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Luminescent coupling (LC) is a key phenomenon in monolithic tandem solar cells. This study presents a nondestructive technique to quantitatively evaluate the LC effect, addressing a gap in the existing predictions made by optical modeling. The method involves measuring the ratio of photons emitted from the high bandgap top cell that escape through the rear, contributing additional current to the bottom cell, and to those escaping from the front side of top cell. The findings indicate that in the analyzed monolithic perovskite/silicon tandem solar cells, more than 85% of the emitted photons escaping from the perovskite top cell are used to generate additional current in the bottom cell. This process notably reduces the mismatch in the generated current between each subcell, particularly when the current is limited by the low bandgap subcell. The presented method is applicable to a variety of monolithic tandem structures, providing vital information for subcell characterization, providing vital information for predicting energy output and optimization for outdoor applications., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Bulk defects in silicon solar cells are a key contributor to loss of efficiency. To detect and identify those defects, temperature- and injection-dependent lifetime spectroscopy is usually used, and the defect parameters are traditionally extracted using fitting methods to the Shockley-Read-Hall recombination statistics. In this study, we propose a deep learning-based extraction technique that is based on an alternative representation of the lifetime curves: lifetime mapping in the temperature and minority carrier concentration space. The deep learning approach successfully predicts all the defect parameters while addressing one of the main limitations of the traditional approach of locating the defect in the energy spectrum, which usually outputs two possible solutions. Furthermore, the approach is applied to temperature-dependent defect parameters where the traditional approach is not applicable, achieving satisfying levels of prediction of the defect parameters. Image representation and deep learning have the potential to bolster solar cell characterization techniques by extracting more insights from the characterization data.