Comprehensive experimental and numerical optimization of surface morphology of transparent conductive oxide films for tandem thin film photovoltaic cells

Typical thin-film photovoltaic cells incorporate a textured transparent conductive oxide to efficiently harvest solar energy. What should be the ideally desired morphology of this textured surface for best cell performance - is highly debated but remains an unsolved mystery. We present a comprehensive methodology to 1) accurately model, 2) extract macroscopically sufficient statistical finger-prints and 3) predict best desired values for these statistical finger-prints of such a randomly textured surface for the best performance of the cell.