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iii
NICKEL PHOSPHORUS COATED SUBSTRATE MICRO-DEFECT
REDUCTION THROUGH CHEMICAL MECHANICAL PLANARIZATION
PROCESS OPTIMIZATION
NAZIM MOHD. SALEH
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Engineering (Industrial Engineering)
Faculty of Mechanical Engineering
Universiti Teknologi Malaysia
JUNE 2014
v
Kepada Ayahbonda (Hj Mohd. Saleh bin Urip dan Hjh Tuminah bte Hj. Nor), terima
kasih atas segala doa, dorongan dan nasihat yang tak pernah putus.
Kepada Isteri tersayang Zainon Tugiran, terima kasih atas segala pengorbanan dan
kasih sayang yang dicurahkan dalam mengemudi bahtera kehidupan sebagai
persediaan kita ke kehidupan yang kekal abadi.
Kepada anak-anak ayah, Muhammad Fathi Hakimi, Nur Farhana Hani dan
Muhammad Fatih Irfan semoga menjadi anak-anak yang soleh dan solehah sebagai
asset berharga penyumbang bakti bagi ummah ini..
vi
ACKNOWLEDGEMENT
In preparing this project report, I was liaised with many people around me.
They contributed a lot in order to fulfill this project requirement from the beginning
of understanding problems until implementation of results. I would like to express
my sincere appreciation to my Supervisor, Dr. Jafri bin Mohd. Rohani for his
guidance and advice. I’m also very thankful to my colleagues for their support and
co-operation in order to run experiments and tests for this project. I’m very grateful
for all my family members.ur text begin here.
.
.
vii
ABSTRACT
Chemical mechanical planarization (CMP) was known as a process to gain
superfine surface finish with high precision mechanical parameters. It widely used
within semiconductors wafer manufacturing and hard disk (HDD) industries.
There’re very few research done in HDD compared with silicon wafer. Micro defect
formation caused by first step polishing in multistep polishing reducing the “good
sector” and areal density in HDD substrate platter. In this study, micro defect
reduction became the main objectives and as main response with others parameters
including material removal rate and micro waviness. A 2 level 4 factor fractional
factorial with center points Resolution IV, Design of Experiment (DOE) with total of
12 experimental runs was chosen for main effect and interaction screening purposes.
All key factors such as platen rotation speed (rpm), applied pressure (g/cm2), slurry
distribution points and slurry flow rate (ml/min) were then being optimized using
Response Surface Method (RSM) setup of a set of full factorial 4 factors with center
point, single replicate, 8 axial point and 2 alpha significance value with total 31
random runs. Results shows that all the input factors were affecting MRR and MD
count except micro waviness which not respond to all input factors applied.
Optimum input parameter setup were then established with setup of applied pressure
(103.1 g/cm2), slurry distribution points (4/5 points), slurry flow rate (381.6 ml/min)
and platen rotation speed (15.7 rpm). Predicted composite desirability gained to be
0.998, which can be satisfactory accepted. Verification runs from the parameter
setup shows and concluded that the desired MD and MRR successfully achieved
with 1.688% variation.
viii
ABSTRAK
Pensatahan Kimia dan Mekanik merupakan satu proses untuk mendapatkan
permukaan super-licin dan berketepatan ukuran mekanikal yang tinggi. Ia digunakan
dengan sangat meluas dikalangan industry semi-konduktor dan pembuat cakera keras
komputer. Bagaimanapun, terdapat kajian yang sangat sedikit dilakukan untuk dalam
bidang cakera keras berbanding semi-konduktor. Kecacatan mikro yang disebabkan
oleh penggilapan pertama didalam penggilapan berbilang tingkat telah
mengurangkan “sektor baik” dan kepadatan areal didalam piring perakam cakera
keras. Dalam kajian ini, pengurangan kecacatan mikro menjadi objektif utama dan
tindak balas output utama bersama parameter yang lainnya termasuk kadar
pembuangan bahan dan keberombakan mikro. Satu set 2 level 4 faktor faktorial
pecahan dengan titik tengah Resolusi IV, Design of Experiment (DOE) dengan
jumlah 12 eksperimen telah dipilih untuk menentukan faktor utama dan faktor
interaksi dalam proses penyaringan. Semua faktor-faktor input utama seperti
kelajuan putaran plat (rpm), tekanan permukaan (g/cm2), mata pengedaran buburan
dan kadar aliran buburan (ml/min) kemudiannya dioptimumkan menggunakan
Response Kaedah Permukaan (RSM) dengan setting 31 eksperimen, eksperimen
penuh melalui 4 faktor dengan titik tengah, 8 titik paksi dan nilai signifikan 2 alfa.
