The Effects of Slurry Chemistry on theThe Effects of Slurry Chemistry on theColloidal Behavior of Alumina Slurries andColloidal Behavior of Alumina Slurries and

Copper Copper Nanohardness Nanohardness for Copper Chemicalfor Copper ChemicalMechanical Mechanical Planarization Planarization (CMP)(CMP)

Jan TalbotJan Talbot && Robin Robin Ihnfeldt Ihnfeldt (PhD 2008)*(PhD 2008)*

University of California, San DiegoUniversity of California, San DiegoSept. 22, 2008Sept. 22, 2008

*Apex Medical Technologies, Inc., San Diego

22

CMP - Faculty TeamCMP - Faculty Team

3

Outline- Background

- CMP process- Slurry chemistry and role of abrasives- Research objectives and motivation

- Measuring Colloidal Behavior – Effects of Chemistry- Previous work from T. Gopal (Ph.D. 2004)

- Measuring Nanohardness and Etch Rate – Effects of Chemistry- Review of Luo and Dornfeld CMP Model

- Do agglomerates break up?- Model sensitivity to agglomerate size, distribution, and hardness

- Modeling Results Compared to Experiment- Conclusions- Future Work- Acknowledgments

4

Integrated Circuit (IC) manufacturing requires material removaland global planarity – Chemical Mechanical Planarization (CMP)

CMP slurries provide materialremoval by:Mechanical Abrasion

Nanometer sized abrasiveparticles (alumina)

Chemical ReactionChemical additives (glycine, H2O2,etc.)

Material Removal Rate (MRR)is affected by: Abrasive size and sizedistribution Wafer surface hardness

Cu is interconnect ofchoice- our research focus

Introduction

5

CMP Schematic

Particle concentration = 1 - 30 wt%Particle size = 50 - 1000 nm dia

slurry(100-300 ml/min)

platen

polishing pad

wafer

slurry

wafer carrier

P = 1.5-13 psi

V= 20-60 rpm

Cu MRR= 50 - 600 nm/minPlanarization time = 1- 3 minRMS roughness = < 1 nm

polishing pad

(polyurethane)

6

CMP History-Introduced in 1980’s by IBM-Shrinking device dimensions in IC manufacturing lead to uneven surfaces

-Caused depth of focus problems for photolithography-CMP provided local and global planarization that could not previously be obtained bychemical etching

1http://www.tf.uni-kiel.de/matwis/amat/elmat_en/kap_5/illustr/i5_1_1.html accessed April 20082K. Cadien, “Is There a Future For CMP?” Proceedings for the 13th International CMP for ULSI Multilevel Interconnection

Conference (CMP-MIC), Keynote presentation, Fremont, California, March 4, 2008.

16M DRAM1

8 metal layers2

(state of the art)65nm

1µm

No CMP

7

Copper CMP SlurryAbrasives Most CMP slurries contain alumina or silica abrasives Abrasives may scratch and cause defects

Slurries with abrasives need to be tailored so particle sizes and distributions aresmall enough to not cause scratches

Chemical additives – Surfactants, Complexing agents, Inhibitors, OxidizersChemicals may cause corrosion and low MRRs

Studies of chemistry effects on colloidal behavior of abrasives and thewafer surface are needed

8

Research Objectives

Understand effects of slurry chemistry on Cu CMPprocess Colloidal behavior measured by zeta potential and

agglomerate size distribution - effects of chemicaladditives and presence of copper

Copper surface hardness and etch rate as function ofchemistry

Infer state of the Cu (Cu, CuO, Cu2+, etc.) in slurry andon surface

Use colloidal behavior, nanohardness and etch rates inmodel of CMP

9

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

10

Slurry Abrasives40 wt% α-alumina slurry (from Cabot Corp.)150nm average aggregate diameter – 20nm primary particle diameter

