Presentation: Exploring Best Practices in Design of Experiments - JMP

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Photolithography Example • Consider five factors in a photolithography process – A = aperture setting – B = exposure time – C = develop time – D = mask dimension – E = etch time With each factor at two levels the resulting number of runs is: 2 5 = 32
Properties of this design • Orthogonal • Makes interpretation easy • Minimizes the variance of the model coefficients • Minimizes the average prediction variance • Minimizes the maximum prediction variance • You can’t do any better than this – (for five two-level factors and 32 runs)!
Page 1 and 2: Exploring Best Practices in Design
Page 3 and 4: What Is a Designed Experiment? a st
Page 5 and 6: What Is a Response? A response is a
Page 7 and 8: What is a model? a simplified mathe
Page 9 and 10: ANOVA and regression models are equ
Page 11 and 12: The Model/Design Relationship - Par
Page 13 and 14: The Model/Design Relationship - Ali
Page 15 and 16: Design Optimality Criteria D-optima
Page 17 and 18: Module 1 - Conclusions 1. Remember
Page 19: Optimal Full Factorial Full Factor
Page 23 and 24: Optimal Fractional Factorial Fract
Page 25 and 26: The 2 5-1 • Aliases: - All main e
Page 27 and 28: Module 2 - Summary 1. Main message
Page 29 and 30: Standard Fractional Factorial Desig
Page 31 and 32: Plackett-Burman Designs • These a
Page 33 and 34: JMP Demo
Page 35 and 36: If only we could find a design with
Page 37: How do we get generate this design?
Page 40 and 41: JMP Demo
Page 43 and 44: What if we are not willing to give
Page 45: JMP Demo Alias Optimal Standard Fra
Page 48 and 49: Robust Screening Design Properties
Page 50 and 51: Definitive Screening Designs for 4
Page 52 and 53: Alias Matrix
Page 54 and 55: Definitive Screening Coefficient Va
Page 56 and 57: Definitive Screening Stepwise Resul
Page 58 and 59: Explorers 2011 - Final Thoughts 1.

Presentation: Exploring Best Practices in Design of Experiments - JMP

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