Abstract
AbstractWe report a fast, reliable and non-destructive method for quantifying the homogeneity of perovskite thin films over large areas using machine vision. We adapt existing machine vision algorithms to spatially quantify multiple perovskite film properties (substrate coverage, film thickness, defect density) with pixel resolution from pictures of 25 cm2 samples. Our machine vision tool—called PerovskiteVision—can be combined with an optical model to predict photovoltaic cell and module current density from the perovskite film thickness. We use the measured film properties and predicted device current density to identify a posteriori the process conditions that simultaneously maximize the device performance and the manufacturing throughput for large-area perovskite deposition using gas-knife assisted slot-die coating. PerovskiteVision thus facilitates the transfer of a new deposition process to large-scale photovoltaic module manufacturing. This work shows how machine vision can accelerate slow characterization steps essential for the multi-objective optimization of thin film deposition processes.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
Reference68 articles.
1. National Renewable Energy Laboratory. Champion Photovoltaic Module Efficiency Chart. https://www.nrel.gov/pv/module-efficiency.html (2020).
2. Qiu, L., He, S., Ono, L. K., Liu, S. & Qi, Y. Scalable fabrication of metal halide perovskite solar cells and modules. ACS Energy Lett. 4, 2147–2167 (2019).
3. Park, N.-G. & Zhu, K. Scalable fabrication and coating methods for perovskite solar cells and solar modules. Nat. Rev. Mater. 5, 333–350 (2020).
4. Swartwout, R., Hoerantner, M. T. & Bulović, V. Scalable deposition methods for large‐area production of perovskite thin films. Energy Environ. Mater. 2, 119–145 (2019).
5. Rong, Y. et al. Challenges for commercializing perovskite solar cells. Science 361, eaat8235 (2018).
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献