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This course explores statistical modeling and control in manufacturing processes.
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English
English [CC]
FREE
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Description
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.
Course content
- Introduction—Processes and Variation Framework Unlimited
- Semiconductor Process Variation Unlimited
- Mechanical Process Variation Unlimited
- Probability Models of Manufacturing Processes Unlimited
- Probability Models, Parameter Estimation, and Sampling Unlimited
- Sampling Distributions and Statistical Hypotheses Unlimited
- Shewhart SPC and Process Capability Unlimited
- Process Capability and Alternative SPC Methods Unlimited
- Yield Modeling Unlimited
- Introduction to Analysis of Variance Unlimited
- Full Factorial Models Unlimited
- Modeling Testing and Fractional Factorial Models Unlimited
- Aliasing and Higher Order Models Unlimited
- Response Surface Modeling and Process Optimization Unlimited
- Process Robustness Unlimited
- Nested Variance Components Unlimited
- Sequential Experimentation Unlimited
- Case Study 1: Tungsten CVD DOE/RSM Unlimited
- Case Study 2: Cycle to Cycle Control Unlimited
- Case Study 3: Spatial Modeling Unlimited
- Case Study 4: “Modeling the Embossing/Imprinting of Thermoplastic Layers.” Unlimited
Instructor
Massachusetts Institute of Technology
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