Common Copper CMP Slurry AdditivesGlycine, ethylene-diamene-tetra-acetic acid (EDTA), hydrogen peroxide(H2O2), benzotriazole (BTA), sodium-dodecyl-sulfate (SDS)

Copper nano-particlesAdded to simulate removal of copper surface during CMP<100 nm in diameter (from Aldrich)

Zeta Potential and Agglomerate Size DistributionBrookhaven 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 solutionSolution pH adjusted using KOH and HNO3 and ultrasonicated for 5 minprior to measuring

Measuring Colloidal Behavior –Experimental Procedure

11

Zeta PotentialDiffuse Layer

ShearPlane

ParticleSurface

1/κ

Zeta Potential - Potential at the Stern LayerElectrophoresis – Zeta potential estimated byapplying electric field and measuring particlevelocity

+ +

++

+

+ +++++

++

++

++

+

+

++

a

+

+

+

Distance

Pot

entia

l

ζ

Stern Layer

-60

-40

-20

0

20

40

60

80

2 4 6 8 10 12

pH

t Z

eta

Po

ten

tial (m

V)

0

1000

2000

3000

4000

g A

gg

lom

era

te S

ize (

nm

)Surface charge on metal

oxides is pH dependant:

Isoelectric point (IEP) at ζ = 0 Slurries are stable when |ζ | > 25 mV

M-OH + H+ → M-OH2+

M-OH + OH- → M-O- + H2O

Cabot alumina without additives in 10-3M KNO3 solution(bars indicate standard deviation of agglomeratesize distribution)

12

Previous WorkPrevious work done by T. Gopal (PhD_2004). Colloidal Behavior - Zeta potential and particle size distribution

measurements were taken for EKC Tech alumina Various common copper slurry additives were investigated Solution pH varied from 3-10

Experimental Results1: pH had the largest effect on particle size Additives typically decreased magnitude of zeta potential, and increased

particle sizes Modeling - Incorporated particle size distributions into Luo and

Dornfeld model and compared to experimental CMP from literatureModeling Results2:

Model capable of predicting trends of MRR with changes in chemistry asobserved in literature

1T. Gopal and J. B. Talbot, J. Electrochemical Soc., 153, G622 (2006). 2T. Gopal and J. B. Talbot, J. Electrochemical Soc., 154, H507 (2007).

13

Zeta PotentialCabot alumina in 10-3M KNO3 solution with and without 0.12mM copper

IEP ~6.5 with and without copper IEP~9.2 for α-alumina from literature* Presence of NO3

-, SO42-, etc., can lower IEP**

At high pH values magnitude of zeta potential is smaller with copperthan without

*M.R. Oliver, Chemical-Mechanical Planarization of Semiconductor Material, Springer-Verlag, Berlin (2004).**G.A. Parks, Chem. Tevs., 65, 177 (1965).

-80

-40

0

40

80

0 2 4 6 8 10 12

pH

Zeta

Po

ten

tial (m

V)

Without Cu

With Cu

14

Agglomerate Size Distribution

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

Cabot alumina dispersion in 1mM KNO3 solution with (red) and without(blue) 0.12 mM copper and without chemical additives

pH 2

0

10

20

30

0 2 4

Agglomerate size (µm)

% in

so

luti

on

pH 7

0

10

20

30

40

0 5 10

Agglomerate size (µm)

% in

so

lutio

n

15

Potential-pH forCopper-waterSystem[Cu]=10-4M at 250C and1atm (M. Pourbaix 1957)

■ Agglomeration behavior is consistent with the Pourbaix diagram

Copper-Alumina-Water System

Average agglomerate size of bimodal distributions in a 1 mM KNO3 solution

IEP of CuO ~ 9.5*

*G.A. Parks, Chem. Tevs., 65, 177 (1965).

