Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

doi:doi:10.57702/mma2o4zp

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.

BibTex:

@dataset{Alexander_E_Siemenn_and_Eunice_Aissi_and_Fang_Sheng_and_Armi_Tiihonen_and_Hamide_Kavak_and_Basita_Das_and_Tonio_Buonassisi_2024,
    abstract = {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.},
    author = {Alexander E. Siemenn and Eunice Aissi and Fang Sheng and Armi Tiihonen and Hamide Kavak and Basita Das and Tonio Buonassisi},
    doi = {10.57702/mma2o4zp},
    institution = {No Organization},
    keyword = {'computer vision segmentation', 'high-throughput automation', 'hyperspectral imaging', 'optical band gap', 'perovskites', 'stability measurement'},
    month = {dec},
    publisher = {TIB},
    title = {Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization},
    url = {https://service.tib.eu/ldmservice/dataset/using-scalable-computer-vision-to-automate-high-throughput-semiconductor-characterization},
    year = {2024}
}