Automatic defect classification for semiconductor manufacturing
Machine Vision and Applications
ACM Computing Surveys (CSUR)
Expert Systems with Applications: An International Journal
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Hybrid machine learning to improve predictive performance
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Predictive Performance of Clustered Feature-Weighting Case-Based Reasoning
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Separation of composite defect patterns on wafer bin map using support vector clustering
Expert Systems with Applications: An International Journal
Recognizing yield patterns through hybrid applications of machine learning techniques
Information Sciences: an International Journal
Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting
Engineering Applications of Artificial Intelligence
Analyzing ECG for cardiac arrhythmia using cluster analysis
Expert Systems with Applications: An International Journal
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
Diverse defect patterns shown on the wafer map usually contain important information for quality engineers to find their root causes of abnormalities. Today, even with highly automated and precisely monitored facilities used in a near dust-free clean room and operated with well-trained process engineers, the occurrence of spatial defects still cannot be avoided. This research presents a spatial defect diagnosis system and attempts to solve two challenging problems for semiconductor manufacturing: (1) to estimate the number of clusters in advance, and (2) to separate both convex and non-convex defect clusters at the same time. In this paper, a spatial filter is used to denoise the noisy wafer map and to extract meaningful defect clusters. To isolate various types of defect patterns, a hybrid scheme combining entropy fuzzy c means (EFCM) with spectral clustering is applied to the denoised output. Furthermore, a decision tree based on two cluster features (convexity and eigenvalue ratio) is constructed to identify the specific defect type and to provide decision support for quality engineers. The proposed approach is validated with an empirical wafer bin maps obtained in a DRAM company in Taiwan. Experimental results show that four kinds of mixed-type defect patterns are successfully extracted and classified. More importantly, the proposed method is very promising to be further applied to other industries, such as liquid crystal or plasma display.