Small

Average (nm)

Large

Average (nm)

Small

Average (nm)

Large

Average (nm)

2 Cu, Cu+

170 5000 160 810

7 Cu, Cu 2O, CuO, Cu(OH) 2 580 3300 1700 9400

10 Cu, Cu 2O, CuO, Cu(OH) 2 150 720 300 1600

Without Copper With Copper

Possible State of CopperpH

16

Zeta PotentialCabot alumina in 0.1M glycine and 10-3M KNO3 solution with and

without 0.12mM copper

IEP ~6.5 without copper IEP~9.2 increased with copper

*M.R. Oliver, Chemical-Mechanical Planarization of Semiconductor Material, Springer-Verlag, Berlin (2004).**G.A. Parks, Chem. Tevs., 65, 177 (1965).

-80

-40

0

40

80

0 2 4 6 8 10 12

pH

Zeta

Po

ten

tial (m

V) Without Cu

With Cu

17

Potential-pH for Copper-Glycine-Water System*[Cu]=10-4M, [Glycine]=10-1M at 250Cand 1atm

Agglomeration behavior is consistent with Pourbaix diagram

Average agglomerate size of bimodaldistributions in a 1 mM KNO3 solution withvarious additives

Copper-Glycine-Water System

*S. Aksu and F. M. Doyle, J. Electrochemical Soc., 148, 1, B51 (2006).

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16pH

E, V

vs. S

HE

Cu2+

CuO/

Cu(OH)2

CuCu2O

CuO22-CuHL2+

CuL+

CuL2

CuL2-

Small

Average (nm)

Large

Average (nm)

Small Average

(nm)

Large

Average (nm)

2 Cu, CuHL2+

310 8100 220 700

7 Cu, CuL 2 2000 1900

10 Cu, CuL 2-, CuL 2 1030 6300 350 2100

Possible State of

Copper

Without Copper With Copper

Solution

0.1M Glycine

pH

18

Hardness Measurements -TriboScope Nanomechanical Testingsystem, Hysitron Inc.1000 nm Cu sputter deposited on 30nm Ta on 1 cm2 silicon wafer pieces10min exposure in 100ml of solution(without abrasives), removed, driedwith air and measuredMaximum applied load varied from50-3000 µNEtch Rates - wafer pieces weighedbefore and after immersion in solution

Experimental Procedure – MeasuringHardness and Etch Rates

19

Measuring Hardness and Etch Rates

■ Considerations* - δmin - Applied load must be large enough to induce plastic deformation

Indentations depths must be >5nm δmax - Applied load must be small enough to not have effects of

underlying layer – even with high etch rate slurry solutions whichdecrease copper thickness

General Rule δmax < 0.1 x film thickness ~100nm

*N. Ye and K. Komvopoulos, J. Tribology, 125, 685 (2003).

20

Nanohardness Before Chemical Exposure

1D. Beegan, S. Chowdhury, and M. T. Laugier, Surface and Coatings Technology, 210, 5804 (2007).2A. Jindal and S.V. Babu, J. Electrochemical Soc., 151 (10), G709-G716 (2004).3M. Ueda, C. M. Lepienski, E.C. Rangel, N. C. Cruz, and F. G. Dias, Surface and Coatings Technology, 156, 190 (2002).4A. Szymanski and J. M. Szymanski, Hardness Estimation of Minerals Rocks and Ceramic Materials, Elsevier Science Publishers B.

V., New York, NY (1989).

0

2

4

6

8

10

0 50 100 150 200 250

Indentation Depth (nm)

Hw

(G

Pa)

Average Hw=2.6 GPa Hardness technique Material Value

Nanohardness (GPa) Cu1

2.5 ± 0.3

Ta2

3.3 ± 1.5

Si3

12 ± 2

Cu2O* 17 ± 5

Moh's hardness4

Cu(OH)2 2.0-2.5

Cu metal 3

CuO 3.5

Cu2O 4

Ta 6.5

Si 6.5

*as measured in this study

Hardness values

Nanohardness vs indentation depthNanohardness near surface (<20nm) >Cu metalPossibly formation of copper oxide

At indentation depth >30nm, hardness of Cu metal

21*M. Pourbaix, Atlas of Electrochemical Equilibria in Aqueous Solutions, National Association of CorrosionEngineers, Houston, Texas (1974).

Hardness values near surface (<40nm) increase as the pH increasesConsistent with potential-pH equilibrium diagrams which indicate that copperoxides are more stable at higher pH*Nanohardness is that of Cu metal for indentation depths >70nm

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120 140

Indentation Depth (nm)

Hw

(G

Pa

)pH 2.9

pH 8.3

pH 11.7

Effect of pH on Hardness

22

Nanohardness vs indentation depth after exposure tosolutions with 0.1M glycine and 2wt% H2O2 at various pH Possible Surface Reactions:

passivation2Cu + H2O → Cu2O + 2H+ + 2eCu2O + H2O → 2CuO + 2H+ + 2e

complex formationCu2O+4HL→2CuL2(s)+H2O+2H++2eCuO + 2HL →CuL2 (s) + H2O

dissolutionCuL2 (s) → CuL2 (l)

decompositionH2O2 + e- → OH* + OH-

* G. Xu, H. Liang, J. Zhao, and Y. Li, J. Electrochemical Soc., 151, (10) G688 (2004).

Effect of Additives on Hardness

At pH 8.3 film is very soft & etch rate is very large (56 nm/min)H2O2 decomposition occurs faster at higher pH.*At pH 10.0, large etch rate, 33 nm/min, but a thick passivation layer possiblyforms which inhibits Cu-glycine complex formation.

0

2

4

6

8

10

12

14

16

18

20

0 25 50 75 100 125 150Indentation Depth (nm)

Hw

(G

Pa)

0.1M Glycine, 2.0wt% H2O2 pH 8.3

0.1M Glycine, 2.0wt% H2O2 pH 10.0

23

Hardness sensitiveto chemistry!(depends on pH, etchrate, and chemicaladditives)

Solutions withoutetching had harderfilms

Solutions with largeetch rates (>14nm/min) have soft(possibly porous) films

For most cases thehardness was that of Cumetal at indentationdepths >70nm

Aqueous solution

with 1mM KNO 3

plus: pH

Etch rate

(±4nm/min)

Hardness for <80nm

indentation depth

(±0.3GPa)

2.9 0.7 1.6-4.6

8.3 0.0 1.8-5.7

11.7 2.6 2.0-7.3

3.1 1.2 1.0-3.5

8.5 7.6 1.6-5.2

10.0 0.0 3.5-16

3.0 45 1.0-4.4

8.3 33 2.1-5.6

10.1 14 0.34-4.7

3.0 38 2.8-8.2

8.3 56 0.1-5.5*

10.0 33 2.3-18

3.0 1.6 1.2-3.3

8.4 0.0 1.7-5.3

10.8 8.6 2.0-4.0*

2.6 0.0 0.9-11*

9.0 0.0 2.5-8.0

10.9 0.0 2.1-13

*Values are different than Cu metal for indentation depths >80nm

0.1 M glycine, 2.0

wt% H 2O2

Combination w/

glycine

Combination w/

EDTA

Etch rates and nanohardness with indentation

depth <80nm

none

0.1 M glycine

0.1 M glycine, 0.1

wt% H 2O2

Nanohardness and Etch RatesFor some cases:

24

Experimental Copper CMPToyoda Polishing Machine1000nm Cu film sputter deposited on 30nm Ta on siliconwafers (100mm diameter)2 min polishing time

Slurry -150 ml/min

IC1000 polishingpadPlaten -

30 rpm

wafer carrier -30 rpm

P0 = 1 psi

25

Luo and Dornfeld CMP Model(IEEE Trans. on Semi. Manuf., 2001)

Overall mass material removal (MRR) rate during CMP:

0CNVMRR

W+= !

where ρw = density of copper surface (g/m3)N = number of active abrasivesV= average volume removed by a single abrasive (m3/min)C0 = removal due to chemical etching (g/min) (negligible?)

*Assume Solid-Solid contact mode.

26

Elastic Deformation of Pad Soft pad Abrasives are small Uniform distribution of asperities Asperities have same radius and

heightPlastic deformation of wafer by

abrasive Small cutting depths (<1µm) cause

brittle materials to be removed inductile manner

Abrasives - spherical geometry Removal due to sliding indentation of

abrasive Normal distribution xact > xavg

Model Assumptions

*J. Luo and D. Dornfeld, IEEE Trans. Semicond. Manuf., 14, 2 (2001).

27

Luo and Dornfeld CMP ModelMaterial removal rate based on thickness:

Thickness Removed (nm/min) Probability of ActiveAbrasives (value = 0-0.5)

*J. Luo and D. Dornfeld, IEEE Trans. Semicond. Manuf., 14, 2 (2001).

Model requires input values for:xavg – average abrasive sizeσ – standard deviation of abrasive size distributionHW – wafer surface hardnessC0 – etch rate

xavg and σ measured under quiescent conditionsDo agglomerates break up during CMP?

( )023

2

223

1321

31961

Cx

HHB

xxxH

BMRR

avg

WPavgavgavgW

+!!"

#

$$%

&

''(

)**+

, +

''(

)**+

,+-.-

''

(

)

**

+

,++=

/

///

vP

R

alEDmdB

a

PSUMasss

0

25

21

1

3

28

!"

" #=31

0

32

34

2

3

4

4

1

!!"

#$$%

&!"

#$%

&!"

#$%

&=

SUM

P

D

P

R

EB

'

EtchRate

28

Shear Force on AbrasivesShear force is directly

proportional to normalforce1,2

µf – coefficient of frictionbetween wafer andpad asperities tips ~0.561 (at vel=0.3m/s)

FN – normal force = PC AS

PC – contact pressure asgiven in L&D model3

AS-surface area of contacton abrasive

For x = 200nmFS = 3.6x10-10 N

1J. Seok, C.P.Sukam, A. T. Kim, J.A. Tichy, T. S. Cale, Wear, 257, 496 (2004).2S. Tsai, L. Chen, L. Sun, R. Mavliev, W. Hsu, L. Xia, R. Morad, IEEE, 7803, 7216 (2002)3J. Luo and D. Dornfeld, IEEE Trans. Semi. Manuf., 14, 112 (2001).

22

42C

W

fWCfSCfNfS Px

H

xPAPFF

µ!!µµµ ="

#

$%&

' (===

29

Force to Break Up AgglomeratesForce required to break up an agglomerate1:

( )SNsBCFF += µ

µs – static coefficient of frictionbetween particles = 0.681

FN – normal force pressing particlestogether = PC AB

CS – cohesive strength of theagglomerate

1R. L. Brown and J. C. Richards, Principles of Powder Mechanics, Pergamon Press, Elmsford, New York (1970).2W. Pietsch, Agglomeration Processes, Wiley-VCH Verlag GmbH, Weinheim, 2002.3J. K. Beddow and T. P. Meloy, Advanced Particluate Morphology, CRC Press, Boca Raton, Florida, 1980.

Cohesive strength of anagglomerate filled with liquid

(Rumpf model)2: a’ – correction factor, has values between 6-82

ε – porosity of agglomerate – (random loosepacking of spheres ε = 0.399)3

α – surface tension of liquid (H20) 2 = 72 dyn/cmdP – primary particle diameter = 20 nm

( )

p

Sd

aC!

"

"#=

1'

( )SCC

W

s

BCPP

H

xF +=

2

2!µFor x = 200 nm

FB = 5.0 x 10-9N

Agglomerates Do NOT Break Up! FB > FS

30

Maximum Agglomerate Size

0

5

10

15

20

25

30

0 2000 4000 6000 8000

Particle diameter (nm)

% in

so

luti

on

Someagglomerates arelarge (>1µm)

Some distributionsare bimodal

Maximumagglomerate sizestable in fluid1,2:

2

1

2max

182 !

"

#$%

&=

'()r

HrD

1M.Elimelech, J. Gregory,X. Jia, and R. Williams, Particle Deposition and Aggregation, Butterworth-Heinemann, Oxford, Great Britian (1995).

2R. Ihnfeldt and J. B. Talbot, J. Electrochemical Soc., to be published (2007).

Alumina dispersion in 0.1M glycine, 0.1wt% H2O2, 0.12mM Cu and 1mM KNO3solution at pH (♦) 8.3 or (■) 10.0.

DMAX ~ 4.8 µm

Parameter Value

Hamaker Constant, H 4.17 x 10-20

J

Primary particle radius, r 10 nm

separation of particles, ! 0.4 nm

shear stress, " 2.1 Pa

31

Luo and Dornfeld CMP Model*Material removal rate based on thickness:

* J. Luo and D. Dornfeld, IEEE Trans. on Semi. Manuf., 14, 2, 2001.

( )023

2

223

1321

31961

Cx

HHB

xxxH

BMRR

avg

WPavgavgavgW

+!!"

#

$$%

&

''(

)**+

, +

''(

)**+

,+-.-

''

(

)

**

+

,++=

/

///

For our Copper CMP experimental conditions:B1 = 3.18 x 107 nm2GPa1.5/min B2 = 1720 kPa

Complicated function of xavg, σ and HW

To predict MRR:For bimodal distributions, xavg and σ from distributions with xavg<DMAX

HW is average of values with applied loads between 50-100µNExperimentally measured CO is incorporated

Thickness Removed (nm/min) Probability of ActiveAbrasives (value = 0-0.5)

EtchRate

32

0

100

200

300

400

0 2 4 6 8 10 12pH

MR

R (

nm

/min

)

ExperimentModel w/ constant HwModel w/ Hw and Co

Experimental and model MRR predictions using 1mM KNO3 solution

Model overpredicts MRR at low pH Predictions at 8.3 and 11.7 agree well with experiment – low etch rate – not

sensitive to HW

Model Predictions

33

0

100

200

300

400

0 2 4 6 8 10 12pH

MR

R (

nm

/min

)

Experiment

Model w/ const Hw

Model w/ Hw and Co

Experimental and model MRR predictions using 1mM KNO3, 0.1M glycine and 0.1wt% H2O2solution

Model predictions do not agree with experiment for all pH values At low pH, meas. HW is too small At pH>8, solution in very chemically active (large etch rates >8nm/min),

model is very sensitive to HW

HW under quiescent conditions different from HW during CMP Surface film to thin to measure using our technique

Model Predictions

34

Model Sensitivity

!!

"

#

$$

%

&++!!

"

#$$%

&3

2

2

2

6 961

min10

avgavgavgxxx

nm ''

0

0.1

0.2

0.3

0.4

0.5

0.6

1 10 100 1000 10000nm

Pro

ba

bil

ity

of

AA

xavg!

0

0.01

0.02

50 150 250 350

xavg → large, xact → xavg and AA→0.5σ → small, xact → xavg and AA →0.5

xavg=270 nmσ=7 nm

MRR predictions exhibit sensitive dependence on xavg and σ Small changes in xavg or σ can cause large differences in predicted

MRR

1

10

100

1000

10000

1 10 100 1000 10000xavg- solid, !- dashed (nm)

MR

R (

nm

/min

)

!=7

!=20

!=100

!

xact=xavg +3σ

35

Model Sensitivitya) HW decreases with increase in

xavg from 100 to 200 nm HW increases with increase in

xavg from 200 to 1000 nm

a)

