Feature-level Compensation & Control

WorkshopSeptember 13, 2006

A UC Discovery Project

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Chemical Mechanical Planarization - Faculty Team

Mechanical Phenomena

Chemical Phenomena

Interfacial and Colloid

PhenomenaJan B. TalbotChemical EngineeringUCSB

David A. DornfeldMechanical EngineeringUCB

Fiona M. DoyleMaterials Science and EngineeringUCB

Kyriakos KomvopoulosMechanical Engineering

UCB

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Chemical Mechanical Planarization - Student Team

Mechanical Phenomena

Chemical Phenomena

Interfacial and Colloid

Phenomena Robin IhnfeldtChem Eng UCSD

Shantanu TripathiME/MSE UCB

Jihong Choi ME-UCB

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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. Our work in the past 3 years focused on building a link between CMP process induced variations and pattern transfer feature size including roughness contributions and non-linear resistance changes in interconnects, for two examples. We specifically address “coupling” issues, those that result from the interplay of chemical and mechanical effects during planarization, for example, material removal due to electrochemical effects, wetability and corrosion effects in polishing, and novel consumable design for optimized performance. We develop integrated feature-level process models which drive process optimization to minimize feature, chip and wafer-level defects for both CMP and lapping processes.

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- Status• Wetting studies on two phase or multiphase surfaces (CMP Y3.1) Completed

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. Continues

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

• Coupling mechanisms of chemical-mechanical phenomena in CMP (CMP Y3.5) ContinuesDevelop 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 (Milestone continued from Y2, CMP Y3.6) ContinuesContinue 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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Milestones for Year 4• Develop basic understanding of agglomeration/dispersion effects on CMP (Milestone

continued from Year 3) Experimental analysis of slurry particle size characteristics. Slurry Colloidal Behavior and Copper Nanohardness in Cu CMP. Berkeley/UCSD effort.

• CMP process model development (Milestone continued from Year 3) Model for characterizing chip scale pattern dependencies with respect to within die and within wafer nonuniformity; validate with specific test patterns; build link between CMP process induced variations and pattern transfer feature size including roughness contributions and non-linear resistance changes in interconnects.

• Validation of comprehensive CMP model and CAD and process linkages for design/process optimization Conduct extensive testing with other FLCC teams and industry collaborators of model with special emphasis on the integration elements using the Centura tool as one of the process elements. Verification of formulation as “CMP compatible chip design” software.

• Mechanisms for coupling of chemical and mechanical phenomena in CMP (Milestone continued from Y3) Chemical effect 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 Y3) Development of process model and validation with attention to low down force applications/non-Prestonian material removal as well as subsurface damage effects for electrolytic polishing (E-CMP).

• Lapping nanomechanics and optimization of planarization process parameters (New Milestone for Y4) Elucidate the process of material removal at submicron scales and through modeling and experimentation provide an optimization scheme for key process parameters (e.g. lapping plate material, diamond particle size and spatial distribution, surface geometry, charge density, mean contact pressure, lapping speed, fluid/particle (slurry) supply rate, coefficient of friction, relative motion of lapping plate and die, etc.).

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Key Results for Year 3• Developed capability in integrated model for determining and assessing • pattern sensitivity• MRR predictions are improved using particle size distributions in presence

of copper and measured surface hardness• 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• Use of mechanistic understanding to set up integrated chemical-mechanical • model for material removal• Development of composite samples for measuring galvanic interactions• AFM work to image copper surfaces in different chemical environments• Exploratory work on use of AFM to correlate transient electrochemical currents to

surface damage induced by AFM tip – input for integrated model

Posters will provide details!

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Today’s Presentation- see the posters for details -Pattern Dependent Chip Scale Modeling of Topography Evolution for STI CMP

• 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

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

Slurry Colloidal Behavior and Copper Nanohardness in Cu CMP• Characterize slurry colloidal behavior using zeta potential and particle size

measurements including Effects of common chemical additives and presence of copper• Investigate copper surface hardness and effects of slurry chemistry• Determine the state of the copper (Cu, CuO, Cu2+, etc.) in the slurry and on the wafer

surface• Develop material removal model incorporating colloidal and chemical effects

Lapping Nanomechanics and Optimization of Planarization Process• Analyze the process of material removal at submicron scales through modeling and

experimentation.• Establish an optimization scheme for key process parameters

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Pattern Dependent Chip Scale Modeling of Topography Evolution for STI CMP

liner oxide

pad oxide

Nitride

Si

Etch trenches

HDP-CVD

CMP

active areas Offset between isolation oxide and active area

In STI process, the offset between isolation oxide and active device area should be as small as possible for more advanced devices.

