RECIPE GENERATION OF UNDER FILL PROCESS BASED ON
IMPROVED KERNEL REGRESSION AND PARTICLE SWARM
OPTIMIZATION
MOHD HAFIZ BIN OTHMAN
A project report submitted partial fulfilment of the
requirements for the award of the degree of
Master of Engineering (Electrical – Mechatronics and Automatic Control)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
JANUARY 2012
III
To my beloved wife, daughter, parents and friends.
IV
ACKNOWLEDGEMENT
By the name of Allah Most Merciful and Gracious,
Alhamdulillah, thanks to Allah S.W.T. for the guidance and knowledge
bestowed upon me, without it I would not have been able to come this far.
I wish to express my sincere appreciation to my project supervisor, Doctor
Zuwairie bin Ibrahim, for the encouragement, motivation, guidance, critics and
friendship. I am also very thankful to Mr. Mohd Ibrahim Shapiai and Amar Faiz
Zainal Abidin, for their guidance, advices and help given throughout the project.
I would also like to thank my fellow postgraduate students for their
friendship, support and time spent together.
My special thanks also go to my wife, Noor Hafizah bt Mohd Yasim, for her
love, support and motivation given in completing this study. Last but not least, a
special thanks to my parents that always encourage and praying for my success.
V
ABSTRACT
The under fill process is a process that fills the gap between a chipset and a
substrate using an epoxy material. The output of this process is a length of tongue
that has to be controlled so it avoid touching the keep out zone. A recipe generation
of the input parameters in the under fill process will help the length of tongue
generated from touching the keep out zone. This project proposes a predictive
modeling algorithm called Improved Kernel Regression and Particle Swarm
Optimization in order to find the six input parameters needed in the under fill
process. Even though only few samples of the under fill data sets are used in the
simulation experiment, the proposed approach is able to provide a recipe generation
of the six input parameters.
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ABSTRAK
Proses under fill adalah proses memenuhi ruang di antara peranti elektronik
dan substrat dengan menggunakan bahan epoxy. Keluaran daripada proses ini adalah
saiz tongue yang perlu dikawal supaya ianya tidak menyentuh zon larangan.
Generasi resipi parameter masukan di dalam proses under fill dapat mengawal
penghasilan saiz tongue daripada menyentuh zon larangan. Projek ini mencadangkan
suatu model algoritma ramalan yang diberi nama Improved Kernel Regression dan
Particle Swarm Optimization yang dapat mencari enam parameter masukan yang
diperlukan di dalam proses under fill. Walaupun hanya beberapa sampel data under
fill digunakan semasa ujikaji simulasi, pendekatan yang dicadangkan dapat
menyediakan generasi resipi enam parameter masukan.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION II
DEDICATION III
ACKNOWLEDGEMENT IV
ABSTRACT V
ABSTRAK VI
TABLE OF CONTENTS VII
LIST OF TABLES IX
LIST OF FIGURES X
LIST OF ABBREVIATIONS XI
LIST OF SYMBOLS XII
1 INTRODUCTION 1
1.0 Background 1
1.1 Problem Statement 3
1.2 Project Objective 4
2 LITERATURE REVIEW 5
2.0 Introduction 5
2.1 Historical Data Sets of Under Fill Process 6
3 METHODOLOGY 8
3.0 Introduction 8
3.1 Particle Swarm Optimization 8
VIII
3.1.1 PSO Parameters Initialization 10
3.2 Improved Kernel Regression 12
3.3 Modified Algorithm 13
4 SIMULATION RESULTS 15
4.0 Simulation Result 1 15
4.1 Simulation Result 2 17
4.2 Simulation Result 3 18
4.3 Simulation Result 4 19
4.4 Simulation Result 5 20
4.5 Simulation Result 6 21
4.6 Simulation Result 7 22
4.7 Simulation Result 8 23
4.8 Simulation Result 9 24
4.9 Simulation Result 10 25
4.10 Discussion 26
5 CONCLUSION 27
5.0 Conclusion 27
5.1 Future Work 27
REFERENCES 29
IX
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 The historical data sets of under fill process 6
4.1 Result with tongue length 256305.3 16
4.2 Result with tongue length 199574.8 17
4.3 Result with tongue length 246692 18
4.4 Result with tongue length 166709.3 19
4.5 Result with tongue length 250800.1 20
4.6 Result with tongue length 237581.9 21
4.7 Result with tongue length 215971.4 22
4.8 Result with tongue length 114980.7 23
4.9 Result with tongue length 243672.4 24
4.10 Result with tongue length 251815.5 25
X
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 Illustration of an under fill process in an assembly process 2
1.2 Tongue length produce with excessive amount of epoxy 3
1.3 Epoxy tongue touch the keep out zone 3
2.1 Picture of dispenser equipment 7
3.1 Basic PSO algorithm 10
3.2 Modified algorithm incorporating PSO and improved kernel
regression 13
4.1 Convergence plot for error 0.002 16
4.2 Convergence plot for error 0.026 17
4.3 Convergence plot for error 0.007 18
4.4 Convergence plot for error 0.002 19
4.5 Convergence plot for error 0.02 20
4.6 Convergence plot for error 0.002 21
4.7 Convergence plot for error 0.016 22
4.8 Convergence plot for error zero 23
4.9 Convergence plot for error 0.002 24
4.10 Convergence plot for error 0.001 25
XI
LIST OF ABBREVIATIONS
IBM - International Business Machine
C4 - Controlled Collapse Chip Connection
LCD - Liquid Crystal Display
MATLAB - Matrix Laboratory
PSO - Particle Swarm Optimization
XII
LIST OF SYMBOLS
- inertia weight
- cognitive coefficient
- social coefficient
- random parameter
- random parameter
CHAPTER 1
INTRODUCTION
1.0 Background
In the field of semiconductor industry, the fabrication process development of
a chipset is one of the major processes that must be taken vitally. The chipset or also
known as flip chip packaging technology was firstly introduced by IBM in the late
1960’s and the flip chip is known as Controlled Collapse Chip Connection (C4) [1].
