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通过混合深度学习框架与CNN-变压器双向交互的IC包装材料识别.

Chengbin Zhang1, Xuankai Zhou1, Nian Cai1

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Micromachines
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

一个新的混合深度学习框架,CNN-变压器交互 (CTI) 模型,从图像中准确识别集成电路 (IC) 包装材料. 这种先进的方法实现了高性能,通过精确的材料识别来确保IC可靠性.

关键词:
识别IC包装材料的识别方法双向互动是一种双向互动.卷积神经网络是一种卷积神经网络.变压器变压器变压器变压器

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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:

  • 由于微型和纳米制造技术,电子元件和集成电路 (IC) 的小型化正在迅速推进.
  • 精确识别IC包装材料对于确保微型电子设备的可靠性和性能至关重要.

研究的目的:

  • 使用深度学习开发一种自动化方法来识别IC包装材料.
  • 引入一种新的混合深度学习框架,有效地捕捉IC包装图像中的本地和全球特征.

主要方法:

  • 设计了一个混合深度学习框架,CNN-变压器交互 (CTI) 模型.
  • 该CTI模型使用级联块,每个包含卷积神经网络 (CNN) 对于本地特征和变压器用于全球和本地窗口特征.
  • 双向交互机制在道和空间维度上促进了CNN和变压器分支之间的特征传输.

主要成果:

  • 在识别三种类型的IC包装材料方面,CTI模型表现出高性能.
  • 该框架获得了96.16%的F1得分和97.92%的准确性.
  • 性能超过了现有的IC包装材料识别深度学习方法.

结论:

  • 拟议的CNN-变压器交互 (CTI) 模型对于自动化IC包装材料识别是有效的.
  • 与传统的深度学习技术相比,混合方法提供了更高的性能.
  • 这种方法有助于确保微型集成电路的可靠性.