基于DBN的离子注入机故障诊断方法研究

A DBN-based Fault Diagnosis Method for Ion Implantation Machine

国产半导体制造工艺的发展越来越受到重视,但是设备的故障常常会对工业生产造成阻碍。在这种背景下,通过学习大量的故障数据并自动提供准确的诊断结果的人工智能技术,已经得到了广泛的应用。本研究提出了一种基于深度置信网络(DBN)的故障诊断方法,旨在识别离子注入机的故障类型,以便操作人员定位具体故障位置并实施维修。然后,用六种故障和正常情况总共七种健康状态来评估模型性能。通过实验,深度置信网络模型在离子注入机上的识别正确率为98.66%,准确率及收敛速度均优于传统的机器学习算法。该方法对于复杂结构数据的建模具有强大的能力,在半导体设备故障诊断领域具有应用潜力,有望在国产半导体制造工艺中提高设备可靠性和生产效率。

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.