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
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2022.3141426

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 research has been devoted to feature selection from trace data that is important for process monitoring of natural variation. Additionally, the high-mix production mode with different recipes leads to process dynamic of wafer-to-wafer (W2W) variation which should also be identified for safeguarding false alarms and serving as a warning indicator. Therefore, this paper proposes a data-driven framework to identify the key features with respect to the W2W variation. In particular, the self-organizing map is used to annotate the grade of wafer variation among the in-line metrology data. Subsequently, the adaptive boosting (AdaBoost) is adopted to examine the effectiveness of every feature and its processing times, respectively. To validate the proposed framework, an empirical study from a semiconductor fabrication plant is conducted. The experimental results demonstrate that the key feature identification is of critical importance to build highly capable models for process monitoring. Through the dimensionality reduction technique, it has been illustrated that a smaller set of the identified key features are able to pinpoint the W2W variation of different wafer grades more clearly than the whole set of process features.