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Using scalable computer vision to automate high-throughput semiconductor characterization

Author

Listed:
  • 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
    Aalto University)

  • Hamide Kavak

    (Massachusetts Institute of Technology
    Cukurova University)

  • Basita Das

    (Massachusetts Institute of Technology)

  • Tonio Buonassisi

    (Massachusetts Institute of Technology)

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.

Suggested Citation

  • Alexander E. Siemenn & Eunice Aissi & Fang Sheng & Armi Tiihonen & Hamide Kavak & Basita Das & Tonio Buonassisi, 2024. "Using scalable computer vision to automate high-throughput semiconductor characterization," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48768-2
    DOI: 10.1038/s41467-024-48768-2
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    References listed on IDEAS

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