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相关概念视频

Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Conservation of Mass in Moving, Nondeforming Control Volume01:14

Conservation of Mass in Moving, Nondeforming Control Volume

Stormwater detention basins are essential in managing runoff during heavy rainfall, particularly in urban areas where impervious surfaces increase the risk of flooding. Understanding the conservation of mass in these systems allows engineers to optimize basin performance, balancing inflow, outflow, and water storage.
In the context of a detention basin, the conservation of mass states that the total mass of water entering the basin must equal the mass leaving the basin plus any accumulation of...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

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相关实验视频

Updated: May 11, 2026

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies
08:21

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一个新的端到端深度学习框架,用于芯片包装缺陷检测和检测.

Siyi Zhou1,2, Shunhua Yao3, Tao Shen1,2

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

一个新的深度学习框架准确地检测半导体芯片包装中的空缺陷. 这一进步改善了复杂芯片背景中的缺陷识别,提高了制造质量控制.

关键词:
愿景 马巴巴的愿景在X射线图像细分的X射线图像细分.芯片包装缺陷检测 检测缺陷双流解码器 双流解码器特性相关性相关性特征相关性

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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相关实验视频

Last Updated: May 11, 2026

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科学领域:

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

背景情况:

  • 半导体复杂度不断增加,在包装过程中增加了无效缺陷的风险.
  • 鉴定这些缺陷是具有挑战性的,因为复杂的背景和各种各样的缺陷特征.

研究的目的:

  • 开发一个深度学习框架,用于芯片包装中精确的空缺缺陷细分.
  • 提高半导体制造中空缺陷检测的准确性和效率.

主要方法:

  • 一个新的框架,结合了区提取和空缺缺陷细分网络.
  • 使用基于Mamba模型的编码器与视觉状态空间模块用于多级特征提取.
  • 采用交互式双流解码器与特征相关交叉门模块来增强细分.

主要成果:

  • 该框架在定制X射线芯片数据集上的定量和定性实验中表现出有效性.
  • 当应用于真实工厂检查线时,在芯片资格方面获得了93.3%的准确性.
  • 成功地解决了复杂背景,不同缺陷大小和模糊边界的挑战.

结论:

  • 拟议的深度学习框架为半导体包装中空缺陷细分提供了强大的解决方案.
  • 这项技术显著提高了芯片制造中自动化检查的可靠性.
  • 该框架在现实世界中的成功应用凸显了其在质量保证中的实际价值.