Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation

Kuo Chi Chang; Jeng Shyang Pan; Kai Chun Chu; Der Juinn Horng; Huang Jing

doi:10.1007/978-981-13-5841-8_70

Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation

Kuo Chi Chang, Jeng Shyang Pan, Kai Chun Chu, Der Juinn Horng, Huang Jing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

The semiconductor process is designed to meet the requirements of photolithography, thin film, etching, cleaning of 12″ 10nm advanced commercial semiconductor manufacturing process, and the process integration system obtains the necessary process parameters and product measurement data. The relevant information is transmitted back to the MES system according to the production capacity and the product number of batch. This study first conducted an intelligent review of the 12″ 10nm furnace process tools. According to the SEMI specification, the production equipment interface standard for the furnace tube equipment should be discussed. The 12″ 10nm furnace module was selected for MES big data analysis. Due to the large number of process data, only the LPCVD process temperature distribution during the TEOS process was selected for distributed computation discussion. However, the yield results of this study can be maintained at 92%, while the equipment utilization rate can reach 97%. It is obvious that good results have been achieved.

Original language	English
Title of host publication	Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018
Editors	Jeng-Shyang Pan, Jerry Chun-Wei Lin, Shih-Pang Tseng, Bixia Sui
Publisher	Springer Verlag
Pages	669-678
Number of pages	10
ISBN (Print)	9789811358401
DOIs	https://doi.org/10.1007/978-981-13-5841-8_70
State	Published - 2019
Event	12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 - Changzhou, China Duration: 14 Dec 2018 → 17 Dec 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	834
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018
Country/Territory	China
City	Changzhou
Period	14/12/18 → 17/12/18

Keywords

Big data
Furnace
Information integrated
MES
Semiconductor process automation

Access to Document

10.1007/978-981-13-5841-8_70

Cite this

Chang, K. C., Pan, J. S., Chu, K. C., Horng, D. J., & Jing, H. (2019). Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. In J.-S. Pan, J. C.-W. Lin, S.-P. Tseng, & B. Sui (Eds.), Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018 (pp. 669-678). (Advances in Intelligent Systems and Computing; Vol. 834). Springer Verlag. https://doi.org/10.1007/978-981-13-5841-8_70

Chang, Kuo Chi ; Pan, Jeng Shyang ; Chu, Kai Chun et al. / Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. editor / Jeng-Shyang Pan ; Jerry Chun-Wei Lin ; Shih-Pang Tseng ; Bixia Sui. Springer Verlag, 2019. pp. 669-678 (Advances in Intelligent Systems and Computing).

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title = "Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation",

abstract = "The semiconductor process is designed to meet the requirements of photolithography, thin film, etching, cleaning of 12″ 10nm advanced commercial semiconductor manufacturing process, and the process integration system obtains the necessary process parameters and product measurement data. The relevant information is transmitted back to the MES system according to the production capacity and the product number of batch. This study first conducted an intelligent review of the 12″ 10nm furnace process tools. According to the SEMI specification, the production equipment interface standard for the furnace tube equipment should be discussed. The 12″ 10nm furnace module was selected for MES big data analysis. Due to the large number of process data, only the LPCVD process temperature distribution during the TEOS process was selected for distributed computation discussion. However, the yield results of this study can be maintained at 92%, while the equipment utilization rate can reach 97%. It is obvious that good results have been achieved.",

keywords = "Big data, Furnace, Information integrated, MES, Semiconductor process automation",

author = "Chang, {Kuo Chi} and Pan, {Jeng Shyang} and Chu, {Kai Chun} and Horng, {Der Juinn} and Huang Jing",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 ; Conference date: 14-12-2018 Through 17-12-2018",

year = "2019",

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Chang, KC, Pan, JS, Chu, KC, Horng, DJ & Jing, H 2019, Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. in J-S Pan, JC-W Lin, S-P Tseng & B Sui (eds), Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. Advances in Intelligent Systems and Computing, vol. 834, Springer Verlag, pp. 669-678, 12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018, Changzhou, China, 14/12/18. https://doi.org/10.1007/978-981-13-5841-8_70

Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. / Chang, Kuo Chi; Pan, Jeng Shyang; Chu, Kai Chun et al.
Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. ed. / Jeng-Shyang Pan; Jerry Chun-Wei Lin; Shih-Pang Tseng; Bixia Sui. Springer Verlag, 2019. p. 669-678 (Advances in Intelligent Systems and Computing; Vol. 834).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation

AU - Chang, Kuo Chi

AU - Pan, Jeng Shyang

AU - Chu, Kai Chun

AU - Horng, Der Juinn

AU - Jing, Huang

PY - 2019

Y1 - 2019

N2 - The semiconductor process is designed to meet the requirements of photolithography, thin film, etching, cleaning of 12″ 10nm advanced commercial semiconductor manufacturing process, and the process integration system obtains the necessary process parameters and product measurement data. The relevant information is transmitted back to the MES system according to the production capacity and the product number of batch. This study first conducted an intelligent review of the 12″ 10nm furnace process tools. According to the SEMI specification, the production equipment interface standard for the furnace tube equipment should be discussed. The 12″ 10nm furnace module was selected for MES big data analysis. Due to the large number of process data, only the LPCVD process temperature distribution during the TEOS process was selected for distributed computation discussion. However, the yield results of this study can be maintained at 92%, while the equipment utilization rate can reach 97%. It is obvious that good results have been achieved.

AB - The semiconductor process is designed to meet the requirements of photolithography, thin film, etching, cleaning of 12″ 10nm advanced commercial semiconductor manufacturing process, and the process integration system obtains the necessary process parameters and product measurement data. The relevant information is transmitted back to the MES system according to the production capacity and the product number of batch. This study first conducted an intelligent review of the 12″ 10nm furnace process tools. According to the SEMI specification, the production equipment interface standard for the furnace tube equipment should be discussed. The 12″ 10nm furnace module was selected for MES big data analysis. Due to the large number of process data, only the LPCVD process temperature distribution during the TEOS process was selected for distributed computation discussion. However, the yield results of this study can be maintained at 92%, while the equipment utilization rate can reach 97%. It is obvious that good results have been achieved.

KW - Big data

KW - Furnace

KW - Information integrated

KW - MES

KW - Semiconductor process automation

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SN - 9789811358401

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A2 - Pan, Jeng-Shyang

A2 - Lin, Jerry Chun-Wei

A2 - Tseng, Shih-Pang

A2 - Sui, Bixia

PB - Springer Verlag

T2 - 12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

Y2 - 14 December 2018 through 17 December 2018

ER -

Chang KC, Pan JS, Chu KC, Horng DJ, Jing H. Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. In Pan JS, Lin JCW, Tseng SP, Sui B, editors, Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. Springer Verlag. 2019. p. 669-678. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-13-5841-8_70

Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation

Abstract

Publication series

Conference

Keywords

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