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The temperature coefficient (TC) is an essential figure of merit to accurately evaluate solar cell performance at various operating temperatures and hence enabling the comparison between different cell technologies. Recently, solar cells that use passivating contacts based on transition metal oxide (TMO) layers have attracted much attention due to their excellent performance. Therefore, knowledge of their TCs and insights into their performance at various operating temperatures are of significant interest. In this study, we investigate the temperature‐dependent performance of solar cells with TMO‐based passivating contacts at various illumination intensities. We then compare their performance to that of standard silicon heterojunction (SHJ) solar cells. The efficiency TC (TCη) of solar cells that use passivating contacts based on molybdenum oxide (MoOx) and titanium oxide (TiOx) films is found to be almost identical. Both outperform the TCη of the standard SHJ cells and are greatly superior to those of cell structures without passivating contacts. The superior TCη of the MoOx‐based cells is mainly due to their favourable TCs of the short‐circuit current density (TCJsc) and fill factor (TCFF), whereas the superiority of TCη of the TiOx‐based cells is solely resulting from the superior TCFF. The favourable TCJsc of the MoOx‐based cells is explained by an enhanced spectral response at short wavelengths with increasing temperature, due to the improvement of the passivation quality of the MoOx‐based passivating contacts. The beneficial TCFF of both solar cells is partly resulting from the improvement of the contact resistivity of the TMO‐based passivating contacts which counterbalances some of the fill factor losses at elevated temperatures. Although an improvement of the passivation quality of the TMO‐based passivating contacts is observed at elevated temperature, it does not have a strong impact on the TC of the open‐circuit voltage of the investigated solar cells. Furthermore, we also found that the studied cells are less sensitive to temperature variation at higher illumination intensities. [ABSTRACT FROM AUTHOR]

Photoluminescence (PL) imaging is a powerful inspection technique for research laboratories and photovoltaic production lines. Common PL imaging systems have two limitations: (a) due to the non‐uniformities of the measured samples, the acquired images are affected by lateral carrier flow, resulting in inaccurate lifetime analysis and image blurring; (b) samples' non‐uniformities are measured at locally different injection levels. In this paper, we present a PL imaging system that is not affected by these effects. By adaptively adjusting the light intensity at each pixel, we achieve a uniform excess carrier density across the sample, thereby eliminating any lateral currents. The non‐uniformity of the minority carrier lifetime can then be extracted from the spatial inverse of the illumination intensity. The advantages of the proposed system are demonstrated using silicon wafers with and without a diffusion layer that contain non‐uniform defects. This approach presents a significant improvement in accuracy and sharpness compared to conventional PL imaging techniques and, therefore, is expected to be beneficial for any quantitative PL‐based analysis. [ABSTRACT FROM AUTHOR]

End‐of‐line characterization of solar cells is necessary to filter out defective cells and bin cells to avoid power mismatch loss in photovoltaic modules. Current–voltage testers, used by almost any photovoltaic company and research laboratory, are costly to maintain and to adapt to recent and predicted morphological changes in solar cells: larger and thinner wafers, half or shingled cells, a wide range of busbar layouts, and more. In this study, we challenge this fundamental technique and propose to bin solar cells and detect defective cells based on a deep learning analysis of their electroluminescence images. The use of electroluminescence imaging addresses the above‐mentioned limitations of the current–voltage technique, as well as allowing faster measurements as it avoids any capacitance effects. By introducing LumiNet, a convolutional neural network end‐to‐end framework, solar cell efficiency bins can be accurately predicted from electroluminescence imaging with a mean error similar to that obtained by current–voltage measurements. The proposed framework is validated on several state‐of‐the‐art mono‐crystalline silicon solar cell structures. We show that photovoltaic modules fabricated using the proposed method would have similar mismatch loss as the traditional current–voltage binning. We then demonstrate the method on half‐cut silicon solar cells. Predicting the half‐cut cell efficiencies, from the deep learning framework, enables manufacturers to assess post‐cutting damages and reassess their binning strategy before module assembly. Furthermore, the deep learning framework is shown to work well even on datasets that have not been previously seen. The trained deep learning LumiNet models' structure and weight are shared with the community to accelerate the adaptation of deep learning for luminescence image analysis in the photovoltaic industry. [ABSTRACT FROM AUTHOR]

In this study, we provide insights into planar structure methylammonium lead triiodide (MAPbI3) perovskite solar cells (PSCs) using electroluminescence and photoluminescence imaging techniques. We demonstrate the strength of these techniques in screening relatively large area PSCs, correlating the solar cell electrical parameters to the images and visualizing the features which contribute to the variation of the parameters extracted from current density-voltage characterizations. It is further used to investigate one of the major concerns about perovskite solar cells, their long term stability and aging. Upon storage under dark in dry glovebox condition for more than two months, the major parameter found to have deteriorated in electrical performance measurements was the fill factor; this was elucidated via electroluminescence image comparisons which revealed that the contacts' quality degrades. Interestingly, by deploying electroluminescence imaging, the significance of having a pin-hole free active layer is demonstrated. Pin-holes can grow over time and can cause degradation of the active layer surrounding them. [ABSTRACT FROM AUTHOR]

Electroluminescence and photoluminescence imaging of crystalline silicon solar cells are powerful characterization techniques for research and industrial applications. Both techniques can provide spatially resolved data of the implied open-circuit voltage, a critical parameter for photovoltaic devices. However, the conversion of luminescence intensities into implied voltages often requires a challenging calibration process, which must be performed separately for distinct sample types. Here, we present a novel voltage calibration method for silicon wafers and cells, which eliminates the need for sample-specific calibration. We demonstrate that with a suitable selection of optical filters, the error in the implied open-circuit voltage for typical textured silicon solar cells and wafers can be limited to 3 mV. • To date, it is challenging to extract the implied voltage (i V oc) from luminescence images. • Samples with different optical properties require distinct calibration cells. • A novel method is developed for calibrating luminescence images using a proper bandpass filter. • It enables the quantification of i V oc images with an uncertainty <3 mV regardless of the optical properties of measured cells. [ABSTRACT FROM AUTHOR]