Run-to-Run Control in
`SEMICONDUCTOR
`MANUFACTURING
`
`Edited by
`James Moyne
`Enrique del Castillo
`Arnon Max Hurwitz
`
`CRC Press
`Boca Raton London New York Washington, D.C.
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`Library of Congress Cataloging-in-Publication Data
`
`Moyne, James.
`Run-to-run control in semiconductor manufacturing / by James Moyne, Enrique Del
` Castillo, and Arnon Max Hurwitz.
`p. cm.
`Includes bibliographical references and index.
`ISBN 0-8493-1178-0 (alk. paper)
`1. Semiconductors—Design and construction. 2. Semiconductor industry—Production
` control. 3. Electronic packaging 4. Production management. I. Del Castillo, Enrique. II.
` Hurwitz, Arnon Max. III. Title.
`
`621.3815′2—dc21
`
`00-059910
` CIP
`
`This book contains information obtained from authentic and highly regarded sources. Reprinted material
`is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable
`efforts have been made to publish reliable data and information, but the author and the publisher cannot
`assume responsibility for the validity of all materials or for the consequences of their use.
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`© 2001 by CRC Press LLC
`
`No claim to original U.S. Government works
`International Standard Book Number 0-8493-1178-0
`Library of Congress Card Number 00-059910
`Printed in the United States of America 1 2 3 4 5 6 7 8 9 0
`Printed on acid-free paper
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`Preface
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`The goal of this book is to provide a practical guide to the understanding, imple-
`mentation, and use of run-to-run (R2R) control in semiconductor manufacturing as
`well as manufacturing in general. The target audience is intentionally wide and
`includes technology directors and strategists, technical managers, control engineers,
`process engineers, systems designers, integrators, and users. The aim of the authors
`is to provide insight into the development, integration, application, enhancement,
`and operation of R2R control. In addition, the book points to new directions in R2R
`process control, some of which have only recently been discussed in the literature.
`These directions point to avenues of opportunity for developing even more effective
`R2R control strategies for the fabricator of the future.
`
`WHO SHOULD USE THIS BOOK
`
`The benefits of R2R control implementation are wide-ranging and affect the many
`levels of the manufacturing hierarchy. As such, this book is structured to provide
`benefit to readers at each of these levels. For example, the following is a sample of
`who might utilize this book as a guide and aid in implementing an effective R2R
`control initiative either on a single tool, or facility-wide in a fabrication facility:
`
`• A
` would utilize the book as a resource
`corporate-level technical strategist
`to:
`1. Collect convincing evidence indicating that R2R control will provide
`significant competitive advantage.
`2. See that proven R2R control solutions are available.
`3. Read that benefits, such as C
` and yield, have been proven and quan-
`pk
`tified.
`4. Plan a strategy for integration.
`• A
` would utilize the book for directing facility-wide R2R
`facility director
`control development and deployment. Specifically, the facility director
`would utilize the book to define plans for:
`1. Identifying target applications for R2R control.
`2. Performing the necessary requirements analysis and identifying equip-
`ment, metrology, control, and integration deficiencies.
`3. Identifying the “control problem” for each candidate process, including
`process quality metrics.
`4. Determining the controllability of each candidate process.
`5. Developing stand-alone control solutions for each candidate process.
`6. Integrating these control solutions for a fab-wide R2R control solution.
`
`© 2001 by CRC Press LLC
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` for each process would utilize the book to develop
`• The
`process engineer
`an effective control solution for his/her process. The book would aid the
`process engineer in:
`1. Process input and output parameter selection and refinement.
`2. Process identification for control.
`3. Development of an industrial-quality solution that addresses require-
`ments of parameter bounds, discretization, parameter weighting, pro-
`cess and metrology noise rejection, etc.
`4. Development, integration, and testing of the control software solution.
`
`HOW TO READ THIS BOOK
`
`We have put this book together with the intent that it be of use to the beginning
`reader in R2R control as well as the specialist seeking detailed information on R2R
`control methods and/or recent directions and developments. In order to achieve this
`we have divided the text into six parts plus a conclusion.
`The Introduction and first
`*
` by the reader new to the subject. These
`chapter of each part
` should be read first
`first chapters have been chosen because they are, on the whole, more introductory
`and less burdened with technicalities than later chapters in the same part. Specialist
`readers may, of course, pick and choose as they wish.
