Fiacre Emile Rougieux

Ultraviolet-induced degradation (UVID) is a significant challenge for high-efficiency commercial silicon solar technology. While prior studies have established that UVID degrades surface passivation and is strongly linked to hydrogen-related processes, the interplay between chemical, electronic, and structural effects at the interface remains incompletely understood. In this work, we extend existing frameworks by combining cell-level observations with a quantitative, energy-resolved analysis of interface defect states and their coupling to field-effect passivation. Initially, we evaluate UVID at the cell level and observe that UV exposure induces pronounced degradation on both the front and rear surfaces of PERC solar cells, as well as on the front surface of TOPCon solar cells, whereas no measurable degradation is observed on the rear surface of TOPCon cells. To analyze the underlying mechanisms, we focus on industrial AlOx-passivated lifetime samples without a capping SiNx:H layer and demonstrate that the AlOx/crystalline silicon (c-Si) interface undergoes degradation under UV exposure. This work focuses on the quantitative analysis of the energy-dependent interface defect density (Dit), and UV light increases Dit while simultaneously enhancing field-effect passivation by increasing negative fixed charge (Qf). The combined increase in Dit and Qf ultimately governs the observed reduction in carrier lifetime of AlOx-passivated samples. By combining Gaussian-distribution modelling of the Dit with physical-based lifetime modelling, we demonstrate that UVID is driven by enhanced surface recombination associated with the activation of interface defect states, together with an increase in negative Qf. This physical perspective offers deeper insight into the full degradation mechanism, complementing existing chemical models.

A small number of photovoltaic modules degrade far more rapidly than average, creating a “long tail” in degradation rate distribution that poses a critical challenge to the reliability and financial viability of solar projects. This study investigates the factors contributing to this phenomenon by analyzing a large global dataset from the National Renewable Energy Laboratory. Our analysis reveals that the long tail is an intrinsic and composite feature of module degradation, not merely a statistical consequence of combining different climates. We identify at least three distinct pathways that could contribute to its formation. The first is accelerated degradation driven by strong statistical associations between different degradation modes, where the interplay of mechanisms appears to be a primary contributor of the most severely degraded modules. The second is rapid early-life failure (infant mortality), which populates the tail with modules likely containing initial manufacturing or material defects. The third is failure of individual latent defects, such as solder fatigue or cell cracks, which can cause sudden severe performance loss at random points in a module's life. Based on our results, we suggest that efforts should be made to understand and mitigate the interaction between associated degradation modes. For instance, the careful selection of key components, such as backsheet, is crucial as it could initiate multiple pathways of degradation.

In this work, we present a comprehensive re-evaluation of the iron–gallium (FeGa) recombination parameters in silicon using injection-dependent lifetime spectroscopy (IDLS). Ga-doped silicon wafers (of varying resistivities) with precise concentrations of intentional iron contamination in the silicon wafer bulk, through ion implantation and distribution, were used. The presence of interstitial Fei and FeGa, and their lifetime-limiting effects in these silicon wafers, were confirmed through measuring the effective minority carrier lifetime changes during the conditions that are known to cause FeGa dissociation and association. The presence of Fe was also confirmed by deep-level transient spectroscopy. To ensure accurate IDLS analysis of the FeGa defect in silicon, a lifetime linearization scheme was employed to effectively filter out interference by other defects. Error analysis was employed to find the combination of defect parameters that best fit the experimental data and to ascertain the range of uncertainty associated with the IDLS best-fit results. The optimal fitting of the experimental IDLS by Shockley–Read–Hall statistics produced an electron capture cross section σn=2.3×10−14cm2, hole capture cross section σp=1.1×10−14cm2, and a trap energy level Et=EV+0.2−0.01+0.02eV for the FeGa defect in silicon. The extracted defect parameters are also verified by experimentally measuring the crossover point of Fei and FeGa lifetime curves.

With the remarkable advances in semiconductor processing, devices such as solar cells have fewer and fewer defects that impact their performance. Determination of the defects that currently limit the device performance, predominantly by increasing the charge carrier recombination rate, has become more challenging with standard methods like deep level transient spectroscopy. To circumvent this limitation, the photovoltaic community is attempting to use the measurement of the charge carrier recombination rates to identify the remaining defects, as this approach is intrinsically sensitive to the defects that limit the cell's/sample's performance/lifetime. This article reviews this new approach, contrasting it with the developments that have occurred with deep-level transient spectroscopy, finding several critical limitations in the current assumptions, and providing suggestions for an improved strategy.