Two-Level Factorials (cont.)

Size: px
Start display at page:

Download "Two-Level Factorials (cont.)"

Transcription

1 EE0H 0 wolevel actorials (cont.) Blocking and Confounding ractional actorials he concept of design Resolution EE0H 0 Blocking What if we need to clean the reactor every four runs? block his block will bias the interaction. he generator is =.

2 EE0H 0 A simple Example in Blocking Do experiments, at levels of b, and estimate the effects of x and x x b x EE0H 0 Blocking What if we need to clean the reactor every two runs? his will bias the, interactions but also the effect!! We used two generators: = (b ) = (b ) = x = = = b b block V V

3 EE0H 0 or two runs/block ( blocks) better to use this blocking: b his blocking will bias the, and We used two generators: = (b ) = (b ) = x = = he main effects are still recognizable! b blck V V Blocking EE0H 0 0 A B C D block Examples on Blocking design ( runs) in blocks of runs: Need one generator: = (i.e. block=)

4 EE0H 0 Examples on Blocking design ( runs) in blocks of runs: Need two generators: B =, B = 0 A B C D B B EE0H 0 Can we measure the effect of the added variable? x x x

5 EE0H 0 Large Experiments have many insignificant Effects... Effects of experiment Residuals after eliminating insignificant effects EE0H 0 Large experiments can be cut to half without significant conflicts... 0

6 EE0H 0 ractional actorials Do need runs to estimate significant parameters? fractional factorial (half fraction) he generating relation is = = = = (=) = = = = avg x0. Since,, and are insignificant, this design works. (his is a resolution design.) EE0H 0 ractional actorial he fractional factorial is a complete factorial for any of the variables! () () () () () () () () () () () () () () () () () () () ()

7 EE0H 0 Combining Half ractions = = = = avg x0. = = = = avg x0. EE0H 0 Designs of "Resolution R" No pfactor confounded with anything less than Rp factors. Example: = is a resolution design. Example: onehalf fractional of the LCVD experiment = => runs! = ( ) eff = 0. = ( ) eff = 0. = ( ) eff = 0. = (Avg 0. ) Avg =. avg

8 EE0H 0 ractional actorial 0 A B C D E EE0H 0 Obtaining the Highest ossible Resolution Write a full factorial for the first k variables Associate the kth variable with / the interaction of the k variables. A fractional factorial of of resolution R contains complete factorials in in every set of of R variables!

9 EE0H 0 Example: Modeling lasma Etch Anisotropy arameter Range Units R ower 0000 Watts ressure 0000 morr Electrode Spacing.. cm CCl low 000 sccm He low 000 sccm O low 00 sccm he original experiment included = runs (plus center point replications). A = quarter fraction was used for anisotropy, because the measurements were costly and noisy. EE0H 0 Example: Designing the quarter fraction 0 = = V Rf r El

10 EE0H 0 ractional factorials can be blocked. Just choose interactions that are unimportant. = = 0 Blocked ractional actorial Experiments Rf r El BBB V EE0H 0 ypical Designs are available... 0

11 EE0H 0 Resolving Ambiguities in ractional actorials Upon the completion of a fractional factorial, selected confoundings can be clarified with selected additional runs. = = Suppose that is significant. Since l = ( ) we need a minimum of additional runs. Because of potential blocking between the main experiment and the supplement, we actually need additional runs. One choice is: Rf r El Results R R R inally, solve the system: M 0.l 0.l 0.l = R M 0.l 0.l 0.l = R M 0.l 0.l 0.l = R l l l = l conf EE0H 0 Conclusion actorial experiments can accommodate blocking, if one controls the conflicts in estimating effects. ractional factorial experiments take advantage of the insignificance of higher order terms, to accommodate many variables with few runs. Experiments can be done in stages, initially screening, and later analyzing important effects in detail. (see chapter in BHH or chapter in Montgomery)

Two-Level Factorials (cont.)

Two-Level Factorials (cont.) TwoLevel Factorials (cont.) Blocking and Confounding Fractional Factorials The concept of design Resolution 1 EE290H F05 2 What if we need to clean the reactor every four runs? Run 1 2 3 4 5 6 7 8 P T

More information

Session 3 Fractional Factorial Designs 4

Session 3 Fractional Factorial Designs 4 Session 3 Fractional Factorial Designs 3 a Modification of a Bearing Example 3. Fractional Factorial Designs Two-level fractional factorial designs Confounding Blocking Two-Level Eight Run Orthogonal Array

More information

Two Factor Full Factorial Design with Replications

Two Factor Full Factorial Design with Replications Two Factor Full Factorial Design with Replications Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu These slides are available on-line at: http://www.cse.wustl.edu/~jain/cse567-08/

More information

Two-Level Fractional Factorial Design

Two-Level Fractional Factorial Design Two-Level Fractional Factorial Design Reference DeVor, Statistical Quality Design and Control, Ch. 19, 0 1 Andy Guo Types of Experimental Design Parallel-type approach Sequential-type approach One-factor