`
`* Excerpt for Part 6: Advanced Topics.
`
`© 2001 by CRC Press LLC
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`Editors
`
`James Moyne, Ph.D.,
` is an Associate Research Scientist in the Electrical Engi-
`neering and Computer Science Department at the University of Michigan, and is
`President and co-founder of MiTeX Solutions, Inc., Canton, Michigan. (MiTeX
`Solutions was acquired by Brooks Automation, Inc. in June 2000.) James received
`his B.S.E.E. and B.S.E. in math, and his M.S.E.E. and Ph.D. in electrical engineering
`from the University of Michigan. He has over 30 refereed publications in the areas
`of discrete control, advanced process control, databases, sensor bus technology, and
`communications, and is the author of the patent on the Generic Cell Controller run-
`to-run control enabling technology. He is also the author of a number of SEMI
`(semiconductor manufacturing) international standards in the areas of sensor bus
`systems and communications, and has received four SEMI outstanding achievement
`awards and a technology transfer award.
`James lives in Canton, Michigan, where his hobbies include writing music and
`playing the keyboard and sax. He is a published poet, has released a solo album of
`New Age music, and is a member of Cornerstone, which is currently working on
`its second Rock/Pop album.
`
`Enrique del Castillo, Ph.D.,
`is an Associate Professor in the Harold and Inge
`Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania
`State University. He holds a Ph.D. in industrial engineering from Arizona State
`University, and a Master of Engineering in operations research and industrial engi-
`neering from Cornell University. Dr. Castillo’s research interests include quality
`engineering and applied statistics, with particular emphasis on response surface
`methodology and time series control. He has over 35 papers in journals such as
`IIE
`,
`,
`,
`,
`Transactions
`Journal of Quality Technology
`Metrika
`Communications in Statistics
`,
`International Journal of Production Research
`European Journal of Operational
`, and
`. He has been awarded
`Journal of the Operational Research Society
`Research
`an NSF CAREER grant for research in semiconductor manufacturing process con-
`trol. Dr. Castillo is an Associate Editor of the
`IIE Transactions on Quality and
` journal and a member of the editorial board of the
`Reliability Engineering
`Journal
`of Quality Technology.
`
`Arnon Max Hurwitz, Ph.D.,
` is Managing Director of Qualtech Productivity
`Solutions, South Africa, and Vice President of MiTeX Solutions, Inc., Canton,
`Michigan. He gained his M.S. in applied statistics from Oxford University, England,
`and his Ph.D. in mathematical statistics from the University of Cape Town, South
`Africa. Dr. Hurwitz lectured at the Graduate School of Business, Cape Town, and
`at Guilford College, North Carolina, and was Head of the Mathematics Department
`at Oak Ridge Military Academy, North Carolina. He was Quality Engineer at Corning
`
`© 2001 by CRC Press LLC
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`Glass, Inc., Telecommunications Division, in Wilmington, North Carolina, and Cor-
`porate Statistician for HIMONT USA, Inc., in Houston, Texas. He was a Senior
`Statistician and a Senior Project Manager at the U.S. semiconductor industry con-
`sortium SEMATECH in Austin, Texas. In 1997 he became Vice President of MiTeX
`Solutions, Inc., and also founded Qualtech Productivity Solutions, a corporation
`supplying statistical analytic services to finance and industry.
`Arnon lives near Cape Town, South Africa, and works internationally. He has
`published a number of statistical and control-theoretic works in leading U.S. journals,
`and has contributed chapters to several books. He has a wife and three children. His
`hobbies are reading, writing, fly-fishing, and trying to play the fiddle.
`
`© 2001 by CRC Press LLC
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`Acknowledgment
`
`The authors would like to thank those who contributed additional chapters for this
`book, and those who co-authored chapters, for their excellent and invaluable con-
`tributions. Names and contact information for all contributors follow, and are listed
`in alphabetical order.
`In addition, we would like to thank Nora Konopka of CRC Press for her
`unflagging support and enthusiasm for the project, as well as all those at CRC Press
`who translated a complex manuscript into publishable form.
