1 colloidal aspects of chemical mechanical polishing (cmp) tanuja gopal & jan talbot chemical...
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Colloidal Aspects of Chemical Mechanical Polishing (CMP)
Tanuja Gopal & Jan Talbot
Chemical Engineering Program
University of California, San Diego
May 10, 2004
2
Outline
Introduction
Background & Motivation
Research Approach
Experimental Results
Conclusions
Future Work
3
What is CMP?
Unplanarized
Surface smoothing
Localplanarization
Globalplanarization
Ref.: Steigerwald, J. M., Murarka, S. P. and R. Gutmann, Chemical Mechanical Planarization of Microelectronic Materials, Wiley and Sons, New York (1997).
CMP is a method through synergistic effects of chemical and mechanical forces to achieve local and global planarization of Integrated Circuit (IC) structures.
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CMP Applications
Oxide CMP
Metal CMP
Barrier Layer DepositionPatterning Dielectric
Blanket Metalization After CMP
Cu
SiO2
CMP
Ta
Si Si
CMPSiO2
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CMP Schematic
slurry
wafer polishing pad
platen head
polishing pad
wafer
slurry
wafer carrier
P = 1.5-13 psi
(100-300 ml/min)V= 20-60 rpm
(polyurethane)
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CMP Parameters
Process Variables Wafer down pressure Wafer velocity Pad characteristics Particle characteristics Slurry chemistry Substrate characteristics
Process Results Material Removal Rate Planarization Surface finish
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Typical Process Conditions
Wafer Wafer rotational speed = 20 - 60 rpm Applied pressure = 1.5-13 psi
Slurry Flow rate = 100 - 300 ml per min Particle type = silica, alumina, ceria, titania, etc. Particle concentration = 1 - 30 % by weight Particle size = 50 - 1000 nm diameter
Removal Rate SiO2 = 200 - 300 nm per minute Cu or W = 300 - 600 nm per minute Planarization time = 1- 3 min RMS roughness = < 1 nm
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Mass Transfer Process
(a) movement of solvent into the surface layer under load imposed by abrasive particle (b) surface dissolution under load(c) adsorption of dissolution products onto abrasive particle surface (d) re-adsorption of dissolution products (e) surface dissolution without a load (f) dissolution products washed away or dissolved
Surface
Dissolution products
Abrasive particle
Surface dissolution
Ref.: L. M. Cook, J. Non-Crystalline Solids, 120, 152 (1990).
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CMP Defects
Surface Particle Embedded
ParticleRipout Residual
Slurry Micro-scatch
Dishing
Ref.: Philipossian et al. (2001)
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Why CMP ?
Multi-material surfaces
Global planarization 200 and 300 mm (8 and 12 inch) wafers ICs have feature sizes <0.2 m RMS roughness: < 1nm
Disadvantages Large water consumption CMP defects End point detection
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Motivation for Research
Fundamental understanding of chemical effects in CMP Role of slurry chemistry not understood (additives, ionic
strength, pH) Optimize slurries -high removal rates w/ adequate planarity Reduce consumables (slurries are expensive, mostly not
recycled) Enhance post CMP cleaning – large water usage Focus on Copper CMP – Cu interconnect of choice
Lack of comprehensive CMP model Lou and Dornfeld CMP mechanical model- add colloidal effects
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Research Approach
Experimental study of colloidal behavior of CMP slurries Zeta potential and particle size distribution measurements
Function of pH, ionic strength, additives Commercial alumina slurries Alumina – no additives Alumina in presence of common Cu CMP additives Agglomeration during CMP
Incorporate colloidal chemistry into existing mechanical model by Lou and Dornfeld Average particle size, standard deviation parameters Comparison to literature material removal rates
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Cu CMP Chemical Reactions
Dissolution:Cu(s) + HL CuL+(aq) + H+ + e Oxidation:2Cu + H2O Cu2O + 2H+ + 2e
Oxide dissolution: Cu2O + 3H2O 2CuO2
2- + 6H+ + 2e
Complexation (to enhance solubility)Cu2+ + HL CuL+ + H+
Cu
CuO, Cu2O, CuL2
CuL+, Cu2+, Cu+
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Pourbaix Diagrams
Pourbaix diagrams-predicts stable phases in aqueous systems at equilibrium
copper-water system, [CuT]=10-5M
Ref.: Aksu and Doyle (2002)
copper-water-glycine system, [LT]=10-1M [CuT]=10-5M
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Colloidal Aspects of CMP
1) Particle – particle
2) Particle – surface
3) Particle – dissolution product
4) Surface – dissolution product
Surface
Abrasive particleDissolution product
Interaction forces influence particle stability, aggregation,deposition
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Electrical Double Layer
+ +
++
+
+ + +++
+
++
++
+
+
+
+
+
+
a
+
+
+
Distance
Pote
ntia
l
1/
Diffuse Layer
Shear Plane
Particle Surface
2/122000⎟⎟⎠
⎞⎜⎜⎝
⎛=
RTIF
roεε
∑=i
ii zcI 2
2
1
εη /u=
EVu /=•Potential at surface usually stems from adsorption of lattice ions, H+ or OH-
•Potential is highly sensitive to chemistry of slurry
•Slurries are stable when all particles carry same charge; electrical repulsion overcomes Van der Waals attractive forces
•Agglomeration may occur for || < 5mV.
