Investigation of the dependence of fill factor and efficiency on gallium-doped silicon wafer characteristics using unsupervised learning

Abstract

This research analyses the performance parameters of bifacial silicon solar cells using the production line data of gallium-doped silicon wafers through unsupervised machine learning models and reports suitable input parameters. Gallium doping offers enhanced electronic properties, mitigating the light-induced degradation commonly seen in boron-doped silicon, thus making it a promising material for high-efficiency solar cells. The impact of silicon wafer thickness, resistivity, and carrier lifetime on fill factor, open-circuit voltage, and efficiency has been investigated. Employing unsupervised learning models, extensive production line data has been analysed to elucidate the complex dependencies between input parameters of silicon wafers (thickness, resistivity, and carrier lifetime) and performance parameters of fabricated solar cells using these silicon wafers (fill factor, open-circuit voltage, and efficiency). The findings indicate significant dependency of performance parameters on input parameters. Random forest classifier model demonstrated robust predictive capabilities, providing valuable insights for optimizing manufacturing processes. Based on analysis with unsupervised learning of data, following conclusions are drawn for input parameters of silicon wafers: (i) thickness range of 160–164 micro-meter gives the highest fill factor, (ii) resistivity range of 0.5–0.8 Ohm-centimetre is superior for higher fill factor, (iii) carrier lifetime of 0.9–1.05 micro-second results in better fill factor. A combination of all the above three parameters works in favour of high fill factor, it may not be a good idea to achieve one and deviate from the others. The results contribute to guiding future material and process optimizations, pushing the boundaries of solar cell efficiency.