Machine Learning‐Assisted Novel Photovoltaic Optimization for Tailored Ultra‐Thin CdTe‐Based Solar Cells

This study presents a machine learning‐based design framework utilizing deep Q‐learning (DQL) to optimize ultra‐thin CdTe solar cells with active layer thicknesses ranging from 100 to 400 nm. By coupling the transfer matrix method for optical analysis with SCAPS‐1D simulations for electrical modeling, the DQL agent effectively explored the complex parameter space, optimizing the thicknesses of all key layers, including SnO 2 , CdS, CdTe, MoO 3 , and Au. The DQL framework intelligently adjusted each layer based on electromagnetic wave propagation and absorption profiles, enhancing internal reflection and light trapping within sub‐micron geometries. Even at extremely low absorber thicknesses (e.g., 100 nm), the optimized structures achieved high photovoltaic performance, with power conversion efficiencies up to 9.39% and J sc values exceeding 11 mA/cm 2 . At 400 nm, efficiency increased to 15.75% with J sc of 20.86 mA/cm 2 . These results demonstrate that efficient photon harvesting and carrier transport are achievable through full‐stack optimization. External quantum efficiency and absorption spectra confirmed the integrated optical‐electrical enhancement achieved by DQL. This work highlights the capabilities of reinforcement learning in high‐dimensional solar cell design problems and provides a scalable approach for developing next‐generation, lightweight, efficient, and material‐conscious photovoltaic technologies.