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Measurement of Zeta PotentialEYEPIECE
PRISM
MICROSCOPE
calculated using Smoluchowski eqn:
(valid for a >>1)
= vη/εE
Particle velocity measured through microscope using rotating prism technique
•Pen Kem Lazer Zee Meter•accuracy = ± 5mV
• Brookhaven ZetaPlus•accuracy = ± 2%•particle size-light scattering
||≥ 30 mV: stable
|| < 5 mV: agglomeration
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Background – Colloidal Effects
Zeta potential and iso-electric point (IEP, pH where surface charge is neutral) of polished surface and abrasive particle is important
Ref.:Malik et al. (1997)
-100
-80
-60
-40
-20
0
20
40
1 2 3 4 5 6 7 8 9 10
Al2O3
SiO2
W
Polishing Regime
pH
Zet
a P
oten
tial
(m
V)
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Colloidal effects• Maximum polishing rates for glass observed compound IEP ~ solution pH > surface IEP(Cook, 1990)
• Polishing rate dependent upon colloidal particle - W in KIO3 slurries (Stein et al., J. Electrochem. Soc. 1999)
Pol
ish
ing
rate
(
/min
)
Colloid oxide
Gla
ss p
olis
hin
g ra
te (m
/min
)
Oxide Isoelectric point
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Agglomeration
Agglomeration process of the slurry versus pH, additive concentration, and ion concentration
(Bellman et al., 2002)
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Removal Rate in CMP
Preston’s Equation - most widely used model in CMP: MRR = K*V*P – MRR = Material removal rate
– K = Preston constant– P = Pressure in the wafer- pad space– V = Linear pad- wafer velocity
Drawbacks of Preston’s Eqn: Does not take into account
chemical synergistic effects Fails to provide insight into the
interaction process (particle size, concentration, pad variables etc.)
Ref.: Luo and Dornfeld (1998)
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Model Review
Mechanical Models:•Boning (2001)
•Parameters:P,V, pattern density, step height•Discretize the chip to create a P profile then use Preston’s Eqn. to calculate removal rate.
•Dornfeld (2001)•Parameters: P, V, pad hardness, pad roughness, abrasive size, abrasive geometry, wafer hardness•MRR = w N Vol
w = density of wafer•N = number of active abrasives•Vol = volume removed by single abrasive
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Model Review
Chemical Models:•Stein model (1999) : MRR = k’PV/(1+k”PV)
•Main variables: type of colloidal species and concentration•Chemistry, particle size, P, V constant•Found that MRR and temperature were functions of colloid species concentration
•Subramanian model (1999): mass transport model•Chemical removal of material coupled with mass transport•MRR lower than observed rates because excludes mechanical action
•Gutman (2000): MRR = k’[O]/(1+k”[O])•Main variable: Oxidizer concentration•MRR increases with oxidizer concentration upto saturation point (2 wt %)
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Model Review
Synergistic Model:•Gokis (2000)- MRR results from abrasive and chemical action
MRR = kchem (RRmech)o + kmech (RRchem)o
(RRmech)o = mechanical wear = Ke PV
(RRchem)o = chem. dissolution = kr exp(-E/RT)Cin
kchem = factor accounting for chemical modification
kmech = factor accounting for abrasive activation
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Effects of glycine and H2O2 on Cu removal rate
0
100
200
300
400
500
0 2 4 6 8 10
H2O2 wt%
Material Removal Rate (nm/min)
.. 0.1M glycine
without glycine
etch rate withoutglycine
(Seal et al., 2003)
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Experimental Study
Alumina, silica pH Ionic strength Ultrasonication Cu CMP additives
Stability of colloidal particles
A) Measurement of Zeta Potential
B) Measurement of particle size and distribution as function of slurry chemistry
Coagulation/ well-dispersed Bi-modal – near IEP
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Research Study
Experiments Ceralox® alumina
DI H2O w/ KCl to alter ionic strength –(Babu et al., 2000)
Commercial alumina slurries from Stein (Sandia National Laboratories)
EKC Tech slurry (Doyle, UCB)- Cu CMP slurry additives
Model MRR predictions vs. literature experimental polishing data Average particle size and standard deviations used in Lou and Dornfeld
model
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Alumina particles in DDI H2O
-40
-30
-20
-10
0
10
20
30
40
50
60
3 4 5 6 7 8 9 10 11 12
pH
Zeta Potential (mV)
...
