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Control of Manufacturing Processes S08 2.830J MIT

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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.

Language: English

Professors: Michael Williams

Units: 22

Lesson content

Lec 1 Introduction - processes and variation frame...
Lec 2 Semiconductor process variation.
Lec 3 Mechanical process variation -- physical causes and interpreting data
Lec 4 Probability models of manufacturing processes
Lec 5 Probability models, parameter estimation, and sampling
Lec 6 Sampling distributions and statistical hypotheses
Lec 7 Shewhart SPC and process capability
Lec 8 Process capability and alternative SPC methods
Lec 9 Advanced and multivariate SPC
Lec 10 Yield modeling
Lec 11 Introduction to analysis of variance
Lec 12 Full factorial models
Lec 13 Modeling testing and fractional factorial models
Lec 14 Aliasing and higher order models
Lec 15 Response surface modeling and process optimization
Lec 16 Process robustness
Lec 17 Nested variance components
Lec 18 Sequential experimentation: Experimentation and Robust Design and Engineering Systems.
Lec 19 Case study 1: tungsten CVD DOE/RSM
Lec 20 Case study 2: cycle to cycle control
Lec 21 Case study 3: spatial modeling
Lec 22 Modeling the Embossing/Imprinting of Thermoplastic Layers.

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Manufacturing & Production