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相关概念视频

Atomic Force Microscopy01:08

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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相关实验视频

Updated: Jul 18, 2026

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
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一种基于机器视觉的全流检查方法,用于检测晶圆表面缺陷.

Naigong Yu1,2,3, Hongzheng Li1,2,3, Qiao Xu1,2,3

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Mathematical biosciences and engineering : MBE
|July 28, 2023
PubMed
概括

本研究介绍了一种先进的机器视觉方法,用于半导体晶圆表面缺陷检测. 这种新的方法在识别有缺陷的模具方面达到97%以上的准确性,提高了制造产量.

关键词:
功能提取 特性提取图像细分 图像细分机器学习是机器学习.机器视觉 机器视觉 机器视觉晶圆表面缺陷检测 晶圆表面缺陷检测

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Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects
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Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
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相关实验视频

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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
  • 电气工程 电气工程

背景情况:

  • 半导体制造需要精确的晶圆表面缺陷检测,以提高产量.
  • 现有的机器学习和数字图像处理算法在工业应用中面临挑战,包括数据采集,计算成本和概括.
  • 这些局限性阻碍了自动化晶片检查系统的广泛采用.

研究的目的:

  • 为半导体晶圆表面缺陷检测开发一种强大的全流机器视觉检查方法.
  • 解决当前方法的局限性,特别是在培训样本采集,计算需求和概括方面.
  • 提高工业环境中晶圆缺陷识别的准确性和效率.

主要方法:

  • 建议使用候选框架配合和坐标插值的新型模块分割算法来准确地分割所有模块,防止错过的模块分割.
  • 基于区域缺陷特征集群的模具缺陷异常检测方法被引入,以尽量减少特征提取过程中的噪声干扰.
  • 综合方法将细分和缺陷检测相结合,用于全面的晶圆检查.

主要成果:

  • 拟议的方法精确地将半导体晶片上的位置和段落模拟图像.
  • 检查系统在识别有缺陷的模具方面达到97%以上的高精度.
  • 缺陷检测方法有效地减少了噪声的影响,提高了检测可靠性.

结论:

  • 开发的全流检查方法为工业晶圆表面缺陷检测提供了实用解决方案.
  • 该方法克服了与训练数据,计算负载和概括相关的关键挑战.
  • 这种机器视觉技术显著提高了半导体制造中晶圆检查的准确性和适用性.