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

TL;DR

This paper introduces a rapid, integrated multimodal measurement platform that predicts open circuit voltages in perovskite solar cells, significantly speeding up material screening and device optimization.

Contribution

It presents a novel multimodal metrology approach combined with automated analysis for fast prediction of device performance in perovskite photovoltaics.

Findings

Successfully predicted open circuit voltages in over 100 devices.

Identified material combinations that improve device performance.

Enabled rapid screening of fabrication conditions.

Abstract

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.