This paper explores the application of supervised learning algorithms in the field of wafer defect detection. With the development of the consumer electronics industry, more advanced packaging technology is required in chip manufacturing processes, leading to an increase in the scale and complexity of integrated circuits. The complexity in wafer manufacturing processes increases the probability and variety of wafer defects. To improve production yield and refine process manufacturing, it is crucial to identify defects and pinpoint corresponding process issues.
This paper focuses on the application of supervised learning algorithms in the field of wafer defect detection. The advantages and limitations of algorithms such as RF, CNN classifiers, KNN, SVM, U-NET and YOLO, were analyzed in wafer surface defect detection.
Through comparative analysis, the application characteristics and performance differences of different algorithms in wafer defect detection have been clarified. This paper provided a guiding basis for how to select the most suitable algorithm in practical industrial applications, thereby helping to improve the accuracy and reliability of wafer detection.
In this paper we presented a variety of classic and cutting-edge supervised learning algorithms in the specific application scenario of wafer defect detection for comparison and proposed a practical decision tree that links evaluation indicators with algorithm selection. The future prospects and the direction were also discussed demonstrating significant theoretical significance and practical value.
