SPLTRAK Abstract Submission
Fill Factor Prediction of Modern Industrial Cells: Potential Gaps and Improvements
Gaia Maria N Javier, Priya Dwivedi, Yoann Buratti, Thorsten Trupke, Ziv Hameiri
University of New South Wales, Sydney, Australia

Extracting solar cell electrical parameters directly from luminescence images, instead of the common current-voltage (I-V) measurements, can significantly increase the throughput and reduce the operation cost of photovoltaic production lines. This study investigates the capability of obtaining the fill factor (FF) from luminescence images by assessing the accuracy of published empirical expressions for the FF. The fitting approach for empirical coefficients was first modified. The resulting coefficients marginally improved the fit for the electrical range suggested in the literature as well as of current state-of-the-art solar cells. Nevertheless, through a dataset of 15,000 I-V measurements of industrial cells, a gap between the predicted and measured FF was observed. The impact of the effective ideality factor, edge recombination, and non-uniform recombination on the estimated FF were therefore investigated. Results show that adding information on the ideality factor or edge recombination increases the prediction accuracy. Moreover, the expressions tend to overestimate the FF for non-uniform cells. This study provides insights on the accurate estimation of FF through metrics that can be captured from luminescence images. This paves the way to improving the analysis of luminescence images for end-of-line characterization in industrial manufacturing lines.