Insights into the Effects of RbF‐Post‐Deposition Treatments on the Absorber Surface of High Efficiency Cu(In, Ga)Se2 Solar Cells and Development of Analytical and Machine Learning Process Monitoring Methodologies Based on Combinatorial Analysis. Issue 8 (9th January 2022) - Fonoll‐Rubio, Robert; Paetel, Stefan; Grau‐Luque, Enric; Becerril‐Romero, Ignacio; Mayer, Rafael; Pérez‐Rodríguez, Alejandro; Guc, Maxim; Izquierdo‐Roca, Victor
Abstract: The latest record efficiencies of the Cu(In, Ga)Se2 (CIGSe) photovoltaic technology are driven by heavy alkali post‐deposition treatments (PDTs). Despite their positive effect, especially in the V oc of the devices, their underlying mechanisms are still under discussion. This work sheds light on this topic by performing a high statistics analysis on 620 high efficiency CIGSe solar cells submitted to four different PDT processes (different RbF source temperature) employing a combinatorial approach based on Raman and photoluminescence (PL) spectroscopies. This reveals with statistical confidence subtle differences in the spectroscopic data that relate to the redistribution of defects between the ordered vacancy compound (OVC) and the chalcopyrite phases at the absorber surface. In particular, there is an intertwined decrease of the OVC phase and increase of the so‐called "defective chalcopyrite phase." Additionally, two industry‐compatible methodologies for the assessment of the RbF‐PDT process and prediction of the V oc of the final devices with a ± 2% error and an efficacy of ≈ 95% are developed based on analytical and machine learning approaches. These results show that the combined Raman and PL spectroscopic techniques represent a powerful tool for the future development of the CIGSe technology at a research level and for more efficient industrial manufacturing. Abstract : This work provides novel insights into the effect of RbF‐post‐deposition treatments on the Cu(In, Ga)Se2 absorber surface, and shows the potential of combinatorial analysis based on spectroscopic techniques and machine learning for process monitoring in industrial solar cell manufacturing through device performance prediction at early fabrication stages.