More information

Experimental design (KKR031, KBT120) Tuesday 11/ :30-13:30 V

Experimental design (KKR031, KBT120) Tuesday 11/ :30-13:30 V Experimental design (KKR031, KBT120) Tuesday 11/1 2011-8:30-13:30 V Jan Rodmar will be available at ext 3024 and will visit the examination room ca 10:30. The examination results will be available for

More information

2 k, 2 k r and 2 k-p Factorial Designs

2 k, 2 k r and 2 k-p Factorial Designs 2 k, 2 k r and 2 k-p Factorial Designs 1 Types of Experimental Designs! Full Factorial Design: " Uses all possible combinations of all levels of all factors. n=3*2*2=12 Too costly! 2 Types of Experimental

More information

23. Fractional factorials - introduction

23. Fractional factorials - introduction 173 3. Fractional factorials - introduction Consider a 5 factorial. Even without replicates, there are 5 = 3 obs ns required to estimate the effects - 5 main effects, 10 two factor interactions, 10 three

More information

Multivariate Control and Model-Based SPC

Multivariate Control and Model-Based SPC Multivariate Control and Model-Based SPC T 2, evolutionary operation, regression chart. 1 Multivariate Control Often, many variables must be controlled at the same time. Controlling p independent parameters

More information

5. Blocking and Confounding

5. Blocking and Confounding 5. Blocking and Confounding Hae-Jin Choi School of Mechanical Engineering, Chung-Ang University 1 Why Blocking? Blocking is a technique for dealing with controllable nuisance variables Sometimes, it is

More information

Response Surface Methodology

Response Surface Methodology Response Surface Methodology Process and Product Optimization Using Designed Experiments Second Edition RAYMOND H. MYERS Virginia Polytechnic Institute and State University DOUGLAS C. MONTGOMERY Arizona

More information

Response Surface Methodology:

Response Surface Methodology: Response Surface Methodology: Process and Product Optimization Using Designed Experiments RAYMOND H. MYERS Virginia Polytechnic Institute and State University DOUGLAS C. MONTGOMERY Arizona State University

More information

TWO-LEVEL FACTORIAL EXPERIMENTS: BLOCKING. Upper-case letters are associated with factors, or regressors of factorial effects, e.g.

TWO-LEVEL FACTORIAL EXPERIMENTS: BLOCKING. Upper-case letters are associated with factors, or regressors of factorial effects, e.g. STAT 512 2-Level Factorial Experiments: Blocking 1 TWO-LEVEL FACTORIAL EXPERIMENTS: BLOCKING Some Traditional Notation: Upper-case letters are associated with factors, or regressors of factorial effects,

More information

UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences. PROBLEM SET No. 5 Official Solutions

UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences. PROBLEM SET No. 5 Official Solutions 1 UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences C. SPANOS Special Issues in Semiconductor Manufacturing EECS 290H Fall 0 PROBLEM SET No. Official

More information

Response Surface Methodology IV

Response Surface Methodology IV LECTURE 8 Response Surface Methodology IV 1. Bias and Variance If y x is the response of the system at the point x, or in short hand, y x = f (x), then we can write η x = E(y x ). This is the true, and

More information

Design of Engineering Experiments Chapter 8 The 2 k-p Fractional Factorial Design

Design of Engineering Experiments Chapter 8 The 2 k-p Fractional Factorial Design Design of Engineering Experiments Chapter 8 The 2 k-p Fractional Factorial Design Text reference, Chapter 8 Motivation for fractional factorials is obvious; as the number of factors becomes large enough

More information

ESTIMATION METHODS FOR MISSING DATA IN UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS

ESTIMATION METHODS FOR MISSING DATA IN UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS Journal of Statistics: Advances in Theory and Applications Volume 5, Number 2, 2011, Pages 131-147 ESTIMATION METHODS FOR MISSING DATA IN k k p UN-REPLICATED 2 FACTORIAL AND 2 FRACTIONAL FACTORIAL DESIGNS

More information

How To: Analyze a Split-Plot Design Using STATGRAPHICS Centurion

How To: Analyze a Split-Plot Design Using STATGRAPHICS Centurion How To: Analyze a SplitPlot Design Using STATGRAPHICS Centurion by Dr. Neil W. Polhemus August 13, 2005 Introduction When performing an experiment involving several factors, it is best to randomize the

More information

6. Fractional Factorial Designs (Ch.8. Two-Level Fractional Factorial Designs)

6. Fractional Factorial Designs (Ch.8. Two-Level Fractional Factorial Designs) 6. Fractional Factorial Designs (Ch.8. Two-Level Fractional Factorial Designs) Hae-Jin Choi School of Mechanical Engineering, Chung-Ang University 1 Introduction to The 2 k-p Fractional Factorial Design

