Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study

Abstract

Industry 4.0, smart manufacturing and its related technologies are now becoming the leading trend in the development of the manufacturing industry. One of the key drivers of Industry 4.0 is big data analytics, which can transform large amounts of data into useful information, enabling astute and rapid decision-making strategies when combined with expert domain knowledge. The semiconductor industry is the most important high-tech industry in Taiwan, but it is also one of the most energy-consuming industries in the country. Therefore, it is critical to improve the efficiency of the manufacturing process and reduce the overall energy consumption of facility systems. This research demonstrates how to apply big data analytics in the semiconductor industry to explore the relationships of various machine parameters, develop predictive models for machine energy efficiency and apply optimisation tools to minimise energy consumption, while meeting the production demands. An empirical study is conducted in conjunction with a semiconductor corporation in Taiwan, targeting the air compressor system in its factory. The research framework is shown to be capable of assisting semiconductor fabrication plant decision-makers in optimising machine configurations, resulting in more than 10% savings on energy consumption and significantly decreased manufacturing costs.

Keywords:

• Big data analytics
• air compressors
• semiconductor industry
• dynamic programming
• energy efficiency

Acknowledgments

This research was supported financially by the Advanced Manufacturing and Service Management Center at National Tsing Hua University and United Microelectronics Corporation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Advanced Manufacturing and Service Management Center of National Tsing Hua University and United Microelectronics Corporation.

Notes on contributors

Kuo-Hao Chang

Kuo-Hao Chang is Professor of Industrial Engineering and Engineering Management at National Tsing Hua University and the deputy director of the National Science and Technology Center for Disaster Reduction. He has won several prestigious awards, including the 2012 Bonder Scholar Research Award from INFORMS, 2015 IIE Transactions Best Application Paper Award, 2015 K.D. Tocher Medal from The OR Society and 2016 Outstanding Young Scholar Research Award from Academia Sinica. He has led a team to successfully complete many consultant projects with industrial companies including TSMC, UMC, VisEra, YOMURA, ITRI, etc. He is now serving as Associate Editor of IEEE Transactions on Automation Science and Engineering and Asia-Pacific Journal of Operational Research. He is a member of INFORMS, IIE and IEEE. His research interests include simulation optimisation, stochastic models and Monte Carlo simulation. His email and web addresses are [email protected] and https://chang.ie.nthu.edu.tw.

Yi-Jyun Sun

Yi-Jyun Sun received the B.S. degree and the M.S. degree from the Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan. His research interests include stepwise regression, dynamic programming, modelling and optimisation in semiconductor manufacturing.

Chi-An Lai

Chi-An Lai received a Bachelor's degree in industrial engineering and engineering management from National Tsing-Hua University in Hsinchu City, Taiwan, and a Dual Master's degree in global operation management from Stony Brook University in New York and National Tsing Hua University. Her research interests include data mining, big data analysis, smart manufacturing and its applications in the semiconductor industry. She is currently working in Taiwan Semiconductor Manufacturing Company, and is a sponsor for multiple fab automation and smart manufacturing projects.

Li-Der Chen

Li Der Chen received the M.S. degree in Mechanical Engineering from National Cheng Kung University. He is the Section Manager of Facility Operation & Construction Division of UMC. His expertise are system management of compressor systems, chiller system/clean room, and exhaust gas treatment system in the semiconductor industry.

Chih-Hung Wang

Chih-Hung Wang is Technical Manager of Smart Manufacturing Division in UMC. Mr Wang currently conducts predictive maintenance and virtual metrology projects for UMC foundry.

Chung-Jung Chen

Chung Jung Chen is Technical Manager of Smart Manufacturing Division in UMC.

Chih-Ming Lin

Chih Ming Lin is Department Manager of Smart Manufacturing Division in UMC. He is the coordinator of several industry 4.0 projects in UMC and is experienced in predictive maintenance and virtual metrology of the semiconductor field.