Investigating Wafer Surface Defect Detection: An Analysis of Approaches and Developments

With the increasing demand for electronic devices made for smart technology, integrated circuits are upgrading toward more complicated designs with smaller dimensions. Detecting defects on the surface of silicon wafers is crucial to maintaining product quality in semiconductor device manufacturing. Silicon wafer maps are used to distinguish the functional die from the faulty one. This step will increase the yield and provide quality assurance. The defects can be detected by processing the wafer map images using various image processing techniques or using machine learning techniques. The detection becomes easier after the emerging of deep learning methods. This paper investigates the research progress of various researchers in recent days about wafer surface defect detection. It also identifies the perks of various researches and provides a comparative analysis as well as limitations. An overview of the issues and difficulties associated with the present wafer surface defect detection work, as well as upcoming research trends and fresh concepts were provided.