Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles.

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Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, Tonio Buonassisi (2024). Dataset: Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization. https://doi.org/10.57702/mma2o4zp

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.1038/s41467-024-48768-2
Author Alexander E. Siemenn
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Eunice Aissi
Fang Sheng
Armi Tiihonen
Hamide Kavak
Basita Das
Tonio Buonassisi