Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Layer-specific facial soft-tissue thickness in 1174 Chinese adults: Implications for finite-element headforms and ergonomic design.

Ergonomics·2026
Same author

Independent and joint effects of ambient temperature and relative humidity on fetal distress: effect modification by air pollutants.

International journal of biometeorology·2026
Same author

Permeability prediction of 3D complex fracture networks in deep underground based on fractal analysis of trace maps.

Scientific reports·2026
Same author

Therapeutic leukapheresis with severe pertussis: evaluating efficacy, feasibility, and safety.

BMC pediatrics·2026
Same author

Assessing Executive Function in Patients With Temporal Lobe Epilepsy Through a Game-Based Assessment Task.

Behavioural neurology·2026
Same author

In Vivo Calcium Imaging with a Miniaturized Microscope in the Hypothalamus for Understanding Social Behaviors in Mice.

Journal of visualized experiments : JoVE·2026

相关实验视频

Updated: Jul 23, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

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

Published on: May 28, 2016

13.9K

基于多模式数据融合的CZ单晶的节点损失检测方法

Lei Jiang1,2, Rui Xue1, Ding Liu1,2

  • 1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究引入了一种使用多式联络数据融合来检测单晶生长过程中节点损失的新方法. 该方法准确地识别缺陷,提高半导体制造中的质量控制.

关键词:
在CZ单晶.注意力机制注意力机制连续波形变换连续波形变换.卷积神经网络是一种卷积神经网络.多式联网数据融合节点损失检测检测节点损失检测

更多相关视频

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

相关实验视频

Last Updated: Jul 23, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

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

Published on: May 28, 2016

13.9K
Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

科学领域:

  • 材料科学 材料科学 材料科学
  • 半导体制造业 半导体制造业
  • 人工智能的人工智能

背景情况:

  • 单晶对于半导体和光伏工业至关重要.
  • 生长的Czochralski (CZ) 方法容易发生节点损失,导致晶体失效.
  • 目前的工业方法缺乏在生长过程中有效检测节点损失.

研究的目的:

  • 开发一种高效,数据驱动的方法来检测单晶生长中的节点损失.
  • 探索多式联络数据融合在分析晶体生长中的应用.
  • 提高CZ方法中缺陷检测的准确性和可靠性.

主要方法:

  • 收集多式联运数据,包括直径,温度,拉动速度和阴茎图像.
  • 应用连续波形变换用于一维信号预处理.
  • 利用改进的道注意力机制卷积神经网络 (ICAM-CNN) 和多式融合网络 (MMFN) 进行数据分析和缺陷识别.

主要成果:

  • 拟议的ICAM-CNN和MMFN方法准确地检测了CZ单晶生长中的节点损失缺陷.
  • 在缺陷检测中实现了高精度,稳定性和实时性能.
  • 证明了多式联通数据融合对监测晶体生长的有效性.

结论:

  • 开发的多式联通数据融合方法为实时节点损失检测提供了有效的解决方案.
  • 这种方法可以显著提高工业单晶生产的效率和质量控制.
  • 数据驱动技术为半导体和光伏行业提供了宝贵的技术支持.