Data mining techniques for optimizing inventories for electronic commerce
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Design and Analysis of Experiments
Design and Analysis of Experiments
Design quality and robustness with neural networks
IEEE Transactions on Neural Networks
Data strip mining for the virtual design of pharmaceuticals with neural networks
IEEE Transactions on Neural Networks
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In current semiconductor and TFT-LCD factories, periodic sampling is commonly adopted to monitor the stability of manufacturing processes and the quality of products (or workpieces). As for those non-sampled workpieces, their quality is usually monitored by such as a fault-detection-and-classification (FDC) server. However, this method may fail to detect defected products. For example, a workpiece with all the individual manufacturing process parameters being in-spec may still result in out-of-spec product quality. Under this circumstance, unless this certain defected workpiece is selected for sampling by chance, it cannot be detected by simply monitoring the manufacturing process parameters collected from the production equipment. To solve the abovementioned problem, this research proposes a product quality fault detection scheme (FDS), which utilizes the classification and regression tree to implement a model for identifying the relationship between process parameters and out-of-spec products. Through this model, each set of normal manufacturing process parameters can be real-time and on-line examined to detect failure or defected products.