is the performance reproducibility of perovskite solar cells, which often varies even when the same researcher prepares them. In this paper, we present a method for rapidly optimizing the composition of perovskites and the process conditions by integrating an automated spin-coating system with Bayesian optimization. Using only our own data in combination with Bayesian optimization, we adjusted four key parameters: the amounts of methylammonium chloride and lead iodide(II), rotation speed during spin-coating, and heating temperature to form the perovskite layer. After exploring only 0.36% of all the possible combinations, this method afforded a power conversion efficiency of 21.4%, which is higher than the efficiency of 20.5% that was previously achieved manually using the same materials. Time-resolved fluorescence spectra of multiple samples obtained during the Bayesian optimization cycle showed that the carrier lifetime increased as the optimization progressed. The integration of an automated spin-coating system with Bayesian optimization has been shown to be useful for optimizing the composition of perovskite precursor solutions and processing conditions.