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
Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been su...Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.展开更多
To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-...To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing, such as average filtering and geometric correction, was performed on the captured image, and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast, objective and accurate inspection compared with subjective and inaccurate human eye inspection.展开更多
A method and results of identification of wear debris using their morphological features are presented. The color images of wear debris were used as initial data. Each particle was characterized by a set of numerical ...A method and results of identification of wear debris using their morphological features are presented. The color images of wear debris were used as initial data. Each particle was characterized by a set of numerical parameters combined by its shape, color and surface texture features through a computer vision system. Those features were used as input vector of artificial neural network for wear debris identification. A radius basis function (RBF) network based model suitable for wear debris recognition was established, and its algorithm was presented in detail. Compared with traditional recognition methods, the RBF network model is faster in convergence, and higher in accuracy.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.
基金Special Fund for Science & Technology Research of Education Commission,Chongqing(KJ101302)~~
文摘Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.
文摘To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing, such as average filtering and geometric correction, was performed on the captured image, and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast, objective and accurate inspection compared with subjective and inaccurate human eye inspection.
文摘A method and results of identification of wear debris using their morphological features are presented. The color images of wear debris were used as initial data. Each particle was characterized by a set of numerical parameters combined by its shape, color and surface texture features through a computer vision system. Those features were used as input vector of artificial neural network for wear debris identification. A radius basis function (RBF) network based model suitable for wear debris recognition was established, and its algorithm was presented in detail. Compared with traditional recognition methods, the RBF network model is faster in convergence, and higher in accuracy.
