Using scalable computer vision to automate high-throughput semiconductor characterization

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2024-06-11
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en
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11
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Nature Communications, Volume 15, issue 1, pp. 1-11
Abstract
High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1−xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.
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Publisher Copyright: © The Author(s) 2024.
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Siemenn, A E, Aissi, E, Sheng, F, Tiihonen, A, Kavak, H, Das, B & Buonassisi, T 2024, ' Using scalable computer vision to automate high-throughput semiconductor characterization ', Nature Communications, vol. 15, no. 1, 4654, pp. 1-11 . https://doi.org/10.1038/s41467-024-48768-2