`Of course, technical work of this nature has required the support, over quite a
`number of years, of many different entities and personalities, not the least of which
`were the research institutions that gave the technology birth — namely Massachu-
`setts Institute of Technology and the University of Michigan — and the institutes
`that funded further research. In this case it was the semiconductor research corpo-
`ration SEMATECH International, its member companies, and the tool vendors —
`all members of SEMI/SEMATECH — who made their machines and their expert
`staffs available for our numerous site experiments. There are too many names
`involved to mention, and we thank them one and all.
`Lastly, we thank the reviewers of our book for their valuable comments and
`suggestions which added significantly to the value of the text.
`
`James Moyne, Enrique Del Castillo, and Arnon Max Hurwitz
`
`© 2001 by CRC Press LLC
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`Contributors
`
`Duane S. Boning
`Microsystems Technology Laboratories
`Massachusetts Institute of Technology
`Cambridge, MA 02139 USA
`e-mail: boning@mtl.mit.edu
`
`Jonathan Chapple-Sokol
`IBM Microelectronics Division
`1000 River Road B975/E
`Essex Junction, VT 05452 USA
`e-mail: chapple@us.ibm.com
`
`Nauman A. Chaudhry
`One Oracle Drive
`Nashua, NH 03062 USA
`email: nchaudhr@us.oracle.com
`
`Argon Chen
`Graduate Institute of Industrial
`Engineering
`National Taiwan University
`1, Roosevelt Road
`Sec.1, Taipei, Taiwan 106
`e-mail: achen@ccms.ntu.edu.tw
`
`Jin-Jung Chen
`Department of Mechanical Engineering
`National Taiwan University
`Taipei, Taiwan
`
`John Colt
`IBM Microelectronics Division
`1000 River Road B975/E
`Essex Junction, VT 05452 USA
`e-mail: a442991@us.ibm.com
`
`© 2001 by CRC Press LLC
`
`Enrique Del Castillo
`Department of Industrial &
`Manufacturing Engineering
`Pennsylvania State University
`207 Hammond Building
`University Park, PA 16802 USA
`e-mail: exd13@psu.edu
`
`Chadi El Chemali
`Electrical Engineering & Computer
`Science
`University of Michigan
`2360 Bonisteel Avenue
`Ann Arbor, MI 48109 USA
`e-mail: ccel@umich.edu
`
`Ruey-Shan Guo
`Department of Industrial Management &
`Business Administration
`National Taiwan University,
`50, Lane 144, Sec. 4
`Keelung Road
`Taipei, Taiwan
`e-mail: rsguo@ccms.ntu.edu.tw
`
`Arnon Max Hurwitz
`Qualtech Productivity Solutions
`Sanclare Building,
`Dreyer Street
`Claremont 7735 South Africa
`e-mail: qualtech@iafrica.com
`
`Kareemullah Khan
`RA1-303
`Intel Corportaion
`5200 N.E. Elam Young Parkway
`Hillsboro, OR 97124 USA
`e-mail: kareemullah.khan@intel.com
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`James Moyne
`Electrical Engineering & Computer
`Science
`University of Michigan
`2360 Bonisteel Avenue
`Ann Arbor, MI 48109 USA
`e-mail: moyne@umich.edu
`
`William Moyne
`Virtual Ink
`56 Roland Street, Suite 306
`Boston, MA 02129 USA
`e-mail: william.moyne@virtual-ink.com
`
`Rock Nadeau
`IBM Microelectronics Division
`1000 River Road B975/E
`Essex Junction, VT 05452 USA
`e-mail: rnadeau@us.ibm.com
`
`Zhe Ning
`(Information Unavailable)
`
`Tarun Parikh
`SEMATECH
`2706 Montopolis Drive
`Austin, Texas 78741 USA
`e-mail:
`
`Nital S. Patel
`Texas Instruments, Inc.
`13121 TI Boulevard, MS 352
`Dallas, TX 75243 USA
`e-mail: nsp@ti.com
`
`Elke A. Rundensteiner
`Department of Computer Science
`Worcester Polytechnic Institute
`100 Institute Road
`Worcester, MA 01609 USA
`email: rundenst@owl.WPI.EDU
`
`Paul H. Smith
`IBM Microelectronics Division
`1000 River Road B975/E
`Essex Junction, VT 05452 USA
`e-mail: smitpaul@us.ibm.com
`
`Taber Smith
`Microsystems Technology Laboratories
`Massachusetts Institute of Technology
`Cambridge, MA 02139 USA
`e-mail: taber@mit.edu
`
`Victor Solakhian
`Brooks Automation, Inc.
