Using scalable computer vision to automate high-throughput semiconductor characterization

Alexander E. Siemenn (), Eunice Aissi (), Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das and Tonio Buonassisi
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Alexander E. Siemenn: Massachusetts Institute of Technology
Eunice Aissi: Massachusetts Institute of Technology
Fang Sheng: Massachusetts Institute of Technology
Armi Tiihonen: Massachusetts Institute of Technology
Hamide Kavak: Massachusetts Institute of Technology
Basita Das: Massachusetts Institute of Technology
Tonio Buonassisi: Massachusetts Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-48768-2

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