Automatic Defect Fatality Analysis and Killer-Defect Identification System Based on Deep Learning

With the development of artificial intelligence technology, more and more research has begun to explore the application of AI to the field of chip manufacturing. However,in the stage of mass production of chips, the problems existing in the current chip manufacturing process such as low efficiency of defect analysis and reliance on manual experience judgment have not been fully solved. To tackle this challenge, this paper introduces an intelligent defect analysis method that leverages the strengths of convolutional neural networks (specifically ResNet34) and Transformer architectures, creating a deep learning framework designed to automatically assess the effect of wafer surface defects on chip yield. Although traditional convolutional neural networks perform well in processing local pixel relationships, they often lack attention to the global background of images. The Transformer network introduces a global attention mechanism that pays more attention to the overall information and context of the image. By combining these two network structures, this paper designs a network framework that can capture both deep semantic information and global and contextual information. In order to increase the data set and improve the model’s capacity for generalisation, the group in the experiment used data enhancement technology after gathering a sizable amount of wafer surface defect image data. By comparing the experimental results, the proposed deep learning framework performs better than other advanced algorithms in the task of defect mortality analysis, especially in the analysis of wafer surface defect images, showing higher accuracy and efficiency. In addition, because of the use of a lightweight network, the structure can effectively meet the needs of large-scale production, and has strong promotion and application ability, as well as new ideas and methods for the intelligent development of related fields, so as to make the development of intelligent development in related fields more reasonable.