More information

Section 3: Etching. Jaeger Chapter 2 Reader

Section 3: Etching. Jaeger Chapter 2 Reader Section 3: Etching Jaeger Chapter 2 Reader Etch rate Etch Process - Figures of Merit Etch rate uniformity Selectivity Anisotropy d m Bias and anisotropy etching mask h f substrate d f d m substrate d f

More information

TMA4267 Linear Statistical Models V2017 (L19)

TMA4267 Linear Statistical Models V2017 (L19) TMA4267 Linear Statistical Models V2017 (L19) Part 4: Design of Experiments Blocking Fractional factorial designs Mette Langaas Department of Mathematical Sciences, NTNU To be lectured: March 28, 2017

More information

Chapter 9 Other Topics on Factorial and Fractional Factorial Designs

Chapter 9 Other Topics on Factorial and Fractional Factorial Designs Chapter 9 Other Topics on Factorial and Fractional Factorial Designs 許湘伶 Design and Analysis of Experiments (Douglas C. Montgomery) hsuhl (NUK) DAE Chap. 9 1 / 26 The 3 k Factorial Design 3 k factorial

More information

Etching: Basic Terminology

Etching: Basic Terminology Lecture 7 Etching Etching: Basic Terminology Introduction : Etching of thin films and sometimes the silicon substrate are very common process steps. Usually selectivity, and directionality are the first

More information

ST3232: Design and Analysis of Experiments

ST3232: Design and Analysis of Experiments Department of Statistics & Applied Probability 2:00-4:00 pm, Monday, April 8, 2013 Lecture 21: Fractional 2 p factorial designs The general principles A full 2 p factorial experiment might not be efficient

More information

Design of Screening Experiments with Partial Replication

Design of Screening Experiments with Partial Replication Design of Screening Experiments with Partial Replication David J. Edwards Department of Statistical Sciences & Operations Research Virginia Commonwealth University Robert D. Leonard Department of Information

More information

Design and Analysis of Experiments

Design and Analysis of Experiments Design and Analysis of Experiments Part VII: Fractional Factorial Designs Prof. Dr. Anselmo E de Oliveira anselmo.quimica.ufg.br anselmo.disciplinas@gmail.com 2 k : increasing k the number of runs required

More information

Etching. Etching Terminology. Etching Considerations for ICs. Wet Etching. Reactive Ion Etching (plasma etching) Professor N Cheung, U.C.

Etching. Etching Terminology. Etching Considerations for ICs. Wet Etching. Reactive Ion Etching (plasma etching) Professor N Cheung, U.C. Etching Etching Terminology Etching Considerations or ICs Wet Etching Reactie Ion Etching (plasma etching) 1 Etch Process - Figures o Merit Etch rate Etch rate uniormity Selectiity Anisotropy 2 (1) Bias

More information

Software Sleuth Solves Engineering Problems

Software Sleuth Solves Engineering Problems 1 Software Sleuth Solves Engineering Problems Mark J. Anderson and Patrick J. Whitcomb Stat-Ease, Inc. 2021 East Hennepin Ave, Suite 191, Minneapolis, MN 55413 Telephone: 612/378-9449, Fax: 612/378-2152

More information

Assignment 9 Answer Keys

Assignment 9 Answer Keys Assignment 9 Answer Keys Problem 1 (a) First, the respective means for the 8 level combinations are listed in the following table A B C Mean 26.00 + 34.67 + 39.67 + + 49.33 + 42.33 + + 37.67 + + 54.67

More information

An Introduction to Design of Experiments

An Introduction to Design of Experiments An Introduction to Design of Experiments Douglas C. Montgomery Regents Professor of Industrial Engineering and Statistics ASU Foundation Professor of Engineering Arizona State University Bradley Jones

More information

Plasma modelling & reactor design

Plasma modelling & reactor design Plasma modelling & reactor design Alan Howling, Ben Strahm, Christoh Hollenstein Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland Center for Research in Plasma Physics (CRPP) 1) The "lasma

More information

BLOCK DESIGNS WITH FACTORIAL STRUCTURE

BLOCK DESIGNS WITH FACTORIAL STRUCTURE BLOCK DESIGNS WITH ACTORIAL STRUCTURE V.K. Gupta and Rajender Parsad I.A.S.R.I., Library Avenue, New Delhi - 0 0 The purpose of this talk is to expose the participants to the interesting work on block

More information

ETCHING Chapter 10. Mask. Photoresist

ETCHING Chapter 10. Mask. Photoresist ETCHING Chapter 10 Mask Light Deposited Substrate Photoresist Etch mask deposition Photoresist application Exposure Development Etching Resist removal Etching of thin films and sometimes the silicon substrate

More information

Fractional Factorial Designs

Fractional Factorial Designs Fractional Factorial Designs ST 516 Each replicate of a 2 k design requires 2 k runs. E.g. 64 runs for k = 6, or 1024 runs for k = 10. When this is infeasible, we use a fraction of the runs. As a result,

