Application of deep learning-based wafer defect detection

With the rapid development of the integrated circuit industry, the complexity of the internal structure of the chip is increasing, to improve the inspection efficiency of defects and analyze the types of defects, is the guidance of the production process and the test equipment of each process segment is an important guarantee to improve the output. This paper develops a defect detection device for the back channel of wafer, splices and analyzes the captured images, and compares and classifies the types and sizes of defects on the surface of existing silicon wafer samples through image analysis. Combined with deep learning technology, automatic identification and classification of surface defects is realized. The system has good real-time performance, high efficiency and strong industrial application detection ability. The technical problems of defect detection such as long time, low efficiency and strong subjectivity are optimized.