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.

Design Process Optimization Based on Design Process Gene Mapping

The idea of genetic engineering is introduced into the area of product design to improve the design efficiency. A method towards design process optimization based on the design process gene is proposed through analyzing the correlation between the design process gene and characteristics of the design process. The concept of the design process gene is analyzed and categorized into five categories that are the task specification gene, the concept design gene, the overall design gene, the detailed design gene and the processing design gene in the light of five design phases. The elements and their interactions involved in each kind of design process gene signprocess gene mapping is drawn with its structure disclosed based on its function that process gene.

Ghost Imaging with Deep Learning for Position Mapping of Weakly Scattered Light Source

We propose ghost imaging(GI)with deep learning to improve detection speed.GI,which uses an illumination light with random patterns and a single-pixel detector,is correlation-based and thus suitable for detecting weak light.However,its detection time is too long for practical inspection.To overcome this problem,we applied a convolutional neural network that was constructed based on a classification of the causes of ghost image degradation.A feasibility experiment showed that when using a digital mirror device projector and a photodiode,the proposed method improved the quality of ghost images.