A self-adaptive DBSCAN-based method for wafer bin map defect pattern classification. (August 2021)

• Record Type: Journal Article
• Authors: Chen, Shouhong; Yi, Mulan; Zhang, Yuxuan; Hou, Xingna; Shang, Yuling; Yang, Ping
• Is Part Of: Microelectronics and reliability. Volume 123(2021)
• Publication Date: 2021-08
• Subjects: Wafer map -- Automatic parameters -- Self-adaptive DBSCANWBM -- Clustering; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2021.114183
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: The wafer map is obtained by testing each die in the wafer during semiconductor production for defects and marking the defective die. The classification of wafer maps can provide evidence for problems occurring in the production process, so as to solve the problems and reduce the costs. Before classifying the wafer map, the most important thing is feature extraction. In addition to a certain spatial pattern, the wafer map also has a lot of noise, which affects the process of feature extraction. When the traditional DBSCAN algorithm is used for filtering, it is necessary to manually determine the value of Eps and MinPts parameters, and the selection of the parameters directly affects the accuracy of the clustering. Therefore, this paper proposes an automatic parameter filtering method based on DBSCAN, which can solve the traditional drawbacks of manually parameters setting, the algorithm is a Self-Adaptive DBSCAN-based method for wafer bin map, we call it SA-DBSCANWBM. This method selects a comprehensive index of cluster intra-cluster density and inter-cluster density to evaluate the optimal parameters. The experimental results show that the algorithm proposed in this paper can automatically and reasonably select better parameters and has a good clustering effect, which helpful for subsequent feature extraction and classification. Highlights: We proposed a self-adaptive DBSCAN-based method for wafer bin map defect pattern classification. This algorithm can achieve good clustering effect and solve the disadvantage of traditional manual parameter setting. It implies a potential method for evaluation on wafer bin map defect pattern classification.