More information

Optimize Your Process-Optimization Efforts

Optimize Your Process-Optimization Efforts 1 Optimize Your Process-Optimization Efforts Highly efficient, statistically based methods can identify the vital few factors that affect process efficiency and product quality. Mark J. Anderson and Patrick

More information

American Society for Quality

American Society for Quality American Society for Quality The 2k-p Fractional Factorial Designs Part I Author(s): G. E. P. Box and J. S. Hunter Source: Technometrics, Vol. 3, No. 3 (Aug., 1961), pp. 311-351 Published by: American

More information

Answer Keys to Homework#10

Answer Keys to Homework#10 Answer Keys to Homework#10 Problem 1 Use either restricted or unrestricted mixed models. Problem 2 (a) First, the respective means for the 8 level combinations are listed in the following table A B C Mean

More information

Technology Improvement and Fault TCP Etch Chamber and a Dual Frequency Oxide Etch Chamber

Technology Improvement and Fault TCP Etch Chamber and a Dual Frequency Oxide Etch Chamber Technology Improvement and Fault Detection @ TCP Etch Chamber and a Dual Frequency Oxide Etch Chamber Russell Benson, Micron Daniel Steckert, Micron Lutz Eichhorn, Plasmetrex Michael Klick, Plasmetrex

More information

Fractional Factorial Designs

Fractional Factorial Designs k-p Fractional Factorial Designs Fractional Factorial Designs If we have 7 factors, a 7 factorial design will require 8 experiments How much information can we obtain from fewer experiments, e.g. 7-4 =

More information

STAT451/551 Homework#11 Due: April 22, 2014

STAT451/551 Homework#11 Due: April 22, 2014 STAT451/551 Homework#11 Due: April 22, 2014 1. Read Chapter 8.3 8.9. 2. 8.4. SAS code is provided. 3. 8.18. 4. 8.24. 5. 8.45. 376 Chapter 8 Two-Level Fractional Factorial Designs more detail. Sequential

More information

DESIGN AND ANALYSIS OF EXPERIMENTS Third Edition

DESIGN AND ANALYSIS OF EXPERIMENTS Third Edition DESIGN AND ANALYSIS OF EXPERIMENTS Third Edition Douglas C. Montgomery ARIZONA STATE UNIVERSITY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore Contents Chapter 1. Introduction 1-1 What

More information

Solution to Final Exam

Solution to Final Exam Stat 660 Solution to Final Exam. (5 points) A large pharmaceutical company is interested in testing the uniformity (a continuous measurement that can be taken by a measurement instrument) of their film-coated

More information

Design of experiment ERT k-p fractional factorial. Miss Hanna Ilyani Zulhaimi

Design of experiment ERT k-p fractional factorial. Miss Hanna Ilyani Zulhaimi + Design of experiment ERT 427 2 k-p fractional factorial Miss Hanna Ilyani Zulhaimi + OUTLINE n Limitation of full factorial design n The concept of fractional factorial, 2 k-p n One-half fraction factorial

More information

The One-Quarter Fraction

The One-Quarter Fraction The One-Quarter Fraction ST 516 Need two generating relations. E.g. a 2 6 2 design, with generating relations I = ABCE and I = BCDF. Product of these is ADEF. Complete defining relation is I = ABCE = BCDF

More information

Reactive Ion Etching (RIE)

Reactive Ion Etching (RIE) Reactive Ion Etching (RIE) RF 13.56 ~ MHz plasma Parallel-Plate Reactor wafers Sputtering Plasma generates (1) Ions (2) Activated neutrals Enhance chemical reaction 1 2 Remote Plasma Reactors Plasma Sources

More information

Stat664 Homework #3 due April 21 Turn in problems marked with

Stat664 Homework #3 due April 21 Turn in problems marked with Stat664 Homework #3 due April 2 Turn in problems marked with Note: Whenever appropriate/possible, inspect the possibilities for need of transformation, determine suitable power (considering only power

More information

DOE WEB SEMINAR,

DOE WEB SEMINAR, DOE WEB SEMINAR, 2013.03.29 Electron energy distribution function of the plasma in the presence of both capacitive field and inductive field : from electron heating to plasma processing control 1 mm PR

More information

4. The 2 k Factorial Designs (Ch.6. Two-Level Factorial Designs)

4. The 2 k Factorial Designs (Ch.6. Two-Level Factorial Designs) 4. The 2 k Factorial Designs (Ch.6. Two-Level Factorial Designs) Hae-Jin Choi School of Mechanical Engineering, Chung-Ang University Introduction to 2 k Factorial Designs Special case of the general factorial

More information

Prediction of Etch Profile Uniformity Using Wavelet and Neural Network

Prediction of Etch Profile Uniformity Using Wavelet and Neural Network 256 Prediction of Etch Profile Uniformity Using Wavelet and Neural Network Won Sun Choi, Myo Taeg Lim, and Byungwhan Kim * Abstract: Conventionally, profile non-uniformity has been characterized by relying

More information

Assessing the Similarity of Bioanalytical Methods

Assessing the Similarity of Bioanalytical Methods Assessing the Similarity of Bioanalytical Methods Jason Liao, Ph.D. Merck Research Laboratories Joint work with Yu Tian 00 MBSW Introduction Outline Existing methods & potential problems New method Linear

More information

Fractional Replications

Fractional Replications Chapter 11 Fractional Replications Consider the set up of complete factorial experiment, say k. If there are four factors, then the total number of plots needed to conduct the experiment is 4 = 1. When

More information

Suppose we needed four batches of formaldehyde, and coulddoonly4runsperbatch. Thisisthena2 4 factorial in 2 2 blocks.

