Feature-level Compensation & Control

FLCCCMPApril 5, 2006

A UC Discovery Project

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Chemical Mechanical Planarization - Faculty Team

Mechanical Phenomena

Chemical Phenomena

Interfacial and Colloid Phenomena

Jan B. TalbotChemical EngineeringUCSB

David A. Dornfeld

Mechanical Engineering

UCB

Fiona M. DoyleMaterials Science and EngineeringUCB

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Chemical Mechanical Planarization - Student Team

Mechanical Phenomena

Chemical Phenomena

Interfacial and Colloid Phenomena Robin Ihnfeldt

Chem Eng UCSB

Sunghoon Lee ME-UCB

Alex DeFeo ME-UCB

Shantanu TripathiME/MSE UCB

Jihong Choi ME-UCB

Diego Arbelaez ME-UCBSummer ‘06

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CMP ResearchDescription: The major objective of this work continues to be to establish an

effective linkage between capable process models for CMP and its consumables to be applied to process recipe generation and process optimization and linked to device design and other critical processes surrounding CMP. Specific issues include dishing, erosion and overpolishing in metal polishing, which have an impact on circuit performance — all pattern dependent effects at the chip level — wetability effects in polishing, and novel consumable design (pads and abrasives) for optimized performance. We develop integrated feature-level process models which drive process optimization to minimize feature, chip and wafer-level defects.

Goals: The final goals remain reliable, verifiable process control in the face of decreasing feature sizes, more complex patterns and more challenging materials, including heterogeneous structures and process models linked to CAD tools for realizing “CMP compatible chip design.”

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Current Milestones• Wetting studies on two phase or multiphase surfaces (CMP Y3.1)

Studies on modification of the wetting behavior through optimized use of surfactants and other solvents. • Further development of basic understanding of agglomeration/dispersion effects (CMP Y3.2)

Experimental analysis of slurry particle size characteristics. Study influence of chemistry on particle behavior for characterizing particle size effects.

• SMART pad design scaleup and validation (Y3.3)Scaleup (larger size) and validation of SMART pad design for more commercially viable experimental conditions for enhanced planarization with reduced overpolishing (ILD) and dishing and erosion (metal).

• CMP process model development (Y3.4)Continue development of model for characterizing chip scale pattern dependencies for process optimization with respect to within die and within wafer nonuniformity; validate with specific tests patterns; formulation as “CMP compatible chip design” software.

• Mechanisms for coupling of chemical and mechanical phenomena in CMP (CMP Y3.5)Develop chemical models to characterize the material removal due to chemical/electrochemical effects, and integrate the chemical models into the comprehensive CMP model to account for mechanical, interfacial and chemical phenomena.

• Basic material removal model development (Milestone continued from Y2, CMP Y3.6)Continue development of process model and validation with attention to low down force applications/non-Prestonian material removal as well as subsurface damage effects; applicable to electrolytic polishing(E-CMP) as well.

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Student Research and Milestones• Wetting studies on two phase or

multiphase surfaces (CMP Y3.1)

• Further development of basic understanding of agglomeration/dispersion effects (CMP Y3.2)

• SMART pad design scaleup and validation (Y3.3)

• CMP process model development (Y3.4)

• Mechanisms for coupling of chemical and mechanical phenomena in CMP (CMP Y3.5)

• Basic material removal model development (Milestone continued from Y2, CMP Y3.6)

Jihong Choi

Sunghoon Lee

Alex DeFeo

Robin Ihnfeldt

Shantanu Tripathi

Shantanu Tripathi

Diego Arbelaez Shantanu Tripathi

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Today’s Presentation- see the posters for details -SMART Pad (Pad Micro Feature Design for CMP with Sensor Integration)• Pad prototypes• Validation results• Sensors for CMP/ Sensor requirements (proposed research)

Integrated Tribo-Chemical CMP Model • Mechanisms for coupling of chemical and mechanical phenomena in CMP • Basic material removal model development

Colloidal behavior of slurry particles• agglomeration/dispersion effects on CMP• Incorporate colloidal and chemical effects into basic material removal model

Chip Scale Modeling of High Selectivity STI CMP, Linking HDP-CVD Oxide Topography

• Application of chip scale model to high selectivity STI process• Modeling HDP-CVD oxide topography for CMP model input• Model calibration by comparison with test pattern wafer CMP results• Model application and verification for production pattern wafer

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Colloidal behavior of slurry particles–Develop basic understanding of agglomeration/dispersion effects on CMP

•Characterize slurry colloidal behavior using zeta potential and particle size measurements

•Understand effects of common chemical additives and presence of copper nanoparticles

•Study particle size distribution – Gaussian or bimodal?