Keputusan menunjukkan bahawa semua faktor-faktor input memberikan kesan
terhadap MRR dan jumlah MD kecuali keberombakan mikro yang tidak bertindak
balas kepada semua faktor-faktor input digunakan. Parameter input yang telah
dioptimumkan kemudiannya telah dapat dibina dengan setup of tekanan permukaan
(103.1 g/cm2), mata pengedaran buburan (4/5 poin), kadar aliran buburan (381.6
ml/min) and kelajuan putaran plat (15.7 rpm). Jangkaan komposit yang dikehendaki
adalah 0.998. Parameter yang telah dioptimumkan itu kemudian telah ditentusahkan
dan satu kesimpulan dapat dibuat bahawa objektif MD dan MRR yang dikehendaki
berjaya dicapai dengan ralat 1.688%.
ix
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION iv
DEDICATION v
ACKNOWLEDGEMENTS vi
ABSTRACT vii
ABSTRAK viii
TABLE OF CONTENTS ix
LIST OF TABLES xii
LIST OF FIGURES xiii
1 INTRODUCTION 1
1.1 Background of Study
1.2 Problem Statement
1.3 Objective of Study
1.4 Scope of Study
1.5 Study Question
1.6 Significance of Study
1
1
2
3
4
4
2
LITERATURE REVIEW
2.1 Introduction
2.2 History of CMP
2.2.1. History of HDD
2.3. CMP Equipment
2.4 How CMP works
2.5 Polishing parameters
5
5
6
7
8
10
11
x
2.5.1 Pressure
2.5.2 Speed
2.5.3 Slurry Flow Rate
11
13
13
3
2.6 CMP Performance Measures
2.6.1 MRR
2.6.2 Micro-waviness
2.6.3 Micro Defect
2.7 Polyurethane pad
2.7.1 Pad Microstructure
2.7.2 Types of Pad Design
2.8 Slurry
2.9 Previous Research Findings
2.10 Research Gap Analysis
METHODOLOGY
3.1. Introduction
3.2. Theory of Experimental Design
3.2.1. Machining Parameters
3.2.1.1 Selection of Machining
Parameters
3.2.1.2 Constant Parameter &
Condition
3.2.2. Two Level Factorial Design
3.2.3. Response Variable
3.2.3.1 MRR
3.2.3.2. Micro Waviness
3.2.3.3 Micro Defect
3.3. Experiment Setup & Procedure
3.4. Research Instrument
3.4.1. Machining Equipment
3.4.2. Measuring Equipment
3.4.2.1 X-Strata XRF Machine
14
14
14
15
19
19
20
20
22
28
29
29
30
31
31
33
33
35
35
35
35
36
37
38
38
38
xi
3.4.2.2 OSA (KLA Tencor) Candela
38
4
5
ANALYSIS AND DISCUSSION
4.1. Introduction
4.2. DOE Design Experimental Result
4.2.1. MRR
4.2.2. MD
4.2.3. Micro waviness
4.3. Optimization result (RSM analysis)
4.3.1. MRR
4.3.2. MD
4.4. Optimal parameter setup
CONCLUSION AND FUTURE STUDY
40
40
41
41
45
48
52
52
56
59
62
REFERENCES 64
Appendices A-E 69-72
xii
LIST OF TABLES
TABLE NO. TITLE PAGE
1 Summary of previous research. 23
2 List of parameters selected in the experiment. 31
3 Fixed polishing parameter 33
4 Fractional factorial Res IV with center points 34
5 DOE Experiment results 40
6 Full factorial RSM CCD and results 52
7 Verification run results 60
xiii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.0 Microdefect AFM analysis fallout 2
2.0 Areal density demand over years of production 6
2.1
Head flying height decrease trend (Tomonobu
Ogasawara, Kenji Ozawa, 2008)
7
2.2
Illustration of double sided polishing (Toshi Kasai,
2008)
8
2.3
Schematic of Substrate CMP (S.Sideq, S.Izman,
2013)
9
2.4 Three body abrasion on CMP process 9
2.5
Wear rates of ferrite and copper as a function of
nominal contact pressure when using 0.5 p.m
AI2O3 particles and soft polishing pads.