Suppose we needed four batches of formaldehyde, and coulddoonly4runsperbatch. Thisisthena2 4 factorial in 2 2 blocks. 58 2. 2 factorials in 2 blocks Suppose we needed four batches of formaldehyde, and coulddoonly4runsperbatch. Thisisthena2 4 factorial in 2 2 blocks. Some more algebra: If two effects are confounded with

More information

An Experimental Design Approach

An Experimental Design Approach An Experimental Design Approach to Process Design Martha Grover Gallivan School of Chemical & Biomolecular Engineering Georgia Institute of echnology ebruary 11, 2008 actors Influencing Material Properties

More information

Fractional Factorials

Fractional Factorials Fractional Factorials Bruce A Craig Department of Statistics Purdue University STAT 514 Topic 26 1 Fractional Factorials Number of runs required for full factorial grows quickly A 2 7 design requires 128

More information

FRANKLIN-SIMPSON HIGH SCHOOL

FRANKLIN-SIMPSON HIGH SCHOOL FRANKLIN-SIMPSON HIGH SCHOOL Course Name: Physics Unit Name: Speed, Velocity and Acceleration Quality Core Objectives: Unit 1 Speed, Velocity and Acceleration A.1. Scientific Inquiry a. Identify and clarify

More information

2D Hybrid Fluid-Analytical Model of Inductive/Capacitive Plasma Discharges

2D Hybrid Fluid-Analytical Model of Inductive/Capacitive Plasma Discharges 63 rd GEC & 7 th ICRP, 2010 2D Hybrid Fluid-Analytical Model of Inductive/Capacitive Plasma Discharges E. Kawamura, M.A. Lieberman, and D.B. Graves University of California, Berkeley, CA 94720 This work

More information

4. Design of Experiments (DOE) (The 2 k Factorial Designs)

4. Design of Experiments (DOE) (The 2 k Factorial Designs) 4. Design of Experiments (DOE) (The 2 k Factorial Designs) Hae-Jin Choi School of Mechanical Engineering, Chung-Ang University 1 Example: Golfing How to improve my score in Golfing? Practice!!! Other than

More information

E SC 412 Nanotechnology: Materials, Infrastructure, and Safety Wook Jun Nam

E SC 412 Nanotechnology: Materials, Infrastructure, and Safety Wook Jun Nam E SC 412 Nanotechnology: Materials, Infrastructure, and Safety Wook Jun Nam Lecture 10 Outline 1. Wet Etching/Vapor Phase Etching 2. Dry Etching DC/RF Plasma Plasma Reactors Materials/Gases Etching Parameters

More information

Reference: Chapter 6 of Montgomery(8e) Maghsoodloo

Reference: Chapter 6 of Montgomery(8e) Maghsoodloo Reference: Chapter 6 of Montgomery(8e) Maghsoodloo 51 DOE (or DOX) FOR BASE BALANCED FACTORIALS The notation k is used to denote a factorial experiment involving k factors (A, B, C, D,..., K) each at levels.

More information

Etching Issues - Anisotropy. Dry Etching. Dry Etching Overview. Etching Issues - Selectivity

Etching Issues - Anisotropy. Dry Etching. Dry Etching Overview. Etching Issues - Selectivity Etching Issues - Anisotropy Dry Etching Dr. Bruce K. Gale Fundamentals of Micromachining BIOEN 6421 EL EN 5221 and 6221 ME EN 5960 and 6960 Isotropic etchants etch at the same rate in every direction mask

More information

8. Sequences, Series, and Probability 8.1. SEQUENCES AND SERIES

8. Sequences, Series, and Probability 8.1. SEQUENCES AND SERIES 8. Sequences, Series, and Probability 8.1. SEQUENCES AND SERIES What You Should Learn Use sequence notation to write the terms of sequences. Use factorial notation. Use summation notation to write sums.

