A Mining-based Category Evolution Approach to Managing Online Document Categories
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 7 - Volume 7
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Expert Systems with Applications: An International Journal
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
LSI yield modeling and process monitoring
IBM Journal of Research and Development
A novel fuzzy rule base system for pose independent faces detection
Applied Soft Computing
IEEE Transactions on Neural Networks
Defect cluster recognition system for fabricated semiconductor wafers
Engineering Applications of Artificial Intelligence
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Wafer bin maps (WBMs) that show specific spatial patterns can provide clue to identify process failures in the semiconductor manufacturing. In practice, most companies rely on experienced engineers to visually find the specific WBM patterns. However, as wafer size is enlarged and integrated circuit (IC) feature size is continuously shrinking, WBM patterns become complicated due to the differences of die size, wafer rotation, the density of failed dies and thus human judgments become inconsistent and unreliable. To fill the gaps, this study aims to develop a knowledge-based intelligent system for WBMs defect diagnosis for yield enhancement in wafer fabrication. The proposed system consisted of three parts: graphical user interface, the WBM clustering solution, and the knowledge database. In particular, the developed WBM clustering approach integrates spatial statistics test, cellular neural network (CNN), adaptive resonance theory (ART) neural network, and moment invariant (MI) to cluster different patterns effectively. In addition, an interactive converse interface is developed to present the possible root causes in the order of similarity matching and record the diagnosis know-how from the domain experts into the knowledge database. To validate the proposed WBM clustering solution, twelve different WBM patterns collected in real settings are used to demonstrate the performance of the proposed method in terms of purity, diversity, specificity, and efficiency. The results have shown the validity and practical viability of the proposed system. Indeed, the developed solution has been implemented in a leading semiconductor manufacturing company in Taiwan. The proposed WBM intelligent system can recognize specific failure patterns efficiently and also record the assignable root causes verified by the domain experts to enhance troubleshooting effectively.