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相关概念视频

X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Updated: Jul 25, 2025

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography CT and Light Microscopy LM Correlated with Scanning Electron Microscopy SEM
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在X射线图像中检测IC电线结合缺陷的轻量级方法.

Daohua Zhan1,2, Jian Lin1,2, Xiuding Yang1,2

  • 1State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangzhou 510006, China.

Micromachines
|June 28, 2023
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概括

一个新的卷积神经网络 (CNN) 框架与空间卷积注意力 (SCA) 模块改善了集成电路 (IC) 电线粘合缺陷的检测. 轻量级的轻量级移动网络 (LMNet) 提供高精度的高效性能.

关键词:
一些X射线图像.卷积神经网络是一种卷积神经网络.轻量级网络轻量级的网络.电线粘合缺陷 电线粘合缺陷

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科学领域:

  • 电气工程 电气工程
  • 计算机视觉 计算机视觉
  • 材料科学 材料科学 材料科学

背景情况:

  • 集成电路 (IC) 电线粘合检查对于产品质量至关重要.
  • 目前的缺陷检测方法受到缓慢的速度和高能耗的影响.

研究的目的:

  • 开发一种高效准确的基于CNN的框架来检测IC电线粘合缺陷.
  • 为实际的工业应用引入轻量级网络 (LMNet).

主要方法:

  • 提出了一个CNN框架,包括一个空间卷积注意力 (SCA) 模块.
  • 开发了一个轻量级网络LMNet,集成SCA模块用于多尺度特征分析和自适应加权.
  • 通过使用平均平均精度 (mAP50),GFLOPs和FPS等指标评估网络的性能.

主要成果:

  • 该LMNet实现了99.2%的平均平均精度 (mAP50).
  • 该网络以1.5GFLOPs和108.7FPS的高效表现.
  • 拟议的框架平衡了高性能与低能耗.

结论:

  • LMNet框架有效地检测了IC芯片中的电线粘合缺陷.
  • 该SCA模块增强了功能集成和自适应加权,以提高准确性.
  • LMNet为工业IC检查提供了一个实用的解决方案,提供了有利的性能-消耗权衡.