Graphical approach of equipment health monitoring using network analysis

被引：0

作者：

Ki, Wanwook ^{[1
]}

Choi, Minjae ^{[1
]}

Lee, Yongjun ^{[1
]}

Yoon, Kiseop ^{[1
]}

Hwang, Hanchan ^{[1
]}

Park, Jisoo ^{[1
]}

Kang, Taewook ^{[1
]}

Park, Jaeyong ^{[1
]}

Kim, Younghoon ^{[1
]}

机构：

[1] Samsung Elect, Seoul, South Korea

来源：

2023 7TH IEEE ELECTRON DEVICES TECHNOLOGY & MANUFACTURING CONFERENCE, EDTM | 2023年

关键词：

Equipment health monitoring; Network; WGCNA; Sensor correlation;

D O I：

10.1109/EDTM55494.2023.10102976

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As the complexity of semiconductor devices increases, new approaches are required for monitoring equipment health. In this paper, we adopt the weighted gene co-expression network analysis (WGCNA) for sensor data following in three stages: 1) construction of networks, 2) identification of modules, and 3) detection of abnormal sensors by the graphical approach. Therefore, manufacturing process conditions are able to modify, the similarity between sensors was increased, and in return 0.3% yield rate gain is achieved.

引用

页数：3

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