More information

Regression Analysis. Simple Regression Multivariate Regression Stepwise Regression Replication and Prediction Error EE290H F05

Regression Analysis. Simple Regression Multivariate Regression Stepwise Regression Replication and Prediction Error EE290H F05 Regression Analysis Simple Regression Multivariate Regression Stepwise Regression Replication and Prediction Error 1 Regression Analysis In general, we "fit" a model by minimizing a metric that represents

More information

Data Set 1A: Algal Photosynthesis vs. Salinity and Temperature

Data Set 1A: Algal Photosynthesis vs. Salinity and Temperature Data Set A: Algal Photosynthesis vs. Salinity and Temperature Statistical setting These data are from a controlled experiment in which two quantitative variables were manipulated, to determine their effects

More information

Lecture 9: Factorial Design Montgomery: chapter 5

Lecture 9: Factorial Design Montgomery: chapter 5 Lecture 9: Factorial Design Montgomery: chapter 5 Page 1 Examples Example I. Two factors (A, B) each with two levels (, +) Page 2 Three Data for Example I Ex.I-Data 1 A B + + 27,33 51,51 18,22 39,41 EX.I-Data

More information

Supplementary Information

Supplementary Information Supplementary Information Supplementary Figure 1 Raman spectroscopy of CVD graphene on SiO 2 /Si substrate. Integrated Raman intensity maps of D, G, 2D peaks, scanned across the same graphene area. Scale

More information

Definitive Screening Designs

Definitive Screening Designs Definitive Screening Designs Bradley Jones September 2011 Copyright 2008, SAS Institute Inc. All rights reserved. Joint work with Chris Nachtsheim Outline 1. Motivation 2. Design Structure 3. Design Construction

More information

Blocks are formed by grouping EUs in what way? How are experimental units randomized to treatments?

Blocks are formed by grouping EUs in what way? How are experimental units randomized to treatments? VI. Incomplete Block Designs A. Introduction What is the purpose of block designs? Blocks are formed by grouping EUs in what way? How are experimental units randomized to treatments? 550 What if we have

More information

2.830 Homework #6. April 2, 2009

2.830 Homework #6. April 2, 2009 2.830 Homework #6 Dayán Páez April 2, 2009 1 ANOVA The data for four different lithography processes, along with mean and standard deviations are shown in Table 1. Assume a null hypothesis of equality.

More information

Enhanced Chamber Management and Fault Detection in Plasma Etch Processes via SEERS(Self Excited Electron Resonance Spectroscopy)

Enhanced Chamber Management and Fault Detection in Plasma Etch Processes via SEERS(Self Excited Electron Resonance Spectroscopy) Enhanced Chamber Management and Fault Detection in Plasma Etch Processes via SEERS(Self Excited Electron Resonance Spectroscopy) *Kye Hyun Baek, Gopyo Lee, Yong Woo Lee, Gyung-Jin Min, Changjin Kang, Han-Ku

More information

19. Blocking & confounding

19. Blocking & confounding 146 19. Blocking & confounding Importance of blocking to control nuisance factors - day of week, batch of raw material, etc. Complete Blocks. This is the easy case. Suppose we run a 2 2 factorial experiment,

More information

10.0 REPLICATED FULL FACTORIAL DESIGN

10.0 REPLICATED FULL FACTORIAL DESIGN 10.0 REPLICATED FULL FACTORIAL DESIGN (Updated Spring, 001) Pilot Plant Example ( 3 ), resp - Chemical Yield% Lo(-1) Hi(+1) Temperature 160 o 180 o C Concentration 10% 40% Catalyst A B Test# Temp Conc

More information

Plasma Etch Tool Gap Distance DOE Final Report

Plasma Etch Tool Gap Distance DOE Final Report Plasma Etch Tool Gap Distance DOE Final Report IEE 572 Doug Purvis Mei Lee Gallagher 12/4/00 Page 1 of 10 Protocol Purpose: To establish new Power, Pressure, and Gas Ratio setpoints that are acceptable

More information

Formula for the t-test

Formula for the t-test Formula for the t-test: How the t-test Relates to the Distribution of the Data for the Groups Formula for the t-test: Formula for the Standard Error of the Difference Between the Means Formula for the

More information

EE290H F05. Spanos. Lecture 5: Comparison of Treatments and ANOVA

EE290H F05. Spanos. Lecture 5: Comparison of Treatments and ANOVA 1 Design of Experiments in Semiconductor Manufacturing Comparison of Treatments which recipe works the best? Simple Factorial Experiments to explore impact of few variables Fractional Factorial Experiments

More information

DOE Wizard Screening Designs

DOE Wizard Screening Designs DOE Wizard Screening Designs Revised: 10/10/2017 Summary... 1 Example... 2 Design Creation... 3 Design Properties... 13 Saving the Design File... 16 Analyzing the Results... 17 Statistical Model... 18

More information

Guide to the Expression of Uncertainty in Measurement (GUM)- An Overview

Guide to the Expression of Uncertainty in Measurement (GUM)- An Overview Estimation of Uncertainties in Chemical Measurement Guide to the Expression of Uncertainty in Measurement (GUM)- An Overview Angelique Botha Method of evaluation: Analytical measurement Step 1: Specification

More information

Effect of Noble Gas. Plasma Processing Laboratory University of Houston. Acknowledgements: DoE Plasma Science Center and NSF