IEP 9
(Sumitomo Chem. Co.,250 nm)
(Ceralox®, 300 nm)
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Ceralox® alumina – ionic strength
Ionic Strength: 10-4 to 10-7M
-50
-40
-30
-20
-10
0
10
20
30
40
3 4 5 6 7 8 9 10 11
Zeta Potential (mV)
1.0E-08
1.0E-07
1.0E-06
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
Ionic Strength (M)
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vs. pH for Ceralox alumina particles with 10-3M KNO3
IEP ~9, agglomerationBroader distribution near IEPAverage size 300 nm
Standard deviation pH 3.5-7 ~ 10 nm pH 9 ~300 nm
-50
-40
-30
-20
-10
0
10
20
30
40
3 5 7 9 11
pH
Zeta Potential (mV)
...
0
0.5
1
1.5
2
Effective Diameter
(microns)
...
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500 3000 3500Diameter (nm)
Intensity
pH 8.8pH 5.6
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Common Cu slurry additives
Additives Name Concentration
Buffering agent NH4OH, KOHKOH, HNOHNO33 bulk pH 3-8
Complexing agent GlycineGlycineEthylene-diamine-tetra-acetate(EDTAEDTA)citric acid
0.01-0.1M
Corrosion inhibitor Benzotriazole (BTABTA)3-amino-triazole (ATA) KI
0.01-1wt%
Oxidizer HH22OO22, KIO3, K3Fe(CN)
citric acid
0-2 wt%
Surfactant Sodium-dodecyl-sulfate (SDSSDS), cetyltrimethyl-ammonium-bromide (CTAB)
1-20 mM
32
and particle size vs. pH for EKC Tech alumina with 10-3M KNO3
IEP ~9 → agglomeration varied by±15%200 nm - pH<8
-40
-20
0
20
40
60
3 4 5 6 7 8 9 10 11
pH
Zeta Potential (mV)
..
0
500
1000
1500
2000
2500
3000
3500
Effective Particle Size (nm)
..
particle size standard deviation < 5nm for pH>8 > 300 nm for pH<8
33
and particle size vs. pH for EKC Tech alumina with 10-3M KNO3 and glycine
IEP ~9, agglomeration varied by ±2%200 nm pH<8
-30
-20
-10
0
10
20
30
40
50
60
70
3 4 5 6 7 8 9 10 11
pH
Zeta Potential (mV)
...
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Effective Particle Size (nm)
...
0.001M glycine
0.01M glycine
0.1M glycine
34
and particle size vs. pH for EKC Tech alumina with 10-3M SDS and 10-3M KNO3
ranged from -34 to -46 mVAverage particle size ~220nm (approximately double stated size)Particle size standard deviation small (< 5nm)
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
3 5 7 9
pH
Zeta Potential (mV)
..
100
150
200
250
300
Particle Size (nm)
..
-5
0
5
10
15
20
25
30
0 50 100 150 200 250 300 350
Particle Size (nm)
Percentage
pH 6
35
and particle size vs. pH for EKC Tech alumina with 0.01 wt% BTA or 0.01M EDTA & 10-3M KNO3
BTA - no effectEDTA - shifted IEP to pH 5, large particles
-30
-20
-10
0
10
20
30
40
50
3 4 5 6 7 8 9 10 11
pH
Zeta Potential (mV)
..
0
500
1000
1500
2000
2500
3000
Effective Particle Size (nm)
..
-30
-20
-10
0
10
20
30
40
50
3 4 5 6 7 8 9 10 11pH
Zeta Potential (mV)
..
0
500
1000
1500
2000
2500
3000
Effecti ve Particle Size (nm)
..
