Error-Smoothing Exponentially Weighted Moving Average for Improving Critical Dimension Performance in Photolithography Process

Authors

DOI:

https://doi.org/10.23055/ijietap.2016.23.5.3131

Keywords:

adaptive process control, manufacturing intelligence, yield enhancement, semiconductor manufacturing

Abstract

The increasingly stringent tolerance of linewidths is a result of shrinking feature size of integrated circuits, and thus the manufacturing process in wafer fabrication should be accurately controlled to maintain process yields. Critical dimension (CD) is defined as the minimum width of a photoresist line or space printed on an exposure pattern by a stepper or scanner in photolithography. The CD is measured using metrology equipment and is compensated by modifying the corresponding equipment setup parameters. A feedback message is then sent to the next wafer for pre-adjustment and a feedforward message is sent to the previous wafer for post-adjustment. This study aimed to address a manufacturing intelligence framework to improve CD performance in a photolithography process. The input recipe is updated for the next run that is based on recently measured process data through the modified controller called as an error-smoothing exponentially weighted moving average (E-EWMA); both process and information flows are considered. A case study with a run-to-run process control is conducted to compensate the process variation to demonstrate the proposed framework. The results demonstrate that the proposed E-EWMA outperforms the conventional EWMA used in the company. 

Author Biography

Jei-Zheng Wu, Soochow University, Taiwan

Dr. Jei-Zheng Wu is an Associate Professor and Vice Chairman of Department of Business Administration, Soochow University (SCU), Taipei, Taiwan, R.O.C. He received his PhD and MS in Industrial Engineering and Engineering Management from National Tsing Hua University (NTHU) in Hsinchu. He received BS with double majors in Business Administration and Mathematics from National Taiwan University. His professional experience includes Adjunct Associate/Assistant Professor at NTHU, Yuan Ze University, Postdoctoral researcher at NTHU, and co-op at IBM Thomas J. Watson Research Center (Yorktown Heights, New York).

He received Quality Paper Award from Chinese Society for Quality, Award for Distinguished Performance on Industry-Academia Collaboration from National Science Council, Outstanding Researcher Scholarship from National Science Council, Research Award from Soochow Business Administration Education Foundation, Research Publication Prize from Soochow University, the Best Paper Award at the Twelfth Asia Pacific Industrial Engineering & Management System (APIEMS 2011), the Best Paper Award at the CIIE Annual Meeting (2011 and 2010), and the Young Scientist Prize at the Intelligent Manufacturing & Logistics Systems International Conference in 2008. His main research interests include manufacturing strategy, operations management, supply chain management, decision analysis, meta-heuristics, decision support systems, and management and applications of telematics. He has published research outcome in SCI/SSCI–indexed jounals including Computers & Industrial Engineering, OR Spectrum, IEEE Transactions on Semiconductor Manufacturing, International Journal of Production Research, Journal of Intelligent Manufacturing, International Journal of Shipping and Transport Logistics, Growth and Change, Expert Systems and Applications, INFORMATION-An International Interdisciplinary Journal, and other international journals including Industrial Engineering & Management Systems (Australian Index System, APIEMS official publication) and Journal of Quality (EI). He has served as Guest Editor for a number of journals including Flexible Services and Manufacturing Journal (SCI), and Journal of Quality (EI).

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Published

2017-01-06

How to Cite

Hsu, C.-Y., & Wu, J.-Z. (2017). Error-Smoothing Exponentially Weighted Moving Average for Improving Critical Dimension Performance in Photolithography Process. International Journal of Industrial Engineering: Theory, Applications and Practice, 23(5). https://doi.org/10.23055/ijietap.2016.23.5.3131

Issue

Vol. 23 No. 5 (2016)

Section

Special Issue: 2015 International Symposium on Semiconductor Manufacturing Intelligence

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