0

100

200

300

400

0 2 4 6 8 10 12 14 16 18 20

HW (GPa)

Pre

dic

ted

MR

R (

nm

/min

)

100nm

200nm

1000nm

xavg

b)

0

100

200

300

400

0 2 4 6 8 10 12 14 16 18 20

HW (GPa)

Pre

dic

ted

MR

R (

nm

/min

)

10nm

50nm

200nm

!

b) For small MRR predictions modelis insensitive to HW, and for largeMRR predictions model is verysensitive to HW

36

a)

0

50

100

150

0 10 20

HW (GPa)

MR

R (

nm

/min

)

pH 2.9

pH 8.3

pH 11.7

a) xavg and σ values smaller at low pH compared to alkaline conditionsb) Meas. HW to small, need larger HN to achieve exp. MRR.

Thin harder film on surface which cannot be measured using our technique Surface film under quiescent conditions different than during CMP

Model Predictions

b)

0

50

100

150

0 10 20

HW (GPa)

MR

R (

nm

/min

) pH 2.9

Meas.HW

MRR

Prediction

Exp. MRR

Range

HN

MRR model predictions using measured xavg, σ, and C0 for 1mM KNO3 solution, withvarying HW

At pH 8.3 and 11.7 solutions are less chemically active (low etch rate) – not sensitive to HW

37

Model Sensitivity

38

ConclusionsColloidal Behavior pH has greatest effect on colloidal behavior Presence of Cu nanoparticles will increase or decrease

agglomeration depending on state of copper in solution Agglomeration behavior with copper is consistent with

potential-pH diagramsNanohardness and Etch Rates of Copper Surface HW can vary from 0.1-20 GPa depending on chemistry (pH,

additives) due to: formation of different films (CuO, Cu2O, etc.) and/or changes in

compactness from dissolution or complexing reactions HW very sensitive to chemistry

depends on pH, etch rate, and additives For most cases, state of copper on surface is consistent with

potential-pH diagrams

39

Modeling Agglomerated abrasive particles do not break up during CMP Agglomerate size measurements can be used in Luo and

Dornfeld model Model predictions improved using measured HW and C0

compared to using constant HW of Cu metal and neglecting C0 Model only agrees with experiment for solutions with small

etch rates (<8nm/min) and pH>8 MRR is overpredicted for acidic solutions MRR does not agree with experiment for solutions with large

etch rates (>8nm/min) and pH>8 HW under quiescent conditions is different than HW during CMP film is too thin to measure using our technique

Luo and Dornfeld model is useful for Cu CMP

Conclusions

40

Future WorkContinue to develop basic understanding of agglomeration/

dispersion effects on CMP Study rate of agglomeration as a function of chemistry Investigate effects of heating (temperature) on agglomeration and surface

hardness as a function of chemistrySurface Hardness Study effects of exposure time of solution on hardness (film formation due to

fast reaction or slow reaction) Measure nanohardness after exposure to flowing slurry How to probe 1-5nm range? Adjust equipment parameters, use different

AFM tip?Modeling Develop rate of agglomeration model as a function of chemistry which

includes the effects of heating (temperature) Incorporate rate of agglomeration and heating effects into Luo and Dornfeld

CMP model Other Models?

41

Funded by FLCC Consortium through a UC Discovery grant. Wegratefully acknowledge the companies involved in the UC Discoverygrant: 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

Collaboration of Prof. Komvopoulos and Doyle and their researchgroups from UC Berkeley

Prof. Nesterenko from UC San Diego provided expertise indeveloping criteria to determine when agglomerates break apart

Prof. Dornfeld and his research group at UC Berkeley for use ofthe CMP apparatus and model program

Prof. Bavarian at CalStateU-Northridge, and Prof. Talke at UCSDfor the use of the Hysitron Instrument.

