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相关概念视频

Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

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To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
6.8K

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相关实验视频

Updated: Jan 14, 2026

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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对于晶圆缺陷检查的高效和强大的SEM图像否定.

Hyunwoong Bae1, Jaeseok Byun2, Yongwoo Lee3

  • 1Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro, South Seoul 08826, Korea.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|October 23, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了Relaxed Noise2Noise with Input dropout (ReNIn) 用于扫描电子显微镜 (SEM) 消除噪声. ReNIn为晶圆缺陷检查提供了高效的培训和改进的泛化.

关键词:
这就是SEM SEM.深度学习是一种深度学习.拒绝使用,拒绝使用.拒绝进行检查.

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Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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相关实验视频

Last Updated: Jan 14, 2026

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
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Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 图像处理 图像处理

背景情况:

  • 扫描电子显微镜 (SEM) 噪音阻碍了精确的晶圆缺陷检查.
  • 目前深度学习对SEM的方法受到效率低下和对新型图像结构的不良概括的影响.

研究的目的:

  • 为SEM图像引入基于深度学习的高效和强大的Denoising方法.
  • 为了提高无形图像结构的模拟模型的概括能力.
  • 改进下游晶圆检查任务,例如圆圈检测.

主要方法:

  • 拟议的放松噪声2噪声 (ReNIn) 框架将放松的噪声2噪声方法与输入失效相结合.
  • 放松的Noise2Noise组件优化了消除噪声性能和数据收集成本之间的权衡.
  • 输入丢失技术增强了对各种图像结构的模型概括.

主要成果:

  • 与监督方法相比,ReNIn在培训数据收集成本显著降低的情况下实现了具有竞争力的排泄性能.
  • 输入丢失可以提高结构新图像的性能,而不会降低熟悉结构的性能.
  • 该方法提高了下游检查任务中圆圈检测的准确性.

结论:

  • ReNIn 提供了一种有效的解决方案来消除 SEM 图像,平衡性能和数据采集成本.
  • 拟议的方法证明了SEM图像分析的优越泛化能力.
  • 通过提高缺陷检测准确度,ReNIn有效支持关键晶圆检查流程.