Process monitoring and feedback control using multiresolution analysis and machine learning

Citation
Process monitoring and feedback control using multiresolution analysis and machine learning

Material Information

Title:
Process monitoring and feedback control using multiresolution analysis and machine learning
Creator:
Ganesan, Rajesh
Place of Publication:
[Tampa, Fla]
Publisher:
University of South Florida
Publication Date:
Language:
English

Subjects

Subjects / Keywords:
End point detection
Reinforcement learning
Run-by-run control
Sequential probability ratio test
Wavelet
Dissertations, Academic -- Industrial Engineering -- Doctoral -- USF
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Abstract:
ABSTRACT: Online process monitoring and feedback control are two widely researched aspects that can impact the performance of a myriad of process applications. Semiconductor manufacturing is one such application that due to the ever increasing demands placed on its quality and speed holds tremendous potentials for further research and development in the areas of monitoring and control. One of the key areas of semiconductor manufacturing that has received significant attention among researchers and practitioners in recent years is the online sensor based monitoring and feedback control of its nanoscale wafer fabrication process. Monitoring and feedback control strategies of nanomanufacturing processes often require a combination of monitoring using nonstationary and multiscale signals, and a robust feedback control using complex process models. It is also essential for the monitoring and feedback control strategies to possess stringent properties such as high speed of execution, low ^cost of operation, ease of implementation, high accuracy, and capability for online implementation. Due to the above requirement, a need is being felt to develop state-of-the-art sensor data processing algorithms that can perform far superior to those that are currently available both in the literature and commercially in the form of softwares.The contributions of this dissertation are three fold. It first focuses on the development of an efficient online scheme for process monitoring. The scheme combines the potentials of wavelet based multiresolution analysis and sequential probability ratio test to develop a very sensitive strategy to detect changes in nonstationary signals. Secondly, the dissertation presents a novel online feedback control scheme. The control problem is cast in the framework of probabilistic dynamic decision making, and the control scheme is built on the mathematical foundations of wavelet based multiresolution analysis, dynamic programming, and machine learning. ^Analysis of convergence of the control scheme is also presented. Finally, the monitoring and the control schemes are tested on a nanoscale manufacturing process (chemical mechanical planarization, CMP) used in silicon wafer fabrication. The results obtained from experimental data clearly indicate that the approaches developed outperform the existing approaches. The novelty of the research in this dissertation stems from the fact that they further the science of sensor based process monitoring and control by uniting sophisticated concepts from signal processing, statistics, stochastic processes, and artificial intelligence, and yet remain versatile to many real world process applications.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2005.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: World Wide Web browser and PDF reader.
System Details:
Mode of access: World Wide Web.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 90 pages.
General Note:
Includes vita.
Statement of Responsibility:
by Rajesh Ganesan.

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
001912177 ( ALEPH )
173688968 ( OCLC )
E14-SFE0001248 ( USFLDC DOI )
e14.1248 ( USFLDC Handle )

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Format:
Book

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ABSTRACT: Online process monitoring and feedback control are two widely researched aspects that can impact the performance of a myriad of process applications. Semiconductor manufacturing is one such application that due to the ever increasing demands placed on its quality and speed holds tremendous potentials for further research and development in the areas of monitoring and control. One of the key areas of semiconductor manufacturing that has received significant attention among researchers and practitioners in recent years is the online sensor based monitoring and feedback control of its nanoscale wafer fabrication process. Monitoring and feedback control strategies of nanomanufacturing processes often require a combination of monitoring using nonstationary and multiscale signals, and a robust feedback control using complex process models. It is also essential for the monitoring and feedback control strategies to possess stringent properties such as high speed of execution, low ^cost of operation, ease of implementation, high accuracy, and capability for online implementation. Due to the above requirement, a need is being felt to develop state-of-the-art sensor data processing algorithms that can perform far superior to those that are currently available both in the literature and commercially in the form of softwares.The contributions of this dissertation are three fold. It first focuses on the development of an efficient online scheme for process monitoring. The scheme combines the potentials of wavelet based multiresolution analysis and sequential probability ratio test to develop a very sensitive strategy to detect changes in nonstationary signals. Secondly, the dissertation presents a novel online feedback control scheme. The control problem is cast in the framework of probabilistic dynamic decision making, and the control scheme is built on the mathematical foundations of wavelet based multiresolution analysis, dynamic programming, and machine learning. ^Analysis of convergence of the control scheme is also presented. Finally, the monitoring and the control schemes are tested on a nanoscale manufacturing process (chemical mechanical planarization, CMP) used in silicon wafer fabrication. The results obtained from experimental data clearly indicate that the approaches developed outperform the existing approaches. The novelty of the research in this dissertation stems from the fact that they further the science of sensor based process monitoring and control by uniting sophisticated concepts from signal processing, statistics, stochastic processes, and artificial intelligence, and yet remain versatile to many real world process applications.
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Reinforcement learning.
Run-by-run control.
Sequential probability ratio test.
Wavelet.
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PAGE 6

iii 4.7.2 WRL RbR Controller Performance for a MIMO Process 71 4.7.2.1 CMP Modeling 71 4.8 Observations 74 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH 78 5.1 Conclusions 78 5.2 Research Extensions 79 REFERENCES 82 APPENDICES 88 Appendix A De fi nitions and Theorems 89 A.1 Sup norm of a Vector 89 A.2 Lipschitz Continuity 89 A.3 Span 89 A.4 Borkar's Lemma 89 A.5 Asymptotically Stable Critical Point 90 ABOUT THE AUTHOR End Page

PAGE 101

ABOUT THE AUTHOR Rajesh Ganesan received his Ph.D. in Industrial Engineering and M.A in Mathematics with conc entration in Statistics in 2005 and M.S. in Industrial and Management Systems Engineering in 2002, all from the University of South Florida, Tampa, FL. He is currently an Assistant Professor in the Systems Engineering and Operations Research Department at the George Mason University, Fairfax, VA. His B.S. degree is in Mechanical Engineering from the National Institute of Technology, Calicut, In dia, where he received the University First Rank Medal for the year 1996. He won the First Place Graduate Research Award from the Institute of Industrial Engineers at the 2004 Industrial Engineering Research Conference in Houston. He also received the USF Outstanding Thesis Award for the year 2003. He served as a Senior Quality Engineer at BOSCH, India from 1996 2000 where his primary task was to conduct QS9000 internal quality system audits. He also served as the Project Manager of the NSF Funded GK 12 pr oject at USF. His areas of research include wavelet based statistical process monitoring, stochastic control optimization and translational bioinformatics. He is also interested in engineering education research and GK 12 outreach programs.


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