Feature-level Compensation & Control

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Chemical Mechanical Planarization

InvestigatorsFiona M. Doyle, Materials Science and Engineering1

David A. Dornfeld, Mechanical Engineering1

Jan B. Talbot, Chemical Engineering2

University of California1) Berkeley, Los Angeles, Santa Barbara, 2) San Diego

Workshop11/19/2003

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Outline

• Objective of FLCC effort in CMP• Year 1 milestones• Review of research (Details of these and other key

areas in posters!)– Mechanical phenomena– Interfacial and colloidal phenomena– Chemical phenomena

• Modeling efforts• Future milestones

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Students Involved in CMP EffortMechanical Aspects

• Andrew Chang (RAPT Technologies)

• Yongsik Moon (Applied Materials)

• Jianfeng Luo (Cypress Semiconductor)

• Inkil Edward Hwang

• Sunghoon Lee

• Jihong Choi (NSF)

Chemical Aspects• Serdar Aksu (Suleyman

Demiril University, Turkey)• Ling Wang

Interfacial and Colloid Aspects• Tanuja Gopal

Other Aspects• Runzi Chang (Applied

Materials)

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Feedback from April ‘03 review

• CMP (4/4/5). This program keeps improving and it now has “jelled” nicely. We have clearly achieved critical mass in our team.

• Strengths: The program has found direction and the theoretical work is excellent while the experimental work is very good. The smart pad work must continue. This is a very strong program compared to other academic programs. They were pleasantly surprised at the quality and depth in this work, and they noted that there we are attracting excellent students.

• Opportunities for Improvement: We must increase the emphasis on chemistry. We need to work more on the fundamental basis. We need to test of basic assumptions of the model.

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FLCC CMP Program Goals

• We have pursued solutions by focusing on:– fundamental understanding, specially in chemistry– modeling variability mechanisms– sensing variability causes during production

• We are now focusing on controlling and improving variability.

• Our context is lithography, plasma, CMP, diffusion, and the way these steps interact with each other.

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Year 1 Milestones• Preparation of idealized two phase surfaces, characterization of differential etching behaviors (Milestone 6)Determine how the presence of additional phases modifies the chemical behavior of solids in the presence of CMP slurries and their constituents.

• Wetting studies on two phase or multiphase surfaces (Milestone 7)Study the effects of wetting behavior of metals, low-k dielectrics and other phases on their polishing behavior. Explore modification of the wetting behavior through optimized use of surfactants and other solvents.

• Develop basic understanding of agglomeration/dispersion effectson CMP (Milestone 8)Conduct experimental analysis of slurry particle size characteristics. Study influence of chemistry on particle behavior.

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Year 1 Milestones

• Develop SMART pad design criteria (Milestone 9)Create an analytical representation of mechanical properties of pad behavior and surface features for estimating performance for standard feature set from the cooperative photomask activity.

• CMP linked model development (Milestone 10)Develop model module linkages to adjacent processes and validatewith specific test plans with other FLCC groups using the Centura machine as a basis for evaluation of one test process.

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Aim

• Insure uniform, global planarization with no defects by means of optimized process recipes and consumables

• Idealized single-phase CMP processes are now well understood in terms of the fundamental physical-chemical phenomena controlling material removal

• The challenge is to extend this to heterogeneous structures that are encountered when processing product wafers with device features

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Approach

• Develop integrated feature-level process models, encompassing upstream and downstream processes

• These models will drive process optimization and the development of novel consumables to minimize feature-level defects

• We will require the capability of faithfully predicting defects and non-idealities at feature boundaries.

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Chemical Mechanical Planarization

Mechanical Phenomena

Chemical Phenomena

Interfacial and Colloid

Phenomena

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w p :pad rotation

tablepad

slurry feedconditioner

head

w w : wafer rotationOscillationF : down force

Backingfilm

Retainerring

Wafer

Wafer Carrier

Pad

Pore Wall

Abrasive particle

CMP Process Schematic

Electro plated diamond conditioner Typical pad

Wall

Pad

Pore

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♦Via formation(Metal CMP)

♦Shallow Trench Isolation(STI CMP)Multilevel Metalization

♦Interconnection(ILD CMP)

Applications of CMP – ILD, Metal and STI CMP

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Modeling progress to date and planned

• Identify key influences of chemical and mechanical activity

• Experimental analysis of influences in parallel with model formulation for “module” development

• Identify of “coupling” elements of mechanical and chemical activity

• Build “coupling” elements into integrated model• Model verification by simulation and test• Model-based consumable design/design tools • Strategies for model-based process optimization• Suitability for industrial application/evaluation

mech

0 10

chem

done planned

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CMP Research in FLCC

VALIDATION- FLCC testing- published data- partner testing- other (3rd party)

VALIDATION- FLCC testing- published data- partner testing- other (3rd party)

SOFTWAREPACKAGING

SOFTWAREPACKAGING

PROCESS MODELING- parameters- pad- abrasive- chemistry- materials

PROCESS MODELING- parameters- pad- abrasive- chemistry- materials

PROCESSAPPLICATIONS

- design- optimization- evaluation

PROCESSAPPLICATIONS

- design- optimization- evaluation

TOOL &CONSUMABLES

- pad design- abrasive design- machine design

TOOL &CONSUMABLES

- pad design- abrasive design- machine design

DEVICE DESIGN- lithography- layout- materials

DEVICE DESIGN- lithography- layout- materials

METROLOGY- scatterometry- mask & e-test- AE endpoint

METROLOGY- scatterometry- mask & e-test- AE endpoint

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Process Modeling “Roadmap”

