A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis
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 r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2017-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717721810 |
| _version_ | 1797725300592738304 |
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| author | Seok-Woo Jang Gye-Young Kim |
| author_facet | Seok-Woo Jang Gye-Young Kim |
| author_sort | Seok-Woo Jang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T10:29:11Z |
| format | Article |
| id | doaj.art-da54fbc1210748089aec3fd2902b5b8f |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:29:11Z |
| publishDate | 2017-07-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-da54fbc1210748089aec3fd2902b5b8f2023-09-02T09:25:00ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772017-07-011310.1177/1550147717721810A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysisSeok-Woo Jang0Gye-Young Kim1Department of Digital Media, Anyang University, Anyang, KoreaSchool of Software, Soongsil University, Seoul, KoreaThis 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.https://doi.org/10.1177/1550147717721810 |
| spellingShingle | Seok-Woo Jang Gye-Young Kim A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis International Journal of Distributed Sensor Networks |
| title | A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis |
| title_full | A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis |
| title_fullStr | A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis |
| title_full_unstemmed | A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis |
| title_short | A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis |
| title_sort | monitoring method of semiconductor manufacturing processes using internet of things based big data analysis |
| url | https://doi.org/10.1177/1550147717721810 |
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