(Yongsong Xie, 1996)
11
2.6
Trajectory analysis to simulate a single point on a
carrier travel during polishing (Toshi Kasai, 2008)
12
2.7
MRR vs Table rotation speed (Chu, Bai & Chen ,
2011)
12
2.8 MRR vs slurry flowrate (Chu, Bai & Chen , 2011) 13
2.9
Primary profile (top), waviness (middle),
roughness (bottom)
14
2.10
Correlation of substrate Waviness and take-off
height
15
2.11
Defects formation by impacted surface of slurry
particle
16
2.12 AFM Image of embedded particle 17
2.13 AFM image of Irregular pit defect 18
xiv
2.14 AFM image of Worm pit defect 18
2.15
Polishing pad microstructure with existence of
pores
19
2.16 Several pad groove design 20
2.17 Irregular shape alumina in slurry 21
2.18 Aluminum coated with silica 21
2.19 SiO2 Silica based slurry 22
3.0 Design of experimental flow 30
3.1
Initial setup of slurry distribution discharge point
system
32
3.2
Experiment setup of the slurry distribution
discharge point system
32
3.3 Overall experiment flow setup 37
3.4 Taiyo 11.8B CMP Polishing machine 37
3.5 XRF machine 38
3.6 KLA Tencor (Candela) 39
4.0 MRR DOE normal plot 41
4.1 Pareto chart of MRR 43
4.2 Main effect plot of MRR 46
4.3 Interaction plot of MRR 44
4.4 Normal plot for MD count 45
4.5 Pareto chart for MD count 47
4.6 Main effect plot for MD count 47
4.7 Interaction plot fr MD count 48
4.8 Pareto chart for micro waviness 48
4.9 Normal plot for micro waviness 49
4.10 Contour plot of MRR 55
xv
4.11 Surface plot of MRR 55
4.12 Contour plot of MD 58
4.13 Surface plot of MD 58
4.14 Optimization plot model 60
4.15
Excel spreadsheet thoretical calculation for
improved setting 61
1
CHAPTER 1
INTRODUCTION
1.1 Background of study
In Hard disk industries, the recording media which called substrates contains
Al-Mg disk as based and plated with thin layer of nickel phosphorus to improve
smoothness and hardness. With extreme demand of higher memory space in hard
disk, manufacturers are pushing to the higher density of substrate platter. It can be
achieved by reducing the height of the recording head to fly to strengthen the weak
and smaller signals (Lei and Luo, 2004, Lei et.al, 2009). Reduction of flying head
media needs super-smooth recording media platter to prevent head crash resulting
data loss
1.2 Problem Statement
Most of the studies in reducing Chemical Mechanical Planarization (CMP)
defects focusing on the slurry parameter such as abrasive particle size (Lei and Luo,
2004, Xie and Bushan, 1996), abrasive particle type and shape (Hong Lei et.al, 2009,
2010), oxidizing agent contents, additives to improve material removal rate and
surface quality. Recent study starts to focus on friction modifier additives in slurry
for improving surface topography and reducing surface defects (S.Sideq, S.Izman,
2013). Yong-Jin Seo, Sang-Yong Kim , Woo-Sun Lee (Yong-Jin Seo et.al, 2003)
studied about the advantage of using POU slurry filters for reducing sub-micron
2
defect of silicon wafer substrate. Heike Lauber, Sven Miinzberger (Heike Lauber,
Sven Miinzberger, 2007) focused on post-polishing parameter and DIW rinsing
optimization for CMP defect reduction for semiconductor substrate wafer. Most of
the studies focusing on semiconductor wafer. At present, very less of the approach
focusing on polishing parameter optimization for CMP defects reduction for hard
disk substrate even there are a lot of study focusing on silicon wafer substrate.
From the data collected from one of the world leading hard disk
manufacturer, from total of 32,224 defect points analyzed from 1611 substrate
surfaces it showed that highest contributor of substrate micro defect was from CMP
defect which were irregular pit and embedded particle as illustrated in Figure 1.0.
Details of irregular pit and embedded particle defect formation will be discussed in
Chapter 2 in literature review. It is mission critical to improve the micro defect count
and optimize the CMP process.
Figure 1.0: Microdefect AFM analysis fallout.
1.3 Objective of study
Objective of the study are:
1. To characterize the CMP process factors that influence to the process output
response.
3
2. To identify the main factor and interaction factor that contribute to the CMP
process response.
3. To optimize the CMP process from the experiment analysis result.
1.4 Scope of study
The scope of the study are:-
i. Polishing 2nd step of Al-Mg Nickel Phosphorus coated substrate disk, 1.27
mm thickness as work material by effect of Chemical Mechanical
Planarization.
ii. The study will be carried out using double sided Precision Polishing Machine
with polyurethane polishing pad and colloidal silica based slurry.
iii. The process input parameter to be tested are Pressure, Speed, Slurry
discharge point and Slurry flow rate.
iv. The response performance to be analyzed are material removal (MRR), micro
waviness, surface micro defect count.
v. Design of Experiment (DOE) design and analysis will be using Minitab
Release 16.
vi. Surface quality analysis will be using Atomic Force Microscopy (AFM) for
micro-defect detection scanning and count and surface topography.
This project is to study the 2nd step polishing parameters effect towards
upstream (1st step) defects removal. Assuming all slurry related parameter already
optimized including hybrid Al2SO3 slurry particle at 1st step polishing with lower
slurry particle size 0.35~0.5m, constant oxidizing agent which uses H2O2 0.5% -
1% concentration at of 1.3~1.7pH after mix and no interference from the polishing
pads parameters characteristics. This study only includes the defect formation of the
effect of 1st step polishing process and embedded particle as results of uneven
thickness removal due to imbalance between mechanical and chemical removal and
4
determination of the optimal 2nd step polishing parameter setup. This study will not
covers the post-CMP cleaning and mechanical brushing to condition or pad
conditioning.
1.5 Research Question
1. What are the optimum parameter setting for achieving the optimum substrate
CMP characteristic?
2. What are the main influence factor or interaction of the influence factor that
affecting output response of the process?
3. What is the predicted model can be generated for the output response?
4. What is the maximum variation percentage of error between experimental
and predicted for output response?
1.5 Significance of study
Since the rapid technology changing which require higher substrate areal
density which require the reduction of sub-micron defect and improvement of
surface roughness and waviness, the outcome of the study will help to increase to
improve the standards of the recording substrate to high-end product. It will also
help to assist industries to select the optimum parameter setting for the
manufacturing process.
64
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65
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