Thinner nitride layer results in smaller offset. However, thinner nitride means bigger possibility of local damage on device grade silicon surface during CMP process.

CMP uniformity window across a chip directly affects the possible nitride thickness window. Hence it is important to know the CMP process window for a given chip layout.

A model can be used not only for direct STI, but also for optimization of reverse tone etch back or dummy fill design.

Motivation

Nitride Strip

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High Selectivity STI CMP

• Ceria powder plus surfactant: oxide and nitride selectivity plus reduced polishing in low areas

• Non-Prestonian behavior

• Good planarization efficiency

• Less pattern dependency

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Experimental

• Trench depth : ~ 4300 Å

• Trench width : 0.1µm ~ 9µm

• Trench aspect ratio : up to 4.3

• Pattern density : 0.1 ~ 0.8

• HDP-CVD Oxide Deposition

• Large features (~65µm) for optical measurement (spectrophotometer)

- Test Pattern

- Oxide Deposition : HDP-CVD

- CMP : 200mm tool, High selectivity (~100:1) slurry

- Metrology : 3 dies per wafer for comparison, spectrophotometer at large features over a die, stylus profiling over die for die scale profile

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Die 1

Die 2

Die 3

Within die variation : ~100 Å

After 40 seconds of CMPHDPCVD Oxide

Within die variation : ~1800 Å

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HDP Cones Before and After CMP (AFM)

fine patternscoarse patternsbefore before40sec CMP 40sec CMP

0.112µm/1µm 0.112µm/0.448µm

0.112µm/0.1681µm0.112µm/0.261µm

After significant amount of CMP, HDP cones still remained

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Initial Oxide Topography from HDP-CVDStep height as high as ~3000Å

Across a die

It is critical to evaluate oxide profile over a die for CMP erosion mapping

Real pattern density

1 cell 3

cell 2

cell 1

a

LPD1LPD2

maximum oxide thickness

cell 2 cell 1

cell 3

b c dz

Cell 1 Cell 2 Cell 3

Topography map

z1 z2 z3z

LS >= LSmax

RPD0 < 1LS = 0

RPD0 = 1

LS < LSmax

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Chip Scale STI Process Model

Pattern density Line widthLine space

Chip Layout

HDP-CVD Deposition Model

CMP model

CMP Input Thickness

Evolution Nitride thinning

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Shape Evolution Example

Over polishing

α : β = 0 :1α : β = 1 :100 α : β = 1 :1000α : β = 1 :10

Shape evolution with different model parameters

Edge factor evolution of the test pattern

As polishing goes on, sharp features become smooth, edge factor decreases.

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Future Goals

• Validation of comprehensive CMP model and CAD and process linkages for design/process optimizationConduct extensive testing with other FLCC teams and industry collaborators of model with special emphasis on the integration elements using the Centura tool as one of the process elements.

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Slurry Colloidal Behavior and Copper Nanohardness in Cu CMPParticle Size Distribution

Alumina dispersion in 1mM KNO3 solution with (♦) glycine + other additives at pH 4.0 or (■) no additives at pH 4.5.

• Glycine + other additives – one narrow distribution• No additives – Bimodal and wider particle size distributions

0

5

10

15

20

25

30

35

0 200 400 600 800Particle Diameter (nm)

% in

sol

utio

n

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Copper-Alumina-Water SystemPotential-pH for Copper-water System[Cu]=10-4M at 250C and 1atm (M. Pourbaix 1957)

• Copper in solution is consistent with Pourbaix diagram

• Unclear if copper surface correlates with Pourbaix diagram

Average agglomerate size of bimodal distributions and preliminary surface hardness of Cu on Ta/Si (100uN applied load)

Small Large Small LargeAverage (nm) Average (nm) Average (nm) Average (nm)

2 Cu, Cu+, Cu2+ 170 5000 160 810 2.97 Cu, Cu2O, CuO 580 3300 1700 9400 1.210 Cu, Cu2O, CuO 150 720 300 160012 Cu, Cu2O, CuO 1.2

Nanohardness (GPa)

Without Copper With CopperpH Possible state of

the Copper

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Model MRR Predictions

0

50

100

150

200

250

0 2 4 6 8 10 12pH

MR

R (n

m/m

in)

CMP conditions-platen/head 30/30rpm, 1psi down pressure, 150ml/min slurry flow rate.

