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使用可扩展的计算机视觉来自动化高通量半导体表征.

Alexander E Siemenn1, Eunice Aissi2, Fang Sheng3

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 化学工程是化学工程的重要组成部分.

背景情况:

  • 高通量材料合成产生了大量的样本,但表征方法显著落后,造成了瓶.
  • 现有的表征技术往往太慢或不灵活,无法快速输出现代合成方法.

研究的目的:

  • 开发自动化表征 (自动表征) 工具,以解决高通量材料发现的瓶.
  • 为了显著提高材料属性表征的吞吐量,与合成的速度相匹配.

主要方法:

  • 实现自适应计算机视觉用于自动化样本分析.
  • 开发一个可通用的组成映射工具.
  • 为频段间隙和环境稳定性计算创建可扩展的自动表征算法.

主要成果:

  • 与非自动化工作流相比,实现了85倍的吞吐量增加.
  • 在6分钟内自动计算200个作曲的乐队间隙,准确率为98.5%.
  • 在20分钟内自动计算了200个组合的环境稳定性,准确度为96.9%.
  • 在formamidinium (FA) 和甲基 (MA) 混合离子矿矿系统上证明.

结论:

  • 开发的自动表征工具有效地同步了材料性质表征与高通量合成.
  • 这些工具在加速发现新型功能材料方面取得了重大进展.