Flip chip technology has been utilized more than 40 years ago and today flip chips
are widely used for mobile phones, disk drives, LCD displays, watches, and lots
more.
The flip chip fabrication process involves several sequential steps: wafer
bumping, attaching the bump die to the board or substrate and then completing the
assembly with adhesive under fill [1]. This project will cover on the under fill
process which is the last step from the fabrication process that is done in INTEL
Malaysia.
2
The under fill is a step where an epoxy material being filled between a
chipset and a substrate in an assembly process. Figure 1.1 shows the illustration of
an under fill process in an assembly process.
Figure 1.1: Illustration of an under fill process in an assembly process
As a result from this process, a tongue length is being produced due to the
dispensed of epoxy between the chipset and the substrate. The tongue length is
presented as the output of the process and it is actually being affected by six input
parameters that need to be tuned. These six input parameters are consists of: chipset
size (dimension of chipset), gap height, the number of bumps, the dispense distance
and dispense weight (amount of epoxy) [2]. These six input parameters will be
represented as the recipes needed or the generated recipes required in order
producing the tongue length needed.
However, wrongly determined the six input parameters frequently can cause
the epoxy to be excessive, the epoxy is on the chipset or the epoxy may insufficient.
Figure 1.2 shows an example of a finishing of under fill process that produces an
excessive length of tongue. The length of tongue generated must be taken care so
that it avoids from touching the keep out zone. Figure 1.3 shows a result when the
tongue touches the keep out zone. Thus, it is important for the engineers to have a
recipe generation that gives key references for them to overcome the length of tongue
from touching the keep out zone. In practice, the chosen ingredient of the recipes
mainly depends on the engineer’s knowledge and the experiment usually involves
large samples and it involves high cost in setting up the experiments. This can often
3
result in insufficient data samples and by using the historical chipset data from an
under fill process that consists of only a few samples, a simple prediction technique
will be implemented in selecting the six input parameters.
Figure 1.2: Tongue length produce with excessive amount of epoxy
Figure 1.3: Epoxy tongue touch the keep out zone
1.1 Problem Statement
The experimental setup of the chipset is rather expensive in determining the
best recipes that can avoid the length of tongue generated during the under fill
process from touching the keep out zone. Thus, it is needed for the experiment to be
cost effective. By using the historical data sets of under fill process from INTEL
Malaysia, this project proposes an approach based on particle swarm optimization
4
and improved kernel regression for finding the best input parameters needed so that
the length of tongue is in the area from keep out zone.
1.2 Project Objective
The objective of this project is to predict the recipes, which are the six input
parameters needed in the under fill process using particle swarm optimization and
improved kernel regression.
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REFERENCES
[1] M. K. Md Arshad, U. Hashim and Muzamir Isa, “Under Bump Metallurgy
(UBM) – A Technology Review for Flip Chip Packaging,” International
Journal of Mechanicals and Materials Engineering (IJMME), Vol. 2, 2001,
No.1, 48 – 54.
[2] M. I. Shapiai, et al., “Recipe Generation from Small Samples: Incorporating
Weighted Kernel Regression with Artificial Samples,” 2011.
[3] C.L. Song, T.S. Yeoh, T.J. Yeoh, “Case Study Application of TRIZ Tools –
Strengths & Weaknesses,” Intel Technology Sdn. Bhd, Penang, Malaysia.
[4] A. P. Engelbrecht, “Fundamentals of Computational Swarm Intelligence,”
Wiley, 2005.
