This course explores statistical modeling and control in manufacturing processes. Topics include the use of experimental design and response surface modeling to understand manufacturing process physics, as well as defect and parametric yield modeling and optimization. Various forms of process control, including statistical process control, run by run and adaptive control, and real-time feedback control, are covered. Application contexts include semiconductor manufacturing, conventional metal and polymer processing, and emerging micro-nano manufacturing processes.
Control of Manufacturing Processes (SMA 6303)
Massachusetts Institute of Technology via MIT OpenCourseWare
Overview
Syllabus
- 1: Introduction — Processes and Variation Framework
- 2: Semiconductor Process Variation
- 3: Mechanical Process Variation
- 4: Probability Models of Manufacturing Processes
- 5: Probability Models, Parameter Estimation, and Sampling
- 6: Sampling Distributions and Statistical Hypotheses
- 7: Shewhart SPC and Process Capability
- 8: Process Capability and Alternative SPC Methods
- 9: Advanced and Multivariate SPC
- 10: Yield Modeling
- 11: Introduction to Analysis of Variance
- 12: Full Factorial Models
- 13: Modeling Testing and Fractional Factorial Models
- 14: Aliasing and Higher Order Models
- 15: Response Surface Modeling and Process Optimization
- 16: Process Robustness
- 17: Nested Variance Components
- 18: Sequential Experimentation
- 19: Case Study 1: Tungsten CVD DOE/RSM
- 20: Case Study 2: Cycle to Cycle Control
- 21: Case Study 3: Spatial Modeling
- 22: Case Study 4: "Modeling the Embossing/Imprinting of Thermoplastic Layers."
Taught by
Prof. Duane Boning and Prof. David Hardt
