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.
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