Data mining for yield enhancement in semiconductor manufacturing and an empirical study
By Chen-Fu Chien, Wen-Chih Wang, Jen-Chieh Cheng. Expert systems with applications, 2007
295 citations recorded by the source corpus.
During wafer fabrication, process data, equipment data, and lot history will be automatically or semi-automatically recorded and accumulated in database for monitoring the process, diagnosing faults, and managing manufacturing. However, in high-tech industry such as semiconductor manufacturing, many factors that are interrelated affect the yield of fabricated wafers. Engineers who rely on personal domain knowledge cannot find possible root causes of defects rapidly and effectively. This study aims to develop a framework for data mining and knowledge discovery from database that consists of a Kruskal-Wallis test, K-means clustering, and the variance reduction splitting criterion to investigate the huge amount of semiconductor manufacturing data and infer possible causes of faults and manufacturing process variations. The extracted information and knowledge is helpful to engineers as a basis for trouble shooting and defect diagnosis. We validated this approach with an empirical study in a semiconductor foundry company in Taiwan and the results demonstrated the practical viability of this approach.
Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/219748927fa306d6a8d667869ac1c90e86ec68db
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In Industrial and Manufacturing Engineering
In Mechanical Engineering
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Citations in the corpus
- Data mining in manufacturing: a review based on the kind of knowledge (2009, unknown): “To increase the yied of semiconductor manufacturing, Chien et al. [70] developed a framework consisting of Kruskal-wallis test, K-means clustering, and variance reduction splitting criteria.”
- Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets (2015, background): “Therefore, efficient and effective wafer analysis tools are in high demand [4]–[6].”
- A review of data mining applications for quality improvement in manufacturing industry (2011, methodology): “However, it is observed that clustering and/or association methods are also used for classifying/predicting quality (Chien et al., 2007; Shahbaz et al., 2006; Skinner et al., 2002).”