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Wafer bin maps (WBMs) data, presented as images, play a critical role in identifying defects in the semiconductor industry. Thus, accurately classifying WBM defect patterns is essential to maintain high quality and enhance the overall yield. However, the task of labeling and classifying WBM data, which are generated daily in the tens of thousands or more, presents a challenge for experts. Recently, with advancements in artificial intelligence research, there has been a surge in efforts to automatically classify WBM defect patterns. Nevertheless, existing studies have primarily focus on classifying known defect patterns using labels. However, in the real-world semiconductor industry, new defect patterns are constantly emerging in addition to the known patterns. In this study, we propose the contrastive deep clustering (CODEC) for wafer bin maps that identifies new defective patterns in WBMs while simultaneously clustering these patterns into multiple defects without using labels. We use a contrastive loss function to address the challenges associated with a limited number of novel defect patterns. We demonstrate the effectiveness of our proposed methodology in accurately classifying new defect patterns using open data WM-811 k. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue which include issues about supervised learning, the relevance of vector machine, and data mining methodology.

Feature ranking is a widely used feature selection method. It uses importance scores to evaluate features and selects those with high scores. Conventional unsupervised feature ranking methods do not consider the information on cluster structures; therefore, these methods may be unable to select the relevant features for clustering analysis. To address this limitation, we propose a feature ranking algorithm based on silhouette decomposition. The proposed algorithm calculates the ensemble importance scores by decomposing the average silhouette widths of random subspaces. By doing so, the contribution of a feature in generating cluster structures can be represented more clearly. Experiments on different benchmark data sets examined the properties of the proposed algorithm and compared it with the existing ensemble-based feature ranking methods. The experiments demonstrated that the proposed algorithm outperformed its existing counterparts. [ABSTRACT FROM AUTHOR]

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport’s glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport’s receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed; however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future. [ABSTRACT FROM AUTHOR]