FUNDAMENTALSchemical effectsmechanical effectsabrasivechemistrymaterials effects

FUNDAMENTALSchemical effectsmechanical effectsabrasivechemistrymaterials effects

Particle-Scale Material Removal Model

Wafer-Scale Pressure and Velocity Distribution

Die-Scale Pressure Distribution Model

Consumable Parameters(abrasive/chem)

Layout Density

Macroscopic Contact Chem/Mechanics Model (Weight Function)

Pattern Density Window and Effective Pattern Density

Material Removal Rate

Surface Quality

Die-Scale Topography (vertical & lateral directions)

Upper Stream Wafer-Scale Topography

Wafer-Scale Topography Upper Stream

Die-Scale Topography

CMP Tool Configurations

Pattern Density Effect

Dishing Erosion

Dummy FillingCircuit PerformanceECAD

Focus areas

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Interfacial and colloidal phenomenaMass 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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Electrical Double Layer of Abrasive Particles

+ +

++

+

+ + +++

+

++

++

+

+

+

+

++

a

+

+

+

Distance

Pote

ntia

l

ζ

1/κ

Diffuse Layer

Shear Plane

Particle Surface

•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 Waalsattractive forces•If potentials are near zero, abrasive particles may agglomerate

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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 KIO3slurries (Stein et al., J. Electrochem. Soc. 1999) Po

lishi

ng r

ate

(Α/m

in)

Colloid oxide

Gla

ss p

olis

hing

rat

e (µ

m/m

in)

Oxide Isoelectric point

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EKC Tech alumina slurry

-35

-25

-15

-5

5

15

25

35

45

55

65

3 4 5 6 7 8 9 10 11

pH

Zet

a Po

tent

ial (

mV

)

0

500

1000

1500

2000

2500

3000

3500

Eff

ectiv

e Pa

rtic

le S

ize

(nm

)

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Chemical phenomenaChemistry of Glycine-Water System

pKa1=2.350 pKa2=9.778+H3NCH2COOH ↔ +H3NCH2COO- ↔ H2NCH2COO-

Cation: H2L+ Zwitterion: HL Anion: L-

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12 14 16pH

E, V

vs.

SHE

Cu2+

CuL2CuL+

CuO

22-

CuO

Cu2OCu

Potential-pH diagram, with {CuT} = 10-5, {LT} = 10-2

Cu(II) glycinate complexes•Cu(H3NCH2COO)2+ : CuHL2+

•Cu(H2NCH2COO)+ : CuL+

•Cu(H2NCH2COO)2 : CuL2

Cu (I) glycinate complexes•Cu(H2NCH2COO)-

2 : CuL2-

H-O-H

H-O-H

N-H2H2-C

OO=C

C-H2H2-N

C=OOCu2+

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Cu-Glycine

i, A/m 2

10 -4 10 -3 10 -2 10 -1 100 101 102 103

E mV vs. SH

E

-800

-600

-400

-200

0

200

400

600

800

1000

1200

1400

1600

1800

pH 4pH 9pH 12

0

20

40

60

80

100

120

140

160

0 1 2 3 4 5 6H2O2 , wt%

Rem

oval

Rat

e, n

m/m

in Dissolution Rate

Polish Rate

Using electrochemical control of oxidation, see passivation only at high pH, where a solid oxide forms

Using hydrogen peroxide as a chemical oxidant, see passivation at pH 4 and 9, where no solid oxide expected

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H2O2 concentration, wt%0 1 2 3 4 5 6

0

20

40

60

80

100

120

Dissolved C

op

0

500

1000

1500

2000

250050 minutes40 minutes30 minutes20 minutes10 minutes

Copper concentration, mg/l, and dissolved copper, nm, in unbuffered aqueous glycine (pH 4.5)

sampling time, minutes0 10 20 30 40 50 60

i

0

20

40

60

80

100

120

Dissolved C

o

0

500

1000

1500

2000

25000.5 wt% H2O20.75 wt% H2O21 wt% H2O23 wt% H2O25 wt%H2O2

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Model Implementation - Pad Design

SMART pad surface

Polymer pad surface

200um

Polyethylene pad surface

Pad

Wafer

PoreWall

Abrasives

Wafer

Pad

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Orientation Effects in Shape Evolution in CMPExperimental Result – 2D features

near

far

trailingleading

0 20 40 60 80 100 1200

1000

2000

3000

4000

step

hei

ght(a

ngst

rom

s)

line length(µm) trailingleading

0 20 40 60 80 100 1200

1000

2000

3000

4000

step

hei

ght(a

ngst

rom

s)

line length(µm)near far

AB

C D

0 50 100 150 200 250 300 350 4000

100

200

300

400

500

600 A B C D

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Effective pattern density

a=320um

a=640um

a=1280um

< Effective density map >

< Test pattern >

< Post CMP film thickness prediction at die-scale >

Modeling of pattern density effects in CMP

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Year 2 Milestones• Mechanisms for coupling of chemical and mechanical phenomena in CMP (Milestone 22)

Identify means for predicting CMP a priori, and optimizing conditions. Identify basis for modeling the influence of different phases.

• Further develop basic understanding of agglomeration/dispersioneffects (Milestone 23)

Relate colloidal chemistry to surface charge and particle size distribution changes.

• Develop SMART prototyping methodology (Milestone 24)Determine best manufacturing processes for prototyping pads for use in validation testing based on common photomask design.

• Integrate SMART pad design criteria into comprehensive model (Milestone 25)Develop capability in integrated model for determining process-based or device design-based criteria for SMART-pad surface and property specifications.