Catalog

Control of Manufacturing Processes (SMA 6303)

Mechanical Engineering MIT CC BY-NC-SA 4.0 22 lectures

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.

Syllabus

  1. 1 1: Introduction — Processes and Variation Framework
  2. 2 2: Semiconductor Process Variation
  3. 3 3: Mechanical Process Variation
  4. 4 4: Probability Models of Manufacturing Processes
  5. 5 5: Probability Models, Parameter Estimation, and Sampling
  6. 6 6: Sampling Distributions and Statistical Hypotheses
  7. 7 7: Shewhart SPC and Process Capability
  8. 8 8: Process Capability and Alternative SPC Methods
  9. 9 9: Advanced and Multivariate SPC
  10. 10 10: Yield Modeling
  11. 11 11: Introduction to Analysis of Variance
  12. 12 12: Full Factorial Models
  13. 13 13: Modeling Testing and Fractional Factorial Models
  14. 14 14: Aliasing and Higher Order Models
  15. 15 15: Response Surface Modeling and Process Optimization
  16. 16 16: Process Robustness
  17. 17 17: Nested Variance Components
  18. 18 18: Sequential Experimentation
  19. 19 19: Case Study 1: Tungsten CVD DOE/RSM
  20. 20 20: Case Study 2: Cycle to Cycle Control
  21. 21 21: Case Study 3: Spatial Modeling
  22. 22 22: Case Study 4: "Modeling the Embossing/Imprinting of Thermoplastic Layers."

Course materials