• 0.1M Glycine - small surface hardness value causes overprediction of MRR at low pH values

• 0.1M Glycine + 0.1wt% H2O2 - MRR predictions agree well with experimental MRR

0.1M Glycine 0.1M Glycine and 0.1wt% H2O2

Comparison of experimental (●) and predictions (♦) of MRR versus pH for Cabot alumina slurry

0

100

200

300

400

500

0 2 4 6 8 10 12pH

MR

R (n

m/m

in)

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Copper Etch Rates

-25

0

25

50

75

100

0 2 4 6 8 10 12 14pH

etch

rate

(nm

/min

)No additives0.1M Glycine0.1M Glycine + 2.0w t% H2O2

Comparison of copper etch rates after a 10min exposure to various slurry chemistries at different pH values (no alumina in slurry)

• No additives - etch rate is highest at low pH values, consistent with the potential-pH diagram

• Glycine - slightly higher etch rate at pH 12 due to formation of highly soluble copper-glycinate complex

• Glycine + H2O2 - significant increase in etch rate at pH 7 due to H2O2 increasing the formation of the copper-glycinate complex

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Conclusions• pH of slurry affects both slurry colloidal behavior and copper surface

hardness• Addition of chemical additives affects state of copper in slurry, which

will affect alumina particle dispersion• Small changes in copper surface hardness (with addition of chemical

additives) will significantly affect MRR• MRR predictions are improved using particle size distributions in

presence of copper and measured surface hardness

Future Goals• Continue to investigate effects of additives on copper surface hardness;

measure etch rates• Study colloidal properties of nanoparticles in slurries• Determine state of copper (Cu, CuO, etc.) in solution and on wafer

surface• Further develop Luo and Dornfeld model; incorporate etch rates

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Integrated Tribo-Chemical Modeling of Copper CMP Motivation

• Design & simulation of CMP process• Lower pressure copper CMP for porous low-K.• With technology node moving 65nm & below impact of

defects will be more severe.• CMP process underutilized – possibility of getting higher

performance (higher removal rate, higher planarity, lesser defects, lesser pressure) at lower costs.

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The Problem

Integrated Cu CMP Model

ColloidAgglomeration

OxidizerOxidizerInhibitorInhibitor

ComplexingComplexing agentagentSurface FilmSurface Film

PadPadPressure/ VelocityPressure/ Velocity

AbrasiveAbrasive

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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The Problem & the Solution

• Synergism between frequent mechanical interactions and action of chemical slurry make Copper CMP process: electrochemically TRANSIENT; but to date

• NO study of transient behavior, focus on steady state.• NO mechanistic models of tribo-chemical synergism.

We must study:We must study:•• Transient passivation behavior of copper: Transient passivation behavior of copper: first few moments of copper passivation.first few moments of copper passivation.•• AbrasiveAbrasive--copper interactions: frequency, copper interactions: frequency, duration and force.duration and force.•• Properties of passive film: mechanical, Properties of passive film: mechanical, electrical, chemicalelectrical, chemical

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Mechanistic Model

iactive

ipassive

Oxi

datio

n ra

te

i0

Interval between two abrasive-

copper contacts (τ): Stochastic

Abrasive-copper interaction: Stochastic

Bare copper

Thick passive film

Stochastic variation in i0

t0 Time (t’)

Copper oxidized

Copper: transient passivation behavior i(t’)

Copper oxidation influenced by

abrasive interactions

More frequent interactions

∫ +=• τ

τρ 00 )( dttti

nFMV Cu

CWRemoval Rate,

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Transient Passivation Behavior

-2

-3

-4

-5

-6

Region I II III IV V

-2 -1 0 1 2

Log

i (A

/cm

2 )

Log t (s)

Log

i

Log t

• No direct study on Copper CMP slurry constituents.• Observed behavior for other metal-chemicals combination: log-log (oxidation rate – time) [1]• Complex behavior observed for Cu-AHT (inhibitor) behavior [2]• Wide variation observed in decay kinetics for different systems: milliseconds to minutes.

[1]

[2]

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Mechanical Interactions

Differing wear

distance

Relative motion

Contact area in

plan view

Wear distance

Pad asperity

Abrasive

Copper

Passive filmAbrasive

Duration between contact events.