`15 Elizabeth Drive
`Chelmsford, MA 05124 USA
`Victor.Solakhian@brooks.com
`
`Robert A. Soper
`Texas Instruments, Inc.
`13570 N. Central Expressway, MS 3701
`Dallas, TX 75243 USA
`e-mail: soper@ti.com
`
`Joe White
`Crystal Semiconductor
`USA
`e-mail: joew@crystal.cirrus.com
`
`Jinn-Yi Yeh
`Department of Industrial Engineering
`The Dayeh University
`112, Shan-Jiau Road
`Da-Tsuen, Changhua, 5105
`Taiwan, R.O.C.
`email: jyeh@mail.dyu.edu.tw
`
`© 2001 by CRC Press LLC
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`Foreword
`
`Mark Melliar-Smith, President and CEO, and Randy Goodall, Associate
`Director of Productivity and Infrastructure
`International SEMATECH, Austin, Texas
`
`Control is essential for all manufacturing, but every day in the semiconductor
`industry more than a quadrillion transistors, each with dimensions ranging from a
`fraction of a micrometer down to tens of atoms, must be profitably fabricated. To
`manage the exponentially rising cost of meeting this manufacturing challenge, pro-
`cess equipment from each technology generation is increasingly pressed into service
`to support the next generation. In the early years of the twenty-first century, new
`processes for nearly every aspect of transistor fabrication, from thin gate and
`source/drain to interconnecting metal and dielectrics, must be introduced in semi-
`conductor factories around the world.
`The International Technology Roadmap for
` shows starkly that the timing for these technology changes is so
`Semiconductors
`short that we will necessarily “test them in combat.” Active control mechanisms,
`such as the run-to-run methods described in this book, are mandatory if the industry
`is to keep pace with the world’s demand for new electronic products.
`During this period of unprecedented technological advancement, semiconductor
`companies are also initiating the new generation of larger, 300 mm silicon wafers.
`The product value of even a single 300 mm wafer containing more than 1000 advanced
`chips dictates the use of active process control with a new level of urgency. Although
`a staggering challenge, the 300 mm transition also brings with it a new opportunity.
`International SEMATECH’s member companies have collectively and comprehen-
`sively set a high bar for equipment performance in all areas, including factory
`communications and recipe management. Process control implementations should
`now become more straightforward.
`Widespread deployment of run-to-run control has been somewhat of a struggle
`because of the required critical mass of software and communications capability
`necessary in both process equipment and factory systems. Equally scarce were the
`people to drive the development, engineering, and adoption of these techniques.
`With the new generation of software-savvy engineers at both semiconductor and
`supplier companies, this is changing. In addition, new sensors and other lower-cost
`measurement options are becoming available to reduce the time, money, and logistics
`needed to support cost-effective control implementations.
`Interest in better control of process equipment arose at SEMATECH in the early
`1990s. The run-to-run method was then, and continues to be, the least equipment-
`invasive control scheme that demonstrates real benefit. It is gratifying to see the
`ideas supported by SEMATECH reach a level of maturity and industry acceptance
`that supports treatment in a book of their own. This volume represents the continuance
`
`© 2001 by CRC Press LLC
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`of nearly a decade of active work and collaboration by these authors, in both the
`academic setting of their respective universities as well as the industry context of
`SEMATECH. Their long-time focus on the specific control problems of semicon-
`ductor manufacturing imbue this effort with relevance and practicality. We believe
`this book will find an industry ready for standard approaches and solutions for run-
`to-run control. Our hope is that it accelerates the emergence of the new era of
`sophistication in controlling the manufacturing marvel that is the semiconductor
`industry.
