Exploit the Value of Production Data to Discover Opportunities for Saving Power Consumption of Production Tools

• Published in: IEEE transactions on semiconductor manufacturing Vol. 30; no. 4; pp. 345 - 350
• Main Authors: Yu, Chih-Min, Chien, Chen-Fu, Kuo, Chung-Jen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; big data analytics; Data mining; Energy conservation; Energy management; manufacturing intelligence; neural network application; Neural networks; Power consumption; Power demand; power saving; Semiconductor device manufacture; semiconductor fab; Viability
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2017.2750712

Semiconductor industry is both technology and energy intensive. There is a critical need to develop effective ways for energy saving to support smart and green production. This paper aims to develop data mining approach based on neural networks to exploit the value of production data and derive improvement directions for energy saving. In particular, the power consumption per wafer processed step (kilowatt hour per move, kwh/move) of individual production tool sets can be estimated, in which the relationships between kwh/move and 19 individual input factors, including "lot size," "process time," "uptime," "usable machine," "Q-time constrain," and "sampling rate" are derived. An empirical study was conducted in a leading wafer fab and the results have shown practical viability of the proposed approach to discover effective opportunities for saving 17.21% power consumption by production tool sets.