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 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.展开更多
介绍苏州轨道交通自动售检票(AFC,Automatic Fare Collection)系统的发展现状,分析系统发展趋势及思路;分析与AFC系统相关的新兴信息化技术,对相关技术进行适用性分析;将相关技术应用在苏州轨道交通AFC系统中,实现了语音识别、数字人民...介绍苏州轨道交通自动售检票(AFC,Automatic Fare Collection)系统的发展现状,分析系统发展趋势及思路;分析与AFC系统相关的新兴信息化技术,对相关技术进行适用性分析;将相关技术应用在苏州轨道交通AFC系统中,实现了语音识别、数字人民币使用、智能客服等功能。智能化技术及功能的应用,提升了AFC系统的智能化水平,提高了乘客服务质量,同时降低了运营成本。展开更多
基金supported by the 2021 Annual Scientific Research Funding Project of Liaoning Pro-vincial Department of Education(Nos.LJKZ0535,LJKZ0526)the Natural Science Foundation of Liaoning Province(No.2021-MS-300)。
文摘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.
文摘介绍苏州轨道交通自动售检票(AFC,Automatic Fare Collection)系统的发展现状,分析系统发展趋势及思路;分析与AFC系统相关的新兴信息化技术,对相关技术进行适用性分析;将相关技术应用在苏州轨道交通AFC系统中,实现了语音识别、数字人民币使用、智能客服等功能。智能化技术及功能的应用,提升了AFC系统的智能化水平,提高了乘客服务质量,同时降低了运营成本。
