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Virtual metrology for run-to-run control in semiconductor manufacturing

Cited 58 time in Web of Science Cited 73 time in Scopus
Authors
Kang, Pilsung; Kim, Dongil; Lee, Hyoung-joo; Doh, Seungyong; Cho, Sungzoon
Issue Date
2011-03-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS; Vol.38 3; 2508-2522
Keywords
Process controlVirtual metrologyRun-to-run controlExponentially weighted moving average controllerSemiconductor manufacturingPhotolithographyData mining
Abstract
In semiconductor manufacturing processes, run-to-run (R2R) control is used to improve productivity by adjusting process inputs run by run. A process will be controlled based on information obtained during or after a process, including metrology values of wafers. Those metrology values, however, are usually available for only a small fraction of sampled wafers. In order to overcome the limitation, one can use virtual metrology (VM) to predict metrology values of all wafers, based on sensor data from production equipments and actual metrology values of sampled wafers. In this paper, we develop VM prediction models using various data mining techniques. We also develop a VM embedded R2R control system using the exponentially weighted moving average (EWMA) scheme. The experiments consist of two parts: (1) verifying VM prediction models with actual production equipments data, and (2) conducting simulations of the R2R control system. Our VM prediction models are found to be accurate enough to be directly implemented in actual manufacturing processes. The simulation results show that the VM embedded R2R control system improves productivity. (C) 2010 Elsevier Ltd. All rights reserved.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/74928
DOI
https://doi.org/10.1016/j.eswa.2010.08.040
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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