标题： 通过快速多模态表征预测$V_{OC}$的趋势，以及对最先进的 p-i-n 钙钛矿器件的分析 显示英文标题

标题： Predicting Trends in $V_{OC}$ Through Rapid, Multimodal Characterization of State-of-the-Art p-i-n Perovskite Devices

摘要： 钙钛矿光伏技术正接近商业化部署，但单结和叠层结构仍有显著提升功率转换效率和稳定性的空间。 在改变加工条件后，快速筛选材料质量的能力对于加速钙钛矿基技术的优化和商业化至关重要。 目前，研究人员使用各种独立的计量工具来隔离器件堆栈中的功率损失源，这可能速度慢且劳动密集。 在此，我们展示了一种多模态计量方法的使用，在制造过程中迅速确定超过100个钙钛矿器件的最大可实现和预测的开路电压。 获取这些不同数据是通过将它们整合到一个集成测量平台中来实现的。 我们表明，这些数据和自动化分析可用于快速理解和最终预测先进器件架构的开路电压的定量趋势。 所提出的数据显示和自动化分析工作流程提供了一种可靠的方法，以快速识别可能导致改进开路电压的吸收层和电荷传输层组合。 显示英文摘要

摘要： Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid screening of material quality after altering processing conditions is critical to accelerating the optimization and commercialization of perovskite-based technologies. Currently, researchers utilize a wide range of stand-alone metrology tools to isolate sources of power loss throughout a device stack, which can be slow and labor intensive. Here, we demonstrate the use of a multimodal metrology approach to rapidly determine the maximum achievable and predicted open circuit voltages of > 100 perovskite devices during fabrication. Acquisition of these different data are facilitated by combining them into a single integrated measurement platform. We show that these data and automated analysis can be used to rapidly understand and ultimately predict quantitative trends in open circuit voltages of state-of-the-art devices architectures. The data and automated analysis workflow presented provides a reliable approach to quickly identify absorber and charge transport layer combinations that can lead to improved open circuit voltages.