Using Bayesian network technology to predict the semiconductor manufacturing yield rate in IoT

Abstract

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm’s requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage.