FLCC Seminar

1
FLCC Seminar

• Title Effects of CMP Slurry Chemistry on
  Agglomeration of Alumina Particles and
• Copper Surface Hardness
• Faculty Jan B. Talbot
• Student Robin Ihnfeldt
• Department Chemical Engineering
• University University of California, San Diego

2
Introduction
Integrated Circuit manufacturing requires
material removal and global planarity of wafer
surface Chemical Mechanical Planarization (CMP)

• CMP slurries provide material removal by
• Mechanical abrasion
• Nanometer sized abrasive particles (alumina)
• Chemical reaction
• Chemical additives (glycine, H2O2, etc.)

• Material Removal Rate (MRR) is affected by
• Abrasive size and size distribution
• Wafer surface hardness
• Cu is the interconnect of choice- our research
  focus

3
CMP Schematic
P 1.5-13 psi
V 20-90 rpm
slurry
(100-300 ml/min)
wafer carrier
polishing pad
wafer
(polyurethane)
platen
Cu MRR 50 - 600 nm/min Planarization time 1- 3
min RMS roughness lt 1 nm
wafer
Particle concentration 1 - 30 wt Particle
size 50 - 1000 nm dia
slurry
polishing pad
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Motivation

• Better process control
• Understand role of slurry chemistry (additives,
  pH, etc.)
• Develop slurries to provide adequate removal
  rates and global planarity
• Prediction of material removal rates (MRR)
• Predictive CMP models - optimize process
  consumables
• Improve understanding of effects of CMP variables
• Reduce cost of CMP
• Reduce defects
• Control of abrasive particle size
• Control of interactions between the wafer surface
  and the slurry

5
Research Approach

• Experimental study of colloidal behavior of CMP
  slurries
• Zeta potential and particle size distribution
  measurements
• Function of pH, ionic strength, additives
• Alumina particles in presence of common Cu CMP
  additives
• Alumina particles in presence of copper
  nanoparticles
• Measurement of surface hardness as function of
  slurry chemistry
• Develop comprehensive model (Lou Dornfeld,
  IEEE, 2003)
• Mechanical effects (Dornfeld et al., UCB)
• Electrochemical effects (Doyle et al., UCB)
• Colloidal effects (Talbot et al., UCSD)

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Common Cu Slurry Additives
Additives Name Concentration
Buffering agent NH4OH, KOH, HNO3 bulk pH 3-8
Complexing agent - bind with partial or fully charged species in solution Glycine, Ethylene-diamine-tetra-acetate (EDTA), citric acid 0.01-0.1M
Corrosion inhibitor - protect the wafer surface by controlling passive etching or corrosion Benzotriazole (BTA) 3-amino-triazole (ATA) KI 0.01-1wt
Oxidizer - cause growth of oxide film H2O2, KIO3, K3Fe(CN) citric acid 0-2 wt
Surfactant - increase the solubility of surface and compounds Sodium-dodecyl-sulfate (SDS), cetyltrimethyl-ammonium-bromide (CTAB) 1-20 mM
Robin Ihnfeldt and J.B. Talbot. J. Electrochem.
Soc., 153, G948 (2006). Tanuja Gopal and J.B.
Talbot. J. Electrochem. Soc., 153, G622 (2006).
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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 ? 2CuO22- 6H 2e
• Complexation (to enhance solubility)
• Cu2 HL ? CuL H

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Chemical Phenomena Chemistry of Glycine-Water
System
copper-water system CuT10-5M
copper-water-glycine system LT10-1M,
CuT10-5M
Ref. Pourbaix (1957) (Aksu and Doyle (2002)
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Colloidal Aspects of CMP

1. Particle particle
2. Particle surface
3. Particle dissolution product
4. Surface dissolution product

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Experimental Procedure

• Slurry Abrasives
• 40 wt a-alumina slurry (from Cabot Corp.)
• 150nm average aggregate diameter 20nm primary
  particle diameter
• Common Copper CMP Slurry Additives
• Glycine, EDTA, H2O2, BTA, SDS
• Copper nano-particles
• Added 0.12 mM to simulate removal of copper
  surface during CMP
• lt100 nm in diameter (from Aldrich)
• Zeta Potential and Agglomerate Size Distribution
• Brookhaven ZetaPlus
• Zeta Potential Electrophoretic light scattering
  technique (2)
• Agglomerate Size Quasi-elastic light scattering
  (QELS) technique (1)
• All samples diluted to 0.05 wt in a 1 mM KNO3
  solution
• Solution pH adjusted using KOH and HNO3 and
  ultrasonicated for 5 min prior to measuring

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Electrical Double Layer

• 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 de
  Waals attractive forces
• If potentials are near zero, abrasive particles
  may agglomerate

ionic strength
Zeta Potential
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Zeta Potential
Zeta Potential - Potential at the Stern
Layer Electrophoresis Zeta potential estimated
by applying electric field and measuring particle
velocity
Surface charge on metal oxides is pH dependant
M-OH OH- ? M-O- H2O M-OH H ? M-OH2

• IEP at z 0
• Slurries are stable when z gt 25 mV

Cabot alumina without additives in 10-3M KNO3
solution (bars indicate standard deviation of
agglomerate size distribution)
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Zeta Potential
Cabot alumina in 10-3M KNO3 solution with and
without 0.12mM copper

• IEP 6.5 with and without copper
• IEP9.2 for a-alumina from literature
• Impurities (NO3-, SO42-, etc.) may lower IEP
• At high pH values magnitude of zeta potential
  lower with copper than without

M.R. Oliver, Chemical-Mechanical Planarization
of Semiconductor Material, Springer-Verlag,
Berlin (2004). G.A. Parks, Chem. Tevs., 65, 177
(1965).
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Agglomerate Size Distribution
Cabot alumina dispersion in 1mM KNO3 solution
with (red) and without (blue) 0.12 mM copper and
without chemical additives

• pH 2 presence of copper causes decrease in
  agglomeration
• pH 7 presence of copper causes increase in
  agglomeration

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Copper-Alumina-Water System
Potential-pH for Copper-water System Cu10-4M
at 250C and 1atm (M. Pourbaix 1957)
IEP of CuO 9.5
Agglomeration behavior is consistent with the
Pourbaix diagram
Average agglomerate size of bimodal distributions
in a 1 mM KNO3 solution
G.A. Parks, Chem. Tevs., 65, 177 (1965). Robin
Ihnfeldt and J.B. Talbot. J. Electrochem. Soc.,
153, G948 (2006).
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Zeta Potential
Cabot alumina in 0.1M glycine and 10-3M KNO3
solution with and without 0.12mM copper

• IEP 6.5 without copper
• IEP9.2 increased with copper

M.R. Oliver, Chemical-Mechanical Planarization
of Semiconductor Material, Springer-Verlag,
Berlin (2004). G.A. Parks, Chem. Tevs., 65, 177
(1965).
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Copper-Glycine-Water System
Potential-pH for Copper-Glycine-Water
System Cu10-4M, Glycine10-1M at 250C and
1atm

• Agglomeration behavior is consistent with
  Pourbaix diagram

Average agglomerate size of bimodal distributions
in a 1 mM KNO3 solution with various additives
S. Aksu and F. M. Doyle, J. Electrochemical
Soc., 148, 1, B51 (2006).
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Measuring Wafer Hardness
TriboScope Nanomechanical Testing system from
Hysitron Inc.

• 1 cm2 silicon wafer pieces sputter deposited with
  30 nm Ta 1000 nm Cu
• 10 min exposure in 100 ml of slurry solution
  (without abrasives), then removed and dried with
  air and measured

• Considerations
• Large applied load will increase indentation
  depth
• more likely for underlying layer to affect
  nanohardness measurements
• Slurry solutions with high etch rates will
  decrease copper thickness
• thinner copper layer more likely for underlying
  layer to affect measurements

Robin Ihnfeldt and J.B. Talbot. 210th Meeting
Electrochem. Soc., Cancun, Mexico, Oct. 29-Nov.
3, 602, 1147 (2006).
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Copper Surface in Solution
Bulk metallic Cu H 2.3 GPa
Ta2O5 H9 GPa
Surface nanohardness of Cu on Ta/Si (100uN
applied load) after exposure to 1mM KNO3 solution

• pH 2 appears that state of surface is Cu
  metal with increase in nanohardness from
  underlying layer
• pH 7 and 12 hardness less than that of bulk
  metallic Cu
• Cupric hydroxide, Cu(OH)2, is most likely forming

S. Chang, T. Chang, and Y. Lee, J.
Electrochemical Soc., 152, (10), C657 (2005).
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Copper Surface in Solution
Surface nanohardness of Cu on Ta/Si (100uN
applied load) after exposure to 1mM KNO3 solution
and other additives
Film Growth
Increased Hardness

• Glycine
• Surface hardness is less than that of bulk Cu at
  pH 2 and 12
• Glycine may interact with surface layer to
  decrease compactness
• pH 7 appears to be Cu metal with increase due to
  underlying layer
• Glycine H2O2
• H2O2 increases solubility of Cu-glycinate complex
  or increases Cu oxidation
• Surface is less than bulk Cu at pH 2 and 7
  decrease in compactness due to glycine
• pH 12 appears to be cuprous oxide, Cu2O

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CMP Experiments

• Toyoda Polishing apparatus
• (UC Berkeley)
• IC1000 polishing pad pre-conditioned for 20
  minutes with diamond conditioner
• Polished 2 min with Cabot alumina

• Silicon wafers (100 mm dia.) with 1 mm copper on
  30 nm tantalum
• Total of 18 wafers polished with various slurry
  chemistries and at various pH values

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Experimental Copper CMP MRR
MRR is lt20 nm/min for all pH values without
additives, with 0.1M glycine
MRR is gt100 nm/min for several pH values where
both glycine and H202 are present
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Lou and Dornfeld CMP Model
Basic Eqn. of Material Removal MRR N x Vol
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Conclusions

• Colloidal Behavior
• pH has greatest effect on colloidal behavior
• Glycine acts as a stabilizing agent for alumina
• Presence of Cu nanoparticles can increase or
  decrease agglomeration depending on the state of
  copper in solution
• Agglomeration behavior with copper is consistent
  with potential-pH diagrams
• Nanohardness of Copper Surface
• pH of the slurry affects copper surface hardness
• Addition of chemical additives has large effect
  on the surface hardness
• State of copper on surface is consistent with
  potential-pH diagrams
• Under certain conditions glycine may cause
  decrease in copper surface hardness

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Future Work

• Continue to investigate effect of copper on zeta
  potential and particle size
• Determine state of Cu in solution
• Study agglomeration as a function of time
• Initial hardness measurements show large
  differences in copper surface with pH and
  chemical addition
• Determine reproducibility of hardness
  measurements
• Determine state of Cu on surface
• Modeling Luo and Dornfeld Model
• Incorporate experimental measurements (hardness
  and agglomerate size distribution) into model and
  compare with experimental CMP data

J. Luo and D. Dornfeld, IEEE Trans. Semi.
Manuf., 14, 112 (2001).
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Acknowledgments

• Funded by FLCC Consortium through a UC Discovery
  grant. We gratefully acknowledge the companies
  involved in the UC Discovery grant Advanced
  Micro Devices, Applied Materials, Atmel, Cadence,
  Canon, Cymer, DuPont, Ebara, Intel, KLA-Tencor,
  Mentor Graphics, Nikon Research, Novellus
  Systems, Panoramic Technologies, Photronics,
  Synopsis, Tokyo Electron
• Prof. Dornfeld and his research group at UC
  Berkeley for use of the CMP apparatus and model
  program
• Prof. Talke and his research group at UCSD for
  the use of the Hysitron Instrument.