• Passive film thickness ↔corresponding oxidation rate

• Duration/Force of contact ↔ Thickness of Passive film removed

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Interaction Frequency & Duration

Elmufdi & Muldowney [3]

• Interval between asperity-copper contact ≈ 1ms• Duration of contact ≈ 10µs • Needed: study of abrasive-copper interactions

C-RICM image of real contact area• Contact ratio response to

pressure for 3 different pads

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Tribological Properties of Passive FilmsFi

lm th

ickn

ess

(nm

)

Wear Distance (µm)

Film

thic

knes

s (n

m)

Wear Distance (µm)Fi

lm th

ickn

ess

(nm

)Wear Distance (µm)

Wear distance (μm)

Conditions (a)

Conditions (b)

Linear wear till passive film removed

Bi-layer passive film ‘Loading’ of abrasive

Passive film properties varying with slurry chemistry

• Wear of passive film depends on mechanical properties of passive film and abrasive particle, and force of contact.• Mechanical properties of passive film depend on chemical conditions (inhibitor, oxidation potential)

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AFM study of Passive Films

Z-range: 40nm Z-range: 65nm Z-range: 70nm

Copper in air Copper in 0.05M Glycine, 2.5%H2O2, pH4

Copper/Passive film in air after being exposed to slurry

• Study of mechanical properties and surface structure.• Response to scratching using AFM tip.• Correlation of behavior to EQCM study (Ling Wang & Fiona Doyle [4])• Future: AFM scratching under electrochemical control to develop input for model

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Other Considerations• Particle Agglomeration• Surfactant behavior• Fluid dynamics• Mass transport

Future Goals• Continued development of integrated copper CMP model

development, accounting for local variations due to wafer features.

• Integrated model for ECMP• Experiments to study transient passivation behavior• Investigate asperity-wafer interaction.

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Lapping Nanomechanics and Optimization of Planarization Process

Objectives● Analyze the process of material removal at submicron scales through modeling and experimentation.● Establish an optimization scheme for key process parameters (i.e., lapping plate material, diamond particle size and spatial distribution, surface geometry, charge density, mean contact pressure, lapping speed, fluid/particle (slurry) supply rate, coefficient of friction (lube effect), and relative movement of lapping plate vs. die movement).

Methods● Modeling (Analytical and FEM plasticity models).● Nanoscratching Experiments (particle shape effect on friction and material removal process).● Lapping Experiments (effect of above process parameters on nanoscale material removal rate).

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Basic AspectsBasic Aspects

• Relative movement between a soft surface with embedded hard particles and the work surface.

•• FourFour--component system: component system: Work material + fluid + particles + lapping medium

•• Dominant MechanismsDominant Mechanisms:deformation, 2-/3-body abrasion, adhesion/smearing, fracture/fatigue

• Both bulk modulus and hardness of lapping surface affect penetration of the abrasive particles and texturing.

• Mechanical wear can change the lapping surface and, hence, contact mechanics, fluid transport, temperature rise, etc.

• Shape, size and concentration of particles control lapping uniformity and range of dominant wavelengths on lapped/polished surface that determine surface roughness.

• A fluid maintains low friction during lapping to prevent frictional heating and excessive tensile stresses and enable fine-scale material removal.

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Proposed Research TasksProposed Research Tasks• Planarization mechanics of lapping plate.• Analysis of particle charging (roller motion, slurry feed, etc.).• Texturing of lapping plate for enhanced slurry movement during charging.• Scale-dependent surface characterization of charged lapping plate.• Optimization of plate surface for lapping roughness of <1 nm.• Role of ethylene-glycol fluid in lapping lubrication.• Modeling of nanoscale material removal process for hard materials.• Study of residual stresses in lapped ceramic surface vs. lapping process parameters

(i.e., particle size/distribution, pressure, speed, fluid viscosity/chemical behavior).• Analyze removal rate vs. particle size and distribution, mean contact pressure, and

lapping speed (modeling at the right scale!).• Causes of metal smearing from soft tin layer and magnetic read element.• Tin layer softening due to frictional heating under high-pressure/speed lapping

and/or fluid starved interface.• Examine mechanisms of particle dislodging from plastically deformed (softened)

tin layer.• Explore surface treatment/modification methods of lapping plate for enhanced

nanoscale lapping/polishing.