`
`© 2001 by CRC Press LLC
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`Table of Contents
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`Introduction
`James Moyne and Arnon M. Hurwitz
`
`PART 1: FOUNDATION FOR CONTROL
`
`Chapter 1
`Process Control in the Semiconductor Industry
`Taber H. Smith, Duane S. Boning, and James Moyne
`
`Chapter 2
`Process Control and Optimization Methods for Run-to-Run Application
`Enrique Del Castillo and Arnon M. Hurwitz
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`PART 2: R2R CONTROL ALGORITHMS
`
`Chapter 3
`Basic R2R Control Algorithms
`William Moyne
`
`Chapter 4
`Learning and Optimization Algorithms for an Optimizing Adaptive Quality
`Controller
`Enrique Del Castillo
`
`Chapter 5
`An Adaptive Run-to-Run Optimizing Controller for Linear and Nonlinear
`Processes
`Arnon M. Hurwitz and Enrique Del Castillo
`
`Chapter 6
`A Comparative Analysis of Run-to-Run Control Algorithms in the
`Semiconductor Manufacturing Industry
`Zhe Ning, James Moyne, Taber Smith, Duane Boning, Enrique Del Castillo,
`Jinn-Yi Yeh, and Arnon M. Hurwitz
`
`© 2001 by CRC Press LLC
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`PART 3:
`
`INTEGRATING CONTROL
`
`Chapter 7
`Existing and Envisioned Control Environment for Semiconductor
`Manufacturing
`James Moyne and Joe White
`
`Chapter 8
`Design Requirements for an Integrative R2R Control Solution
`James Moyne
`
`Chapter 9
`The Generic Cell Controller
`James Moyne
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`Chapter 10
`Derivation of a Piggyback Run-to-Run Control Solution Design
`James Moyne
`
`Chapter 11
`Integrated Run-to-Run Control Solution Examples
`James Moyne
`
`Chapter 12
`Design and Optimization of an Optimizing Adaptive Quality Controller,
`Generic Cell Controller Enabled Solution
`Enrique Del Castillo, Jinn-Yi Yeh, James Moyne, and Victor Solakhian
`
`PART 4: CUSTOMIZATION METHODOLOGY
`
`Chapter 13
`Case Study: Furnace Capability Improvement Using a Customized
`Run-to-Run Control Solution
`Arnon Hurwitz and James Moyne
`
`Chapter 14
`Process Recipe Optimization
`Enrique Del Castillo
`
`© 2001 by CRC Press LLC
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`PART 5: CASE STUDIES
`
`Chapter 15
`Multizone Uniformity Control of a CMP Process Utilizing a Pre- and
`Postmeasurement Strategy
`James Moyne, Chadi El Chemali, Kareemullah Khan, Rock Nadeau,
`Paul Smith, John Colt, Jonathan Chapple-Sokol, and Tarun Parikh
`
`Chapter 16
`Control of Photolithography Alignment
`Nital S. Patel and Robert Soper
`
`Chapter 17
`Age-Based Double EWMA Controller and Its Application to a CMP
`Process
`Argon Chen and Ruey-Shan Guo
`
`PART 6: ADVANCED TOPICS
`
`Chapter 18
`Advancements in Chemical Mechanical Planarization Process Automation
`and Control
`James Moyne
`
`Chapter 19
`An Enhanced Exponentially Weighted Moving Average Controller for
`Processes Subject to Random Disturbances
`Ruey-Shan Guo, Argon Chen, and Jin-Jung Chen
`
`Chapter 20
`Enabling Generic Interprocess Multistep Control: the Active Controller
`Nauman Chaudhry, James Moyne, and Elke A. Rundensteiner
`
`PART 7: SUMMARY AND CONCLUSIONS
`
`List of Acronyms
`
`© 2001 by CRC Press LLC
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`Dedication
`
`This book is dedicated to our wives,
`Jennifer, Monica, and Mary Frances,
`who make it all worthwhile.
