
MODULE PROFILE
Module Number: 5
Title: Optimisation of Processes
Delivered by: University of Edinburgh
Module Credits: 15
Assessment Weighting:
 Preresidential work: 3
 Postresidential work: 7
 Examination: 10
Convenor:
 Professor Anthony Walton, University
of Edinburgh
Lecturers:
Internal:
 Professor Anthony Walton
 Dr Gerard Allan
Tutors:
Industrial:
 Dr Christopher Hess, PDF Solutions
 Dr Andrew Skumanich, Applied Materials
 Dr Ian Cox, SAS
 Dr Tim Crandle, Stone Pillar
 Dr Martin Fallon, National Semiconductor
 Dr Malcolm Moore,Consultant Engineer
Industrial Advisors:
 Dr Martin Fallon, National Semiconductor,
Greenock
 Dr Ian Cox, SAS Institute, Bucks
Aims:
The aim of this module is to present
the theoretical and practical knowledge that is required to facilitate
the optimisation of IC processing parameters. This Includes methodologies
for problem solving, development of robust production processes and
approaches for the Design of Experiments (DoE). An insight into methods
such as data integration, yield management and data mining to help identify
and realise improvement opportunities for existing manufacturing processes
will also be given.
Learning Objectives:
Upon successful completion of this modules
Delegates will have:
 a sound understanding of the theoretical
techniques for process optimisation
 experience of the methodologies
of problem solving
 gained an awareness of the capabilities
of commercial software for optimisation
 experience of the effect of six sigma
on DFM
 understand screening and RSM experiments
 familiarity with IC yield and optimisation
strategies
 an appreciation of techniques to
help identify and realise improvement opportunities in manufacturing
Assessment:
 Preresidential sessions: assignments
15 %
 Postresidential sessions:
assignments 35 %
 Examination (supervised) 50
%
Background to the Module:
The production of stateoftheart integrated
circuits is a very complex process with many hundreds of process steps.
Hence it is not practical to fully optimise processes using ad hoc procedures;
a more structured approach is required. This module is designed to equip
engineers with the knowledge required for the optimisation of processes
and covers theoretical and practical topics that are generally not found
in first degree engineering courses.
PreRequisite Knowledge:
Participants are expected to have mathematics
at Engineering/Science level and an understanding of IC process and
device operation.
Delivery & Assignments:
Preresidential sessions:
A reading list will sent to delegates
before the start of the module. Some material will also be made available
on restricted access pages on the World Wide Web and participants will
be required to undertake a review / exercises related to the course.
Residential week (35 hours contact
time):
Laboratory sessions are a major component
of this course. These will be performed using the extensive computer
network within the School of Engineering and Electronics. Practical
work will be complemented with formal lectures and tutorials including
case studies.
Post residential sessions:
Assignments will be given after the
conclusion of the residential period, which will make extensive use
of the knowledge gained and test the understanding of the Delegates.
A written report of 20004000 words and advanced tutorial exercises
will be set.
Supervised examination
Candidates will answer 3 questions out
of 5 during a 2 hour examination
SYLLABUS
 Lectures 16 hours
 Laboratory & Interactive Sessions
19 hours
Time 
Topic 
Content 
Day 1 
Robust
processes and strategies for achieving high quality processes:

Introduction
to and comparison of the key strategies for identifying opportunities
for process improvement and realizing them; key concepts and methods:
Cp, Cpk, six sigma, distributions, ztransformations, benchmarking,
design margins, dpu, cycle time reduction, and DFM, Seven Tools
of Quality Improvement. Two group exercises are used to illustrate
the importance of using DFM and achieving six sigma quality.
Lectures: 3 hours
Interactive exercises: 3½ hours

Day 2 
Process
debugging and improvement  Statistical tools of quality improvement
and problem solving: 
Graphical tools for exploring relationships;
statistical comparisons  ttests, ANOVA, the interpretation of
significance levels and confidence intervals; regression analysis
 concept of least squares and assessing the goodness of fit through
graphical analysis of residuals, R2and adjustedR2, and analysis
of variance; qualifying and improving measurement capability through
gauge R&R analysis.
Lectures: 3 hours
Interactive exercises: 3 hours
Guest Lecture / Case study (1 hour): Data Integration Requirements
for Problem Solving

