A DBN-based Fault Diagnosis Method for Ion Implantation Machine
The development of domestic semiconductor manufacturing processes is receiving increasing attention,one of important issues is that the equipment failures that hinder industrial production.In this context,artificial intelligence technology has been widely applied by learning a large amount of fault data and automatically providing accurate diagnostic results.This paper proposes a fault diagnosis method based on deep belief networks(DBN),aiming to identify the types of faults in ion implantation machines,so that operators can locate specific components and implement maintenance.Then,evaluate the performance of the model using a total of seven health states,including six types of faults and the normal condition.It is found that the recognition accuracy of DBN model on the ion implantation machine is up to 98.66%,and the accuracy and convergence speed are better than traditional machine learning algorithms.This method has strong capabilities for modeling complex structured data and it is expected to improve equipment reliability and production efficiency in domestic semiconductor manufacturing processes.
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