Using Bayesian networks to build data mining applications for a semiconductor cleaning process

Data mining is part of the knowledge discovery process that offers a new way to look at data. It consists of the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the discovery of patterns that lie hidden among a set of data. A Bayesian network is a specific model of knowledge network. It offers useful information about the mutual dependencies among the features in the application domain. Such information can be used for gaining better understanding about the dynamics of the process under observation. Manufacturing process monitoring using Bayesian networks is a quite useful example. In this paper, we provide Bayesian networks to extract knowledge from data. We present an approach for the Bayesian networks to implement a data mining task for computer integrated manufacturing (CIM). It could find the cause factors in various parameters that affect in semiconductor cleaning process.