Synopses & Reviews
A practical guide to semiconductor manufacturing from process control to yield modeling and experimental design
Fundamentals of Semiconductor Manufacturing and Process Control covers all issues involved in manufacturing microelectronic devices and circuits, including fabrication sequences, process control, experimental design, process modeling, yield modeling, and CIM/CAM systems. Readers are introduced to both the theory and practice of all basic manufacturing concepts.
Following an overview of manufacturing and technology, the text explores process monitoring methods, including those that focus on product wafers and those that focus on the equipment used to produce wafers. Next, the text sets forth some fundamentals of statistics and yield modeling, which set the foundation for a detailed discussion of how statistical process control is used to analyze quality and improve yields.
The discussion of statistical experimental design offers readers a powerful approach for systematically varying controllable process conditions and determining their impact on output parameters that measure quality. The authors introduce process modeling concepts, including several advanced process control topics such as run-by-run, supervisory control, and process and equipment diagnosis.
Critical coverage includes the following:
- Combines process control and semiconductor manufacturing
- Unique treatment of system and software technology and management of overall manufacturing systems
- Chapters include case studies, sample problems, and suggested exercises
- Instructor support includes electronic copies of the figures and an instructor's manual
Graduate-level students and industrial practitioners will benefit from the detailed exami?nation of how electronic materials and supplies are converted into finished integrated circuits and electronic products in a high-volume manufacturing environment.
Review
"…offers insight into the IC manufacturing process…[to] the practicing engineer or interested professional." (IEEE Circuits & Devices Magazine, November/December 2006)
About the Author
GARY S. MAY, PhD, is Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. Dr. May is a Fellow of the IEEE and Senior Member of the Society of Manufacturing Engineers. He has published more than 150 articles and given over 100 technical presentations in the area of IC computer-aided manufacturing.
COSTAS J. SPANOS, PhD, is Professor in the Department of Electrical Engineering and Computer Sciences and the Associate Dean for Research for the College of Engineering at the University of California, Berkeley. Dr. Spanos is a Fellow of the IEEE and has published extensively in the area of semiconductor manufacturing.
Table of Contents
Preface.Acknowledgments.
1. Introduction to Semiconductor Manufacturing.
Objectives.
Introduction.
1.1. Historical Evolution.
1.2. Modern Semiconductor Manufacturing.
1.3. Goals of Manufacturing.
1.4. Manufacturing Systems.
1.5. Outline for Remainder of the Book.
Summary.
Problems.
References.
2. Technology Overview.
Objectives.
Introduction.
2.1. Unit Processes.
2.2. Process Integration.
Summary.
Problems.
References.
3. Process Monitoring.
Objectives.
Introduction.
3.1. Process Flow and Key Measurement Points.
3.2. Wafer State Measurements.
3.3. Equipment State Measurements.
Summary.
Problems.
References.
4. Statistical Fundamentals.
Objectives.
Introduction.
4.1. Probability Distributions.
4.2. Sampling from a Normal Distribution.
4.3. Estimation
4.4. Hypothesis Testing.
Summary.
Problems.
Reference.
5. Yield Modeling.
Objectives.
Introduction.
5.1. Definitions of Yield Components.
5.2. Functional Yield Models.
5.3. Functional Yield Model Components.
5.4. Parametric Yield.
5.5. Yield Simulation.
5.6. Design Centering.
5.7. Process Introduction and Time-to-Yield.
Summary.
Problems.
References.
6. Statistical Process Control.
Objectives.
Introduction.
6.1. Control Chart Basics.
6.2. Patterns in Control Charts.
6.3. Control Charts for Attributes.
6.4. Control Charts for Variables.
6.5. Multivariate Control.
6.6. SPC with Correlated Process Data.
Summary.
Problems.
References.
7. Statistical Experimental Design.
Objectives.
Introduction.
7.1. Comparing Distributions.
7.2. Analysis of Variance.
7.3. Factorial Designs.
7.4. Taguchi Method.
Summary.
Problems.
References.
8. Process Modeling.
Objectives.
Introduction.
8.1. Regression Modeling.
8.2. Response Surface Methods.
8.3. Evolutionary Operation.
8.4. Principal-Component Analysis.
8.5. Intelligent Modeling Techniques.
8.6. Process Optimization.
Summary.
Problems.
References.
9. Advanced Process Control.
Objectives.
Introduction.
9.1. Run-by-Run Control with Constant Term Adaptation.
9.2. Multivariate Control with Complete Model Adaptation.
9.3. Supervisory Control.
Summary.
Problems.
References.
10. Process and Equipment Diagnosis.
Objectives.
Introduction.
10.1. Algorithmic Methods.
10.2. Expert Systems.
10.3. Neural Network Approaches.
10.4. Hybrid Methods.
Summary.
Problems.
References.
Appendix A: Some Properties of the Error Function.
Appendix B: Cumulative Standard Normal Distribution.
Appendix C: Percentage Points of the χ2 Distribution.
Appendix D: Percentage Points of the t Distribution.
Appendix E: Percentage Points of the F Distribution.
Appendix F: Factors for Constructing Variables Control Charts.
Index.