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Lou and Dornfeld Mechanical Model
Slurry Concentration C
Average Abrasive Size Xavg
Proportion of Active Abrasives
N
Force F & Velocity
Active Abrasive Size Xact
Passivation rate
Wafer hardness Hw
Vol
Basic Eqn. of Material Removal: MRR = N x Vol
Ref.: Lou and Dornfeld (2001)
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Overall Research Approach
Comprehensive Model (Dornfeld, 2003)a) Mechanical effects (Dornfeld et al., UCB)b) Electrochemical effects (Doyle et al., UCB)c) Colloidal effects (Talbot & Gopal, UCSD)
(Moon and Dornfeld et al. 1999)Slurry film thickness (mm)
•Si Wafer
•Pressure: 1.5 psi
•Velocity: 2-12 rpm
•Polishing time: 2-4 hours
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Model Sensitivity to Standard Dev.
Simplified dependency on standard deviation
For xavg <500 nm small variation σ results in large % change in MRR
( )MRR
x
x
avg
avg
∝+ 3
2
3
σ
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Collision Efficiency
•CMP 104-106 s-1
•Collison Efficiency)fraction collisions → permanent attachment
•Most particles do not agglomerate
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
10000 100000 1000000
G (Shear Rate s -1)
Collision Efficiency
...
1000 nm
500 nm
300 nm
100 nm
104 106105
⎥⎦
⎤⎢⎣
⎡=
336)(
Ga
Aafo πμ
α
40
Maximum Aggregate Size
Effective Particle Size (nm) Max. Aggregate Size (nm)
Shear rate 104s-1
100 180
200 or greater Total aggregate break up
Shear rate 103s-1
100 1800
200 900
300 600
400 or greater Total aggregate break up
Rmax =
2/1
218⎟⎠
⎞⎜⎝
⎛δπGa
A
41
•P = 1 psi, 4 inch blanket wafer, wafer carrier & platen velocity = 100 rpm, pad hardness = 100 MP, passivation rate = 100 nm/min
42
MRR prediction and particle size for alumina with and without glycine
Max. MRR 160 nm/min without additives
Max. MRR 120nm/min with 0.1M glycine
0
50
100
150
200
250
3 4 5 6 7 8 9 10 11
pH
MRR (nm/min) @ 1psi
..
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size (nm)
..
0
50
100
150
200
250
3 4 5 6 7 8 9 10 11
pH
MRR (nm/min) @ 1psi
...
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size (nm)
No additives 0.1 M glycine
43
MRR prediction and particle size for alumina with glycine and hydrogen peroxide
Max. MRR 170 nm/min with 0.1 wt% H2O2
Max. MRR 220 nm/min with 2 wt% H2O2
0.1M glycine, 0.1wt% H2O2
0
50
100
150
200
250
3 5 7 9 11
pH
MRR (nm/min) @ 1psi
..
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size (nm)
..
0
50
100
150
200
250
3 5 7 9 11
pH
MRR (nm/min) @ 1psi
..
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size (nm)
..
0.1M glycine, 2 wt% H2O2
44
MRR prediction and particle size for alumina with Cu slurry additives
MRR 1-10 nm/min
Particle size 0.5 -3 microns
0.01wt% BTA, 10-3M SDS, 0.1M glycine, 0.1wt% H2O2,
0123456789
10
3 4 5 6 7 8 9 10 11pH
MRR (nm/min) @ 1psi
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size
(nm)
0
1
2
3
4
5
6
7
8
9
10
3 4 5 6 7 8 9 10 11pH
MRR (nm/min) @ 1psi
0
500
1000
1500
2000
2500
3000
3500
4000
Effective Particle Size
(nm)
0.01wt% BTA, 10-3M SDS, 0.01M EDTA, 0.1wt% H2O2,
45
Summary- effects of additives
Additive Effect
Glycine stabilizing agent
BTA No effect
EDTA Unstable, agglomeration
SDS 2x agglomeration, stable, negative ζ
46
Conclusions
Background electrolyteParticle size distribution vs. IEPEffects of Cu polishing rates w/different chemistries Cu-glycine complexes in presence of H2O2 result in
increased MRR
Slurry additives affect colloidal behavior – pH largest effectLou and Dornfeld model Can predict trends well Model is sensitive to variation of
47
Future Work
Cu CMP Experiments Slurry additives: glycine, hydrogen peroxide
Zeta potential – w/ dissolved Cu or Cu particlesModel improvements Use actual particle distribution Surface hardness link to chemistry Passivation rate of Cu (Doyle)Adhesion tests – post-CMP cleaning