晶圆制造
起重机(装置)
半导体器件制造
故障检测与隔离
架空(工程)
嵌入式系统
薄脆饼
生产线
软件
工程类
制造工程
计算机科学
系统工程
可靠性工程
操作系统
电气工程
机械工程
执行机构
结构工程
作者
HuiChu Fu,Yan Qiao,Liping Bai,Nan Wu,Bin Liu,Yunfang He
出处
期刊:IEEE Robotics & Automation Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-06-01
卷期号:30 (2): 22-33
标识
DOI:10.1109/mra.2023.3263973
摘要
Semiconductor manufacturing relies on a long and complex production line for wafer fabrication. In a modern semiconductor fabrication plant (fab), a wafer manufacturing line is composed of more than 1,000 manufacturing tools and an overhead hoist system for delivering wafer lots among the tools. Novel technologies, such as Internet of Things (IoT) and software engineering technologies, are adopted in semiconductor manufacturing such that large amounts of data can be collected from manufacturing tools. Also, these tools and the overhead hoist system should cooperate well with each other. Due to the data-processing capacity limitation in a current fault detection and classification (FDC) system, the diagnosis efficiency is limited such that some undesired events cannot be detected in time, leading to significant economic loss. With the booming development of big data technology, this work conducts a study on a new FDC framework based on a Hadoop ecosystem to deal with the data-processing limitation and improve diagnosis efficiency. Also, a migration path is presented such that the current FDC system can be smoothly migrated to a Hadoop ecosystem-based one without shutting down a wafer fabrication line. Experimental results show that the proposed FDC framework can run safely and stably.
科研通智能强力驱动
Strongly Powered by AbleSci AI