首页|基于改进ResNet的晶圆缺陷模式识别研究

基于改进ResNet的晶圆缺陷模式识别研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
晶圆缺陷检测是半导体制造的重要环节，通过对由缺陷形成的晶圆图进行缺陷模式的识别可以追溯生产过程中问题并进行专项改进，从而提高晶圆制造的良品率。因此，针对晶圆缺陷的模式识别问题，探究采用改进的ResNet网络对7种常见晶圆缺陷进行自动识别。在原ResNet的基础上，将SE注意力机制嵌入到网络中，提高网络的特征提取能力，发现关键特征，抑制无用特征。改进残差结构，加入深度可分离卷积代替传统卷积，降低网络的计算量和参数量使得网络轻量化，从而方便在工业环境中更好的进行部署。实验表明，改进后的ResNet模型准确率达到98。5%，参数量较原模型大幅减少，与常见的卷积神经网络相比具有较好的效果。综合来看，该方法能够很好地进行常见晶圆缺陷类型的模式识别，具有一定的应用价值。
Wafer Defect Map Pattern Recognition Based on Improved ResNet
The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.

ResNetdeep learningmachine visionwafer defect map pattern recogniton

杨祎宁、魏鸿磊

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大连工业大学机械工程与自动化学院,大连 116034,中国

ResNet 深度学习 机器视觉 晶圆缺陷模式识别

2021 Annual Scientific Research Funding Project of Liaoning Provincial Department of Education2021 Annual Scientific Research Funding Project of Liaoning Provincial Department of EducationNatural Science Foundation of Liaoning Province

LJKZ0535LJKZ05262021-MS-300

2024

10.16356/j.1005-1120.2024.S.010

南京航空航天大学学报(英文版)
南京航空航天大学

南京航空航天大学学报(英文版)

CSTPCD
影响因子：0.279
ISSN：1005-1120
年,卷(期)：2024.41(z1)