Chris Dreessen | Searcharxiv

arXiv2026

Characterizing Fill Factor Limitations in Perovskite-Silicon Tandem Solar Cells

Perovskite-silicon tandem technology has exceeded the single junction theoretical efficiency limit. However, there is still distance to the thermodynamic limit mainly caused by the fill factor. This work presents a methodology to illustrate the mechanisms of FF loss in perovskite-Si monolithic tandem solar cells. Apart from the series resistance related loss characterized by electroluminescence, another loss factor is from the photoshunt, a phenomenon in which the parallel resistance apparently reduces under illumination in perovskite solar cells due to the moderate charge transport layer mobility. In addoition, the two-diode property of the Si cell can also influence the FF of tandem devices. The photoshunt can be hidden when the bottom cell is over illuminated, which explains highly efficient tandem solar cells are usually bottom cell limited. This work outlines strategies that overcoming the photoshunt issue can move the perovskite top cell closer to low FF losses in tandem solar cells.

cond-mat.mtrl-sci↗

arXiv2025

Towards a fully differentiable digital twin for solar cells

Maximizing energy yield (EY) - the total electric energy generated by a solar cell within a year at a specific location - is crucial in photovoltaics (PV), especially for emerging technologies. Computational methods provide the necessary insights and guidance for future research. However, existing simulations typically focus on only isolated aspects of solar cells. This lack of consistency highlights the need for a framework unifying all computational levels, from material to cell properties, for accurate prediction and optimization of EY prediction. To address this challenge, a differentiable digital twin, Sol(Di)$^2$T, is introduced to enable comprehensive end-to-end optimization of solar cells. The workflow starts with material properties and morphological processing parameters, followed by optical and electrical simulations. Finally, climatic conditions and geographic location are incorporated to predict the EY. Each step is either intrinsically differentiable or replaced with a machine-learned surrogate model, enabling not only accurate EY prediction but also gradient-based optimization with respect to input parameters. Consequently, Sol(Di)$^2$T extends EY predictions to previously unexplored conditions. Demonstrated for an organic solar cell, the proposed framework marks a significant step towards tailoring solar cells for specific applications while ensuring maximal performance.

physics.comp-ph↗