Effect of Noble Gas. Plasma Processing Laboratory University of Houston. Acknowledgements: DoE Plasma Science Center and NSF Ion Energy Distributions in Pulsed Plasmas with Synchronous DC Bias: Effect of Noble Gas W. Zhu, H. Shin, V. M. Donnelly and D. J. Economou Plasma Processing Laboratory University of Houston Acknowledgements:

More information

Prentice Hall Stats: Modeling the World 2004 (Bock) Correlated to: National Advanced Placement (AP) Statistics Course Outline (Grades 9-12)

Prentice Hall Stats: Modeling the World 2004 (Bock) Correlated to: National Advanced Placement (AP) Statistics Course Outline (Grades 9-12) National Advanced Placement (AP) Statistics Course Outline (Grades 9-12) Following is an outline of the major topics covered by the AP Statistics Examination. The ordering here is intended to define the

More information

Autoregressive models with distributed lags (ADL)

Autoregressive models with distributed lags (ADL) Autoregressive models with distributed lags (ADL) It often happens than including the lagged dependent variable in the model results in model which is better fitted and needs less parameters. It can be

More information

RESPONSE SURFACE MODELLING, RSM

RESPONSE SURFACE MODELLING, RSM CHEM-E3205 BIOPROCESS OPTIMIZATION AND SIMULATION LECTURE 3 RESPONSE SURFACE MODELLING, RSM Tool for process optimization HISTORY Statistical experimental design pioneering work R.A. Fisher in 1925: Statistical

More information

EFFECT OF PRESSURE AND ELECTRODE SEPARATION ON PLASMA UNIFORMITY IN DUAL FREQUENCY CAPACITIVELY COUPLED PLASMA TOOLS *

EFFECT OF PRESSURE AND ELECTRODE SEPARATION ON PLASMA UNIFORMITY IN DUAL FREQUENCY CAPACITIVELY COUPLED PLASMA TOOLS * EFFECT OF PRESSURE AND ELECTRODE SEPARATION ON PLASMA UNIFORMITY IN DUAL FREQUENCY CAPACITIVELY COUPLED PLASMA TOOLS * Yang Yang a) and Mark J. Kushner b) a) Department of Electrical and Computer Engineering

More information

Do you share more genes with your mother or your father?

Do you share more genes with your mother or your father? Do you share more genes with your mother or your father? By Madeleine Beekman, The Conversation on 12.07.17 Word Count 818 Level MAX Mothers play a big role in our genetic makeup. All mitochondrial DNA

More information

Lecture 6 Plasmas. Chapters 10 &16 Wolf and Tauber. ECE611 / CHE611 Electronic Materials Processing Fall John Labram 1/68

Lecture 6 Plasmas. Chapters 10 &16 Wolf and Tauber. ECE611 / CHE611 Electronic Materials Processing Fall John Labram 1/68 Lecture 6 Plasmas Chapters 10 &16 Wolf and Tauber 1/68 Announcements Homework: Homework will be returned to you on Thursday (12 th October). Solutions will be also posted online on Thursday (12 th October)

More information

Reference: CHAPTER 7 of Montgomery(8e)

Reference: CHAPTER 7 of Montgomery(8e) Reference: CHAPTER 7 of Montgomery(8e) 60 Maghsoodloo BLOCK CONFOUNDING IN 2 k FACTORIALS (k factors each at 2 levels) It is often impossible to run all the 2 k observations in a 2 k factorial design (or

More information

Unit 9: Confounding and Fractional Factorial Designs

Unit 9: Confounding and Fractional Factorial Designs Unit 9: Confounding and Fractional Factorial Designs STA 643: Advanced Experimental Design Derek S. Young 1 Learning Objectives Understand what it means for a treatment to be confounded with blocks Know

More information

Plasma etch control by means of physical plasma parameter measurement with HERCULES Sematech AEC/APC Symposium X

Plasma etch control by means of physical plasma parameter measurement with HERCULES Sematech AEC/APC Symposium X Plasma etch control by means of physical plasma parameter measurement with HERCULES A. Steinbach F. Bell D. Knobloch S. Wurm Ch. Koelbl D. Köhler -1- Contents - Introduction - Motivation - Plasma monitoring

More information

Testing methodology. It often the case that we try to determine the form of the model on the basis of data

Testing methodology. It often the case that we try to determine the form of the model on the basis of data Testing methodology It often the case that we try to determine the form of the model on the basis of data The simplest case: we try to determine the set of explanatory variables in the model Testing for

More information

RESPONSE SURFACE METHODOLOGY

RESPONSE SURFACE METHODOLOGY RESPONSE SURFACE METHODOLOGY WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding, Noel A. C. Cressie, Garrett M. Fitzmaurice, Iain

More information

Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Edition

Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Edition Brochure More information from http://www.researchandmarkets.com/reports/705963/ Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Edition Description: Identifying