`
`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
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`List of Acronyms
`
`AC
`AEC
`APC
`ARL
`AT&T
`BCAM
`CDM
`CIM
`CMP
`
`COO
`CORBA
`Cpk
`CSRS
`CSSWG
`CTE
`CVD
`DBMS
`d-EWMA
`DOE
`DOF
`ECA
`ECS-TF
`EPC
`E-R
`EWMA
`FDC
`FWI
`GCC
`GEM
`GM
`GMt
`GUI
`HSMS
`I/O
`I300I
`IBM
`ILD
`
`Active Controller
`advanced equipment control
`advanced process control
`average run length
`American Telephone and Telegraph
`Berkeley Computer-Aided Manufacturing
`(SAN) common device model
`computer-integrated manufacturing
`chemical mechanical planarization or chemical mechanical
`polishing
`cost of ownership
`Common Object Request Broker Architecture
`process capability
`Control Systems Requirements Specification
`Control Systems Specification Working Group
`center-to-edge (nonuniformity)
`chemical vapor deposition
`database management system
`double exponentially-weighted moving average
`design of experiments
`depth of focus
`event–condition–action (rules)
`Equipment Control Systems Task Force (of SEMI)
`engineering process control
`entity-relationship (database modeling)
`exponentially-weighted moving average
`fault detection and classification
`full wafer interferometry
`Generic Cell Controller
`Generic Equipment Model
`gradual mode (algorithm)
`time-based (extended) gradual model (algorithm)
`graphical user interface
`high-speed message service
`input/output
`International 300-mm Initiative
`International Business Machines
`interlevel dielectric
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`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1008, Page 16 of 341
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`integrated moving average
`IMA
`internal model control
`IMC
`knowledge-based interactive run-to-run controller
`KIRC
`manufacturing execution system
`MES
`multiple input, multiple output
`MIMO
`minimum mean squared error
`MMSE
`mean square(d) deviation
`MSD
`mean square error
`MSE
`(SAN) Network Communication Standard
`NCS
`non-product (wafers)
`NP
`National Science Foundation
`NSF
`optimizing adaptive quadratic controller
`OAQC
`Object-Based Equipment Model
`OBEM
`overall equipment effectiveness
`OEE
`original equipment manufacturer
`OEM
`optical emission spectroscopy
`OES
`object modeling technique
`OMT
`predictor–corrector controller
`PCC
`Process Flow Specification Manager
`PFM
`proportional integral differential
`PID
`partial least squares
`PLS
`process maintenance
`PM
`run-to-run
`R2R
`right-hand side
`RHS
`recursive least squares
`RLS
`removal rate
`RR
`Sensor Actuator Network
`SAN
`(SAM) specific device model
`SDM
`SEMI Equipment Communication Standard
`SECS
`SEMATECH Semiconductor Manufacturing Technology
`SEMI
`Semiconductor Equipment and Materials International
`SIA
`Semiconductor Industry Association
`SISO
`single input, single output
`SPC
`statistical process control
`SRC
`Semiconductor Research Corp.
`TI
`Texas Instruments
`USD
`United States dollars
`VLSI
`very large-scale integration
`WECO
`Western Electric Co. (SPC rules)
`WMSE
`weighted mean-squared error
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`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1008, Page 17 of 341
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`Introduction
`
`James Moyne and Arnon Max Hurwitz
`
`The semiconductor manufacturing industry is arguably the fastest evolving major
`industry in the world. Success in the industry requires constant attention to the state
`of the art in process tools, process chemistries and physics, and techniques for pro-
`cessing and process improvement. The two major fronts along which product advance-
`ments are made in this industry are minimum feature size and wafer dimension. At
`the time of this writing, the “state-of-the-art” minimum feature size was in the 250 to
`180 nm range, while processing on 300 mm wafers was becoming more prevalent.
`As feature sizes shrink and wafer sizes increase, the industry must innovate to
`maintain acceptable product yield, throughput, and overall equipment effectiveness
`(OEE). Some manufacturing capability attributes, such as non-product wafer (NPW)
`usage and wafer scrap, must actually be improved in the transition to larger wafer
`sizes because of the increased value of 300 mm wafers (raw and processed). For
`example, one user reported that a raw 300 mm wafer cost approximately $1500 to
`$2000.* Although the cost of a raw 300 mm wafer may ultimately drop to a few
`hundred dollars, the value of a processed 300 mm wafer increases during its many
`process steps, ultimately representing more than 1000 devices worth $10 to $100
`each. Faults introduced in any stage of manufacturing will often only show up in
`final electronic testing, and the consequent device loss may be (cost-wise) quite
`devastating — especially with these large-diameter wafers.
`Although a number of solutions, including improved equipment design and
`process innovation, will continue to aid in making these transitions cost effective,
`it has become clear that they are no longer sufficient. Specifically, it has become
`generally accepted that process and wafer quality sensing and subsequent process
`tuning will be required to complement these equipment and process improvements.
`The main form of process tuning that is being implemented as a standard process
`and equipment control solution in the industry is
`. As will
`run-to-run (R2R) control
`be shown throughout this book, R2R control is now a proven and available technol-
`ogy, and has become a critical component of the success of existing and next-
`generation fabrication facilities.
`In this introduction we provide important information that lays a foundation for
`understanding the concepts, motivation, and directions presented throughout the text.
