Semiconductor Wafer Defect Detection Based on Machine Learning

Semiconductor wafers are widely used in integrated circuit chips. Because of the complex production process, it is easy to cause various defects. Therefore, the defect detection of semiconductor wafers is an important means to ensure their yield and productivity. This paper mainly expounds on the detection method of wafer defects combined with the machine vision algorithm, including the CNN model, and the classification of the learning-based methods. Current problems and future prospects for total crystalline circle defect detection are discussed in the end.