More information

Vladimir Spokoiny Design of Experiments in Science and Industry

Vladimir Spokoiny Design of Experiments in Science and Industry W eierstraß-institut für Angew andte Analysis und Stochastik Vladimir Spokoiny Design of Experiments in Science and Industry www.ndt.net/index.php?id=8310 Mohrenstr. 39, 10117 Berlin spokoiny@wias-berlin.de

More information

Supplementary Information. Atomic Layer Deposition of Platinum Catalysts on Nanowire Surfaces for Photoelectrochemical Water Reduction

Supplementary Information. Atomic Layer Deposition of Platinum Catalysts on Nanowire Surfaces for Photoelectrochemical Water Reduction Supplementary Information Atomic Layer Deposition of Platinum Catalysts on Nanowire Surfaces for Photoelectrochemical Water Reduction Neil P. Dasgupta 1 ǂ, Chong Liu 1,2 ǂ, Sean Andrews 1,2, Fritz B. Prinz

More information

Sizing Mixture (RSM) Designs for Adequate Precision via Fraction of Design Space (FDS)

Sizing Mixture (RSM) Designs for Adequate Precision via Fraction of Design Space (FDS) for Adequate Precision via Fraction of Design Space (FDS) Pat Whitcomb Stat-Ease, Inc. 61.746.036 036 fax 61.746.056 pat@statease.com Gary W. Oehlert School of Statistics University it of Minnesota 61.65.1557

More information

Improving the Efficiency and Efficacy of Controlled Sequential Bifurcation for Simulation Factor Screening

Improving the Efficiency and Efficacy of Controlled Sequential Bifurcation for Simulation Factor Screening Improving the Efficiency and Efficacy of Controlled Sequential Bifurcation for Simulation Factor Screening Hong Wan School of Industrial Engineering, Purdue University, West Lafayette, IN, 47907, USA,

More information

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008 MIT OpenCourseWare http://ocw.mit.edu 2.830J / 6.780J / ESD.63J Control of Processes (SMA 6303) Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

ARGON RF PLASMA TREATMENT OF PET FILMS FOR SILICON FILMS ADHESION IMPROVEMENT

ARGON RF PLASMA TREATMENT OF PET FILMS FOR SILICON FILMS ADHESION IMPROVEMENT Journal of Optoelectronics and Advanced Materials Vol. 7, No. 5, October 2005, p. 2529-2534 ARGON RF PLASMA TREATMENT OF FILMS FOR SILICON FILMS ADHESION IMPROVEMENT I. A. Rusu *, G. Popa, S. O. Saied

More information

Modeling of Process Plasma Using a Radial Basis Function Network: A Case Study

Modeling of Process Plasma Using a Radial Basis Function Network: A Case Study 68 ICASE: The Institute of Control, Automation and Systems Engineers, KOREA Vol., No. 4, December, 000 Modeling of Process Plasma Using a Radial Basis Function Network: A Case Study Byungwhan Kim and Sungjin

More information

CHAPTER 2 AN OVERVIEW OF TCAD SIMULATOR AND SIMULATION METHODOLOGY

CHAPTER 2 AN OVERVIEW OF TCAD SIMULATOR AND SIMULATION METHODOLOGY 15 CHAPTER 2 AN OVERVIEW OF TCAD SIMULATOR AND SIMULATION METHODOLOGY In this chapter TCAD and the various modules available in the TCAD simulator have been discussed. The simulation methodologies to extract

More information

EE C245 ME C218 Introduction to MEMS Design

EE C245 ME C218 Introduction to MEMS Design EE C45 ME C18 Introduction to MEMS Design Fall 008 Prof. Clark T.-C. Nguyen Dept. of Electrical Engineering & Computer Sciences University of California at Berkeley Berkeley, CA 9470 Lecture 6: Output

More information

Practice 2SLS with Artificial Data Part 1

Practice 2SLS with Artificial Data Part 1 Practice 2SLS with Artificial Data Part 1 Yona Rubinstein July 2016 Yona Rubinstein (LSE) Practice 2SLS with Artificial Data Part 1 07/16 1 / 16 Practice with Artificial Data In this note we use artificial

More information

Confounding and Fractional Replication in Factorial Design

Confounding and Fractional Replication in Factorial Design ISSN -580 (Paper) ISSN 5-05 (Online) Vol.6, No.3, 016 onfounding and Fractional Replication in Factorial esign Layla. hmed epartment of Mathematics, ollege of Education, University of Garmian, Kurdistan

More information

University of California, Santa Barbara Santa Barbara, California 93106

University of California, Santa Barbara Santa Barbara, California 93106 HIGH-ASPECT-RATIO INDUCTIVELY COUPLED PLASMA ETCHING OF BULK TITANIUM FOR MEMS APPLICATIONS E. R. Parker 1, M. F. Aimi 2, B. J. Thibeault 3, M. P. Rao 1, and N. C. MacDonald 1,2 1 Mechanical and Environmental

More information