Acknowledgments

Prof. Arrhenius from UC San Diego providedthe cuprite samples

Evelyn York for the EDX analysis

42

PublicationsJournal Papers Modeling Material Removal Rates for Copper CMP Using Copper Nanohardness and Etch Rates,

R.V. Ihnfeldt and J.B. Talbot, J. Electrochemical Soc., submitted March 2008. Effect of CMP Slurry Chemistry on Copper Nanohardness, R.V. Ihnfeldt and J. B. Talbot, J.

Electrochemical Soc., 155, H412 (2008). Modeling of Copper CMP Using the Colloidal Behavior of an Alumina Slurry with Copper

Nanoparticles, R.Ihnfeldt and J.Talbot, J. Electrochemical Soc., 154, (12) H1018 (2007). The Effects of Copper CMP Slurry Chemistry on the Colloidal Behavior of Alumina Abrasives, R.

Ihnfeldt and J.B. Talbot. J. Electrochemical Soc., 153, G948 (2006).

Conference Papers and Presentations Modeling Copper CMP Material Removal Rates Using Copper Surface Nanohardness, R. V.

Ihnfeldt and Jan B. Talbot, to be presented at the 213th Meeting of the Electrochemical Society,Phoenix, Arizona, May 18-23, 2008.

Copper CMP Removal Rate Predictions Using Alumina Agglomerate Size Distributions, R.V.Ihnfeldt and J.B. Talbot, VLSI Multilevel Intercon. Conf.(VMIC), Sept.25-27, 2007, Fremont, CA.

Effects of CMP Slurry Chemistry on Agglomeration of Alumina and Copper Surface Hardness, R.Ihnfeldt and J.B. Talbot, The 210th Meeting of the Electrochemical Society, Cancun, Mexico, Oct.29-Nov. 3, 2006, 3 (41), 21.

Conference Poster Presentations 2nd Place ICPT 2006 Outstanding Poster Award - Copper Removal Rate Predictions Using

Alumina Agglomerate Size Distribution and Copper Nanohardness Measurements, R. Ihnfeldt andJ.B. Talbot. 2006 International Conference on Planarization/CMP Technology, Oct. 12-13, FosterCity, CA.

43

Solutions

a) 1 mM KNO 3

b) 1 mM KNO 3, 0.1 M Glycine

c) 1 mM KNO 3, 0.1 M Glycine, 0.1 wt% H 2O2

d) 1 mM KNO 3, 0.1 M Glycine, 2.0 wt% H 2O2

e) 1 mM KNO 3, 0.1 M Glycine, 0.1 wt% H 2O2, 0.01 wt% BTA, 0.1 mM SDS

f) 1 mM KNO 3, 0.01 M EDTA, 0.1 wt% H 2O2, 0.01 wt% BTA, 0.1 mM SDS

Concentration of additives

6 different solutions studied Glycine and EDTA – complexing agents H2O2 – oxidizing agent BTA- corrosion inhibitor SDS – anionic surfactant KNO3 added to maintain constant ionic strength

44

Indentation Depth vs Load BeforeChemical Exposure

Indentation depth versus load Indentation depth increases as load increases Film is spatially uniform across the surface

0

50

100

150

200

250

0 500 1000 1500 2000 2500 3000

Load (µN)

Ind

en

tati

on

De

pth

(n

m)

fd

45

Indentation depth does not always increase as the load increasessurface may be nonuniform with different types of films (oxides, hydroxides,etc.)

Non-uniform Surface

0

20

40

60

80

100

120

140

0 200 400 600 800 1000

Load (µN)

Ind

en

tati

on

Dep

th (

nm

)

fd

pH 2.9

pH 8.3

pH 11.7

46

0

100

200

300

400

0 2 4 6 8 10

HW (GPa)

MR

R (

nm

/min

)

pH 8.3

Meas.HW

MRR

Prediction

Exp. MRR +/-14

HN

Model Predictions