Key Feature Identification for Monitoring Wafer-to-Wafer Variation in Semiconductor Manufacturing

• Published in: IEEE transactions on automation science and engineering Vol. 19; no. 3; pp. 1 - 12
• Main Authors: Fan, Shu-Kai S., Hsu, Chia-Yu, Tsai, Du-Ming, Chou, Mabel C., Jen, Chih-Hung, Tsou, Jen-Hsuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AdaBoost; Alarms; Clustering algorithms; Empirical analysis; False alarms; Fault detection; Fault diagnosis; Feature extraction; feature selection; Machine learning; Manufacturing; Metrology; Monitoring; Performance prediction; Process monitoring; Self organizing maps; Self-organizing feature maps; self-organizing map; Semiconductor device modeling; wafer-to-wafer variation

To monitor process and identify the deviation as early as possible, data-driven methods have been applied for process monitoring and fault detection in semiconductor manufacturing. Although various fault detection and classification models had been discussed in the literature, however, little resear...