•Investigate the effects of slurry chemistry on copper surface hardness

•Determine the state of the Cu (Cu, CuO, Cu2+, etc.) both in the slurry and on the wafer surface

–Basic material removal model development

• Incorporate colloidal and chemical effects into the Luo-Dornfeld model (IEEE Trans. on Semi. Manuf. 2001) thru:

– particle size and distributions

– copper surface hardness

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Zeta Potential and Particle Sizeb)

0

500

1000

1500

2000

0 2 4 6 8 10 12pH

Pa

rtic

le S

ize

(n

m)

Without Copper (small)Without Copper (large)With Copper (small)With Copper (large)

a) Zeta potential and b) particle size versus pH for alumina in a 1 mM KNO3 solution with and without 0.12 mM copper (error bars indicate standard deviation of particle size distribution).

• IEP = ~6.5 with and without copper

• Agglomerates larger with copper at pH >7

a)

-80

-40

0

40

80

2 4 6 8 10 12

pH

Ze

ta P

ote

nti

al (

mV

)

Without CuWith Cu

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Potential-pH Diagram for Cu-H2O

-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 16

pH

E,

V v

s. S

HE

Cu2+

CuO

Cu

Cu2OCuO2

2-

Cu+

pH Color Sustance

1.5 mostly dissolved, very light red Cu+

2.4 light red, copper Cu+, Cu

3.2 light red, copper Cu+, Cu

4.6 redish, brown Cu, Cu2O

5.8 grey, brown Cu, Cu2O

6.0 not dissolved, dark grey-brown Cu, Cu2O

9.2 not dissolved, dark grey CuO11.2 grey CuO12.4 blue, green CuO2

2-

Color experiment for 4.8mM Cu nanoparticles in water at various pH values.

Potential-pH diagram for the copper-water system with [Cu]=10-4 M at 25°C and 1 atm (M. Pourbaix 1957)

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Nanohardness Measurements of 1m Cu on 15 nm Ta on silicon

after 10 min exposure to 5 wt% H2O2 slurry solution

*A. Jindal and S. V. Babu, J. Electrochemical Society, 151 10 (2004).

Nanomechanical Test Instrument from Hysitron, Inc. 0

2

4

6

8

10

0 50 100 150 200 250Contact Depth (nm)

Na

no

ha

rdn

es

s (

GP

a)

Bulk Cu*pH2*pH7*pH12*pH2pH7pH12Ta

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Model MRR Predictions

0

1000

2000

3000

4000

5000

6000

2 4 6 8 10 12

pH

MR

R (

nm

/min

)

Model Predictions

Experimental MRR

• MRR predictions improved at low pH values.

• MRR predictions are highly sensitive to the standard deviation of the particle size.

alumina slurry containing 0.1M Glycine and 2.0 wt% H2O2

Lou and Dornfeld model Modified Lou and Dornfeld model (with full particle size distribution in presence of copper particles and measured hardness)

0

100

200

300

400

2 4 6 8 10 12pH

MR

R (

nm

/min

)

Model Predictions

Experimental MRR

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ConclusionsChemical Additives • pH has the greatest effect on colloidal behavior• Glycine acts as a stabilizing agent for alumina• Addition of both glycine and H2O2 increases MRR• Agglomerate size distribution becomes broader as zeta

potential becomes smaller and particles agglomeratePresence of Cu nano-particles• Larger alumina agglomerates at pH>7• Magnitude of zeta potential is slightly lowerLuo and Dornfeld model• MRR predictions improved using distributions in

presence of copper and measured surface hardness

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Future Goals-Further develop understanding of chemical/colloidal effects

•Continue to investigate the effects of additives on the copper surface hardness

•Study colloidal properties of nanoparticles in the slurries

•Determine the state of the copper (Cu, CuO, etc.) in solution as well as on the wafer surface

-Basic material removal rate model development

•Investigate the model sensitivity to changes in particle size and standard deviation

•Use CMP data to develop chemical component of FLCC developed models (Luo, Choi)

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Integrated Tribo-Chemical CMP Model

• Design & simulation of CMP process• Lower pressure copper CMP for porous low-K.• With technology node moving 65nm & below impact of defects much greater.• CMP process underutilized – possibility of getting higher performance (higher removal rate, higher planarity, lesser defects, lesser pressure) at lower costs.

Need for fundamental understanding, need for Need for fundamental understanding, need for bringing together different segments of CMP bringing together different segments of CMP

knowledgeknowledge

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2006 Main Objective• Mechanisms for coupling of chemical and

mechanical phenomena in CMP (CMP Y3.5): – Develop chemical models to characterize the material

removal due to chemical/electrochemical effects, and integrate the chemical models into the comprehensive CMP model to account for mechanical, interfacial and chemical phenomena.

• Basic material removal model development (Milestone continued from Y2 (CMP Y3.6))– Development of process model and validation with

attention to low down-force applications/non-Prestonian material removal, as well as subsurface damage effects; applicable to electrolytic polishing (E-CMP) as well.

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The Problem

Integrated Cu CMP Model

ColloidAgglomeration

OxidizerInhibitor

Complexing agentSurface Film

PadPressure/ Velocity

Abrasive

Needed: an Needed: an Integrated Copper Integrated Copper CMP ModelCMP Model

Fluid MechanicsMass Transfer

Needed: understanding of the synergy between different components

Interactions:•Asperity-copper•Abrasive-copper

Fluid pressureContact pressure

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Challenges• Chemical behavior complex – highly sensitive to chemistry, which can change during CMP• Mechanical material removal more pronounced• Evidence of synergy between chemical and mechanical effects

Knowledge Gaps

Asperity-copper interaction• Local pressure vs material removal• Threshold pressure and removal saturation • Dissolution vs surface film growth• Interval between consequent asperity-wafer contacts

Abrasive-copper interaction• Soft surface film removal:

•Plowing•Plucking

• Mechanically enhanced chemical reaction

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Dornfeld – Luo Model

Framework connecting input physical parameters with material removal rate

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Tribological Models

idt

• Hydrodynamic Model• No pad-wafer contact• Thin slurry film (h < 20–50 µm)• However, Danyluk et al claim

interfacial fluid pressure is sub-ambient.

Sundararajan, Thakurta, Gill, J. Electrochem. Soc., 146, 761-766 (1999)

Contact Model:–Indentation depth & MRR applied pressure –Real contact area applied pressure

–Properties of surface film (thickness, effective hardness)–Material removal per abrasive–#abrasives per asperity–Overall removal rate

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Electrochemical Behavior of Copper

E. Paul, F. Kaufman, et al. J. Electrochem. Soc., 152, G322-G328 (2005)

Copper CMP model: method of kinetics

Pourbaix diagram:Copper-glycine system

S. Aksu & F. M. Doyle, J. Electrochem. Soc., 148, B51-B57 (2001)

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Passivation in H2O2

Equivalent polarization curves for copper dissolution and polishing in aqueous 10-2M glycine solutions

containing different amounts of H2O2 at pH 4

•S. Aksu & F. M. Doyle, CMP V, Ed. S. Seal, The Electrochemical Society, PV-2002-1 pp. 79-90 (2002)•L. Wang & F. M. Doyle, Mat. Res. Soc. Symp. Proc. Vol. 767, F6.5.1

•Unexpected passivation observed due to presence of H2O2

•Passivation due to catalytic action of Cu(II)-glycine on H2O2 decomposition•Mechanism still not fully understood

Active behavior

Passive behavior

Alternating active/passive behavior + mechanical enhancement

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EQCM of Surface Film

L. Wang & F. M. Doyle, unpublished

•Surface film thickness and composition can be estimated at a given chemistry from charge and mass gain measured with EQCM•Rate of formation of surface film can be determined•Etch rates, polishing rates, and repassivation kinetics.

(W. Lu, J. Zhang, F. Kaufman, & A. C. Hilliera, J. Electrochem. Soc., 152, B17-B22, 2005)

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Removal Rate Model

iactive

ipassive

timeInterval between

two asperity-copper contacts (t0)

net charge transfer = q0

Asperity-copper interaction

Function of pad asperity distribution, velocity, down-force

By Faraday’s Law:(Mass copper removed)/(Area) = MCuq0/(nF)•Can be simplified to a bimodal form

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Other ConsiderationsOther considerations:• Role of Inhibitors (BTA)• Particle Agglomeration• Surfactant behavior• Abrasive “chemical tooth”• Fluid dynamics• Mass transport

More fundamental studies needed for:• Asperity-copper interaction to investigate – threshold pressure required for surface film removal; importance of interval between contacts• Surface film characterization• Verification of mechanically enhanced chemical reaction rates

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Future Goals

• Continued development of integrated copper CMP model development, accounting for local variations due to wafer features.

• Integrated model for ECMP• Experiments to investigate Asperity-wafer

interaction.• Process design for abrasive-less slurries.

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Chip Scale Modeling of High Selectivity STI CMP, Linking HDP-CVD Oxide Topography

Layout pattern density

Real pattern density at time t

Effective real pattern density

Topography at time t

slurry characteristics for oxide

nitride exposure

?slurry characteristics for nitride yes

simulation over ?

no

END

Edge factor

Topography at time t+dt

no Effective local contact pressure

Layout line width

Local removal rate

yes

no

nitride exposure

?HDPCVD model

Layout pattern densityyes

Start

HDPCVD model

Initial topography

EPD + 30sec High Selectivity CMP

Simulation procedureand results

Initial Oxide Topography

Future Goals Model calibration with test

CMP results Model application for different

pattern & verification Linking with other process models Stand-alone software

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CMP Pad Micro Feature Design with Sensor IntegrationGeometry Optimization• Open Cell: slurry overflow and slurry will not fill the interface at the contact level.• Closed cell: restrict slurry flow and pressure differentials will build, causing hydroplaning. • Type C has show the best performance

Open Cell

Closed Cell

A B C D

0.25µm(Cu)/0.25µm (Low-K) pattern

Conventional Type C

Future Goals• Validate lower peak forces

with micro scale sensors• Pad Sensor• GHz range output signals• Wireless realtime monitoring• Fracture Sensor Test Wafer