Day 3 
Identifying
the critical variables with DOE: 
Traditional
experimental approaches vs statistically deigned experiments; Taguchi
and how it differs from traditional experimentation; essential prerequisites
to successful experimentation: identification of variables and their
experimental range, reliability with which responses are measured,
randomisation and blocking; design and analysis of screening experiments:
Full Factorial, Fractional Factorial, Plackett Burman and Orthogonal
Arrays.
Lectures: 3 1/2 hours
Laboratory Sessions: 3 1/2 hours
Guest Lecture / Case study (1 hour): Realising Data Integration 
Day 4 
Optimising
processes with DOE: 
Optimising
the mean performance using response surface designs: Design and Analysis
of Full Factorial, Central Composite, BoxBehnken, DOptimal Designs,
optimizing multiple responses.
Lectures: 3 hours
Laboratory Sessions: 3 hours
Guest Lecture / Case study (1 hour): Yield Management 
Day 5 
Optimising
processes with DOE: 
Dealing
with split plots and data aggregation (nesting) random and fixed effects
and their impact upon gauge R&R; Optimising for mean and variance:
inner and outer array designs and alternative approaches; design Tolerancing:
TCAD and other approaches.
Lectures: 3 hours
Laboratory Sessions: 3 hours:
Guest Lecture / Case study (1 hour): Data Mining 
Recommended Texts
Background/General Texts:

"Design and Analysis of Experiments",
D C Montgomery, John Wiley & Sons, 2000, ISBN: 0471316490

"World Class Quality: Using Design
of Experiments to Make It Happen", K R Bhote, AMACOM, 1999, ISBN:
0814404278

"Quality Engineering in Producton
Systems", G Taguchi, E A Elsayed, Thomas Hsiang, McGrawHill, 1989,
ISBN: 0070628300

"Design
and Analysis of Circuits", DC Montgomery, John Wiley &
Sons, ISBN 0471520004
General Statistics & Data Analysis

"Graphical
Methods for Data Analysis", J M Chambers, W S Cleveland, B
Kleiner, P A Tukey, CRC Press, 1983, ISBN: 0412052717

"Applied
Regression Analysis", 2nd Edition, N R Draper, H Smith, John
Wiley & Sons, 1981
 "Applied
Linear Regression Models", 3rd Edition, J Neter, W Wasserman,
M H Kutner, C J Nachtsheim, McGrawHill, 1996, ISBN: 025608601X
Experimental
Design

"Statistics
for Experimenters: An Introduction to Design, Data Analysis &
Model Building", G
E P Box, W G Hunter, J S Hunter, John Wiley & Sons, 1978, ISBN:
0471093157

"Empirical
ModelBuilding and Response Surfaces" G E P Box, N R Draper,
John Wiley & Sons, 1990, ISBN: 0471810339

"Evolutionary
Operation: A Statistical Method for Process Improvement", G
E P Box, N R Draper, John Wiley & Sons, 1998, ISBN: 0471255513

"Engineering
Quality & Experimental Design", D M Grove, T P Davis, Longman
Publishing Group, 1992, ISBN: 0582066875
 "Understanding
Industrial Designed Experiments", 3rd Edition, S R Schmidt, R
G Launsby, Air Academy Press, 1992, ISBN: 096221762X
Variance
Components

"Analysis
of Messy Data, Volume 1: Designed Experiments", G A Milliken,
D E Johnson, CRC Press, 1992, ISBN: 0412990814
 "Design
and Analysis of Experiments", 2nd Edition, D C Montgomery, John
Wiley & Sons, 2000, ISBN: 0471316490
Data
Mining
 "The
Elements of Statistical Learning: Data Mining, Inference, and Prediction",
Trevor Hastie, Robert Tibshirani, SpringerVerlag, 2001, ISBN: 0387952845
Applications
and Related Journal Articles

"Preludes
to a Screening Experiment: A Tutorial", R Carlson (1992). Chemometrics
and Intelligent Laboratory Systems, 14, pp103114

"Some
Things Engineers Should Know About Experimental Design",
G J
Hahn
(1977). Journal of Quality Technology, 9, pp 1320

"A
Systematic Approach to Planning for a Designed Industrial Experiment"
D E Coleman, D C Montgomery (1993)  (with discussions by B H

Gunter
G J Hahn, P D Haaland, M A O'Connell, R V Leon, A C Shoemaker, Technometrics,
35, 1, pp. 127

"Gauge
Capability and Designed Experiments I: Basic Methods", D C
Montgomery, G C Runger (199394), Quality Engineering, 6 (1), pp.
115  135

"Gauge
Capability and Designed Experiments II: Experimental Designs Models
& Variance Component Estimation", D C Montgomery, G C Runger
(199394), Quality Engineering, 6 (2), pp. 289  305

"ComputerAided
Design of Experiments  Some Practical Experiences",
R D
Snee, (1985), Journal of Quality Technology, 17, 4, pp 222236
 "Experiments
in Industry: Design, Analysis, and Interpretation of Results",
R D Snee, L B Hare, J R Trout (1985), ASQC Quality Press, Milwaukee
TIMETABLE
NB: Details of module content, timetable
and lecturers may be subject to change.