`Specifically, in the following sections we provide a definition of R2R control, explore
`the qualities of a VLSI process candidate for R2R control, describe basic character-
`istics of R2R control systems, provide a brief history of the development of R2R
`control as a component of advanced process control, and summarize the layout of
`the book.
`
`* W. Rozich, IBM,
`
`, Vail, CO (1999).
`SEMATECH AEC/APC Symposium XI
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`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1008, Page 18 of 341
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`1 WHAT IS RUN-TO-RUN CONTROL?
`
`Run-to-run control is a form of discrete process and machine control in which the
`product recipe with respect to a particular machine process is modified
`, i.e.,
`ex situ
`between machine “runs,” so as to minimize process drift, shift, and variability. This
`type of control is event-driven, where the events include the determination and
`reporting of pre- and/or postprocess
` metrology data, and the requirement of
`ex situ
`the tool to begin processing. The input/output structure of a typical R2R control
`solution is shown in Figure 1. Note the granularity of control could be wafer-to-
`wafer, or batch-to-batch, etc.
`A typical scheme for utilization of R2R control for a CMP polishing tool is
`illustrated in Figure 2. (
` GCC stands for Generic Cell Controller, a control
`Note:
`structure to be discussed in Part 3 of this book.) Note that the metrology and
`automation scheme for R2R control can vary widely. For example, the metrology
` metrology, but could include
` equipment state
`is generally limited to
`ex situ
`in situ
`
`FIGURE 1
`
`Input/output structure of a typical R2R control solution.
`
`FIGURE 2
`
`Typical R2R control application — R2R control of a CMP process.
`
`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1008, Page 19 of 341
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`and wafer state information. The (
`) premetrology capability may or may not
`ex situ
`be available. The metrology could be in-line or off-line, i.e., it could be directly
`integrated (mechanically and electrically) into the process line, or could exist as a
`stand-alone metrology station. The metrology could be fully integrated into a single
`tool, or could be integrated into the process line as both a postmetrology capability
`for a process and a premetrology capability for the subsequent downstream process.
`
`2 VLSI TOOLS: THE EXAMPLE OF CMP
`
`As VLSI (very large scale integration) technology advances, the feature sizes of both
`the underlying devices and the underlying metal line widths decrease. With this
`decrease comes increased transistor speed and density, but also a need for more
`layers of metal interconnect. Thus interconnect technology is the center of much of
`today’s VLSI research.
`One of the major problems with fabricating additional layers of metal intercon-
`nect is that the topography of the silicon wafer becomes increasingly nonplanar as
`levels of metal are added. This, coupled with the demand for increasingly smaller
`geometries, has led to some problems previously unseen. First, due to the clarity of
`image needed for submicron geometries, the focal depth of lithography machines
`has decreased. This reduced focal depth results in some of the topography of the
`wafer being out of focus when other parts are in focus (see Figure 3). This is
`unacceptable as geometries shrink.
`In addition to lithography concerns, the nonplanar surface can lead to difficult
`processing as the aspect ratio of the valleys of the wafer becomes great enough that
`the interconnect metal is unable to fill and cover these areas. This effect can lead to
`circuit failure due to metal fatigue or lack of connection entirely. Figure 4 shows a
`typical nonplanar process as well as an ideal one.
`There are many techniques used to increase planarity. Most involve applying a
`level of glass or oxide in an attempt to fill the valleys that can lead to trouble later
`on. The problem is that the peaks are also extended to some extent, so it is very
`difficult to achieve planarity through this process alone. The process of etching peaks
`
`Lamp
`
`Mask
`
`Enlarging Lens
`
`Focusing Lens
`
`P+
`
`P+
`
`P+
`
`P+
`
`N
`
`Enlarging lithography system
`
`Focal plane (clear)
`Beyond focal depth
`(blurred image)
`
`FIGURE 3
`
`Enlarging lithography system.
`
`© 2001 by CRC Press LLC
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1008, Page 20 of 341
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`Non-Planar Process
`Possible fault
`
`Ideal Process
`
`P+
`
`P+
`
`P+
`
`P+
`
`P+
`
`P+
`
`P+
`
`P+
`
`N
`
`N
`
`Oxide
`Metal
`
`Comparison between non-planar and planar processes
`
`FIGURE 4
`
`Comparison between nonp