Review of Wafer Surface Defect Detection Methods

Abstract

Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.