A monitoring method of semiconductor manufacturing processes using Internet of Things-based big data analysis
- Authors
- Jang, Seok-Woo; Kim, Gye-Young
- Issue Date
- Jul-2017
- Publisher
- SAGE PUBLICATIONS INC
- Keywords
- Monitoring; learning; prediction; matched pattern; Internet of Things; reference model
- Citation
- INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, v.13, no.7
- Journal Title
- INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
- Volume
- 13
- Number
- 7
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/6308
- DOI
- 10.1177/1550147717721810
- ISSN
- 1550-1477
- Abstract
- This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things-based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.
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