OPTOELECTRONIC MODELING AND ANALYSIS FOR TRANSPARENT PEROVSKITE SOLAR CELL IN FOUR-TERMINAL TANDEM APPLICATIONS

Abstract

This PhD thesis aims to optimize the efficiency and cost-effectiveness of transparent perovskite solar cells in four-terminal tandem applications by leveraging machine learning. The research methodology employed Monte-Carlo simulation, genetic algorithms, neural network training, statistical analysis, and optoelectronic modeling to uncover the most sensitive parameters affecting tandem device efficiency. The study ranked these parameters and identified optimal design parameters to improve performance. The research also developed pathways to improve the efficiency of the tandem device through transparent top cell fabrication parameter tuning and accounting for fabrication variability in a multi-objective optimization of a 4T perovskite tandem. Additionally, a machine learning tool was created to non-invasively predict the geometric parameters of the top cell. The findings from this research will contribute to the optimization and development of perovskite tandem solar cells using machine learning, with potential applications for more efficient and cost-effective renewable energy generation.