CMP Modeling as Part of Design for...
Transcript of CMP Modeling as Part of Design for...
CMP Modeling as Part of Design for Manufacturing
David Dornfeld
Will C. Hall Professor of EngineeringLaboratory for Manufacturing and SustainabilityDepartment of Mechanical EngineeringUniversity of California Berkeley CA 94720-1740
http://lmas.berkeley.edu
University of California at Berkeley
© Laboratory for Manufacturing and Sustainability, 2007
Outline
• Modeling objectives and perspective
• CMP process model development
• Short review
• Towards design for manufacturing (DFM)
University of California at Berkeley
© Laboratory for Manufacturing and Sustainability, 2007
Levels of Flexibility ‐ Design to Manufacturing
Level IFeature prediction, control, andoptimization in an iterative designand process planning environment
Design: HighManufacturing: HighFinishing: High
Level IIFeature prediction, control, andoptimization through the selection ofa manufacturing plan in an "over-the-wall" design-to-manufacturingenvironment
Design: LowManufacturing: HighFinishing: High -> low
Level IIIFeature prediction and controlthrough limited adjustments to apre-established manufacturingprocess
Design: LowManufacturing: LimitedFinishing: High -> low
Level IVFeature prediction for finishingprocess planning, finishing tooltrajectories and sensor-feedbackstrategies
Design: LowManufacturing: LowFinishing: High
So f
twar
e d r
i ven
Har
dwa r
e d r
i ven
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Modeling Roadmap for maximum impact
Minimum cost/CoOMaximum productionMaximum flexibilityMaximum qualityMinimum environmental
& social impactBroadest integration
***
Through software
FunctionalModel
Feedback(validation)
Integrationwith CAD
Feedback(validation)
Feedback(validation)
Include “islands of automation” andexisting models)
Include supply chain with
constraints (e.g.“quality gates” )
Prototype based
on model
Feedback(validation)
Extend to “socialimpact” constraints
(green, sustainability,health, safety, etc.)
Feedback(validation)
Feedback(validation)
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Is there need for this?
Design Manf’g
Design Manf’g
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What you see depends on where you are standing!
+DesignManf’g
+ Manf’gDesign
Source: Y. Granik, Mentor Graphics
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What’s your world view?
Design
Process
Process
Process
Design
Design
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Components of Chemical Mechanical Planarization
Mechanical Phenomena
Chemical Phenomena
Interfacial and Colloid
Phenomena
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Scale Issues in CMP
Scale/sizenm µm mm
Material Removal
Mechanical particle forcesParticle enhanced chemistry
ChemicalReactions
ActiveAbrasives
Pores,Walls Grooves
Tool mechanics,Load, Speed
critical features dies
Pad
Mechanism
Layoutwafer
From E. Hwang, 2004
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Bulk Cu CMP Barrier polishing W CMP Oxide CMP Poly-Si CMP
Physical models of material removal mechanism in abrasive scale
Chemical reactions
Bulk Cu slurry Barrier slurry W slurry Oxide slurry Poly-Si slurry
Mechanical material removal mechanism in abrasive scale
Abrasive type, size and concentration
[oxidizer], [complexing agent], [corrosion inhibitor],
pH …
Pad asperity density/shape
Pad mechanical propertiesin abrasive scale
Pad properties in die scale
Slurry supply/ flow patternin wafer scale
Wafer scale pressure NU Models of WIWNU
Models ofWIDNU
Topography
Wafer scale velocity profile
Wafer bending with zone pressures
Better control of WIWNU
Reducing ‘Fang’
Small dishing & erosion
Ultra low-k integration
Smaller WIDNU
Reducing slurry usageUniform pad performance
thru it’s lifetimeLonger pad life time
Reducing scratch defects
Better planarization efficiency
E-CMPPad groove
Pad design
Fabrication
Test
Fabrication technique
Slurry supply/ flow pattern in die scale
Cu CMP
modeldesign goalPad development
PatternMIT model
DornfeldDoyle
An overview of CMP research in Berkeley
Talbot
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CMP Modeling History in SFR/FLCC*Preston’s E
qn.
MRR = CPV
Com
bined eqn.
R=τC
M/( C
+M)
before SFR/FLCC
Luo(SFR
)MRR
= N ×Vo l
Cho
i (FL
CC) MRR =
Trip
athi
(FLC
C)
Trib
o-el
ectro
-che
mic
al m
odel
Interfacial/colloidal effects
DfM/MfD
Computational efficiencyFlexible in scaleProcess links
now
* According to Dornfeld
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Interactions between Input VariablesFour Interactions:Wafer‐Pad Interaction; Pad‐Abrasive Interaction; Wafer‐Slurry Chemical Interaction; Wafer‐Abrasive Interaction
Polishing pad
Abrasive particles in Fluid (All inactive) Pad asperity
Active abrasiveson Contact area
VolChemically Influenced Wafer Surface
Wafer
Abrasive particles on Contact area with number N
Velocity V
Source: J. Luo and D. Dornfeld, IEEE Trans: Semiconductor Manufacturing, 2001
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Pad Materials/Shape Effects
Dishing d
3Erosion e
21
Df
S1=Df1
S=S0
H=Hcu0+Hox0 H= Hstage1
Hcu0
Hox0
Pad/wafer contact modes in damascene polishing
Linear Viscoelastic Pad0 20 40 60 80 100 120 140 160 180 200
0
50
100
150
200
250
300
350
400
450
500
Polishing Time t (second)
Ste
p H
eigh
t S (n
m)
PDi= 0.1PDi= 0.2PDi= 0.3PDi= 0.4PDi= 0.5PDi= 0.6PDi= 0.7PDi= 0.8PDi= 0.9
Stage 1
Stage 2
Stage 3
Dishing and erosion
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Effect of Pattern Density - Planarization Length (PL)
ILD
Metal linesPlanarization Length
High-density region
Low-density region
Global step
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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
Planarization length (window size) effect on “Up area”
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PAD
Z(x,y)
Reference height (z=0)
Z_pad
Z(x,y)
Z_pad
dz
∫−
−×−+××=padZyxZ
zyxZzPDFdzzPDFdensityasperityKpyxF_),(
0
)),(())()(()_(),(
Feature level interaction between pad asperities and pattern topography
∫=die
dxdyyxFtentF ),(_
F_tent > F_die ? F_tent < F_die ?
++Z_pad --Z_pad
No
Yes
No
Yes
Z_pad
z
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Asperity Height (µm)
Probability Density (µm-1)
pD
ab
active asperities
Characterization of Pad Surface
(source : A.Scott Lawing, NCCAVS, CMPUG 5/5/2004)
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Model for the simulation
asperities# ),(),(),( ×∝−= yxRdt
yxdzyxMRR a
),( yxzfitting parameter accounting for chemical reactions, abrasive size distribution etc.
New model
pattern density effect
asperity radius
abrasive particle size
polishing speed
pad/film properties
pad asperity height distribution
∫==),( 4/7
2/3
2/3*4/12** )(),,(
),(),(
yxz
zpw
pa
pad
dAHDyxyxPDDH
VERRCyxMRR δδδε
Mean distance between asperities
hardness of material polished
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Pattern density Line widthLine space
Chip Layout
HDP-CVD Deposition Model
CMP model
CMP Input Thickness
Evolution Nitride thinning
Modeling Overview
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Removal Rate (RR)
Adding the electro-chemical effects
Slurry chemistry(pH, conc. of oxidizer, inhibitor & complexing agent)
Pad propertieslayers’ hardness, structureAbrasiveType, size & conc. Polishing conditions(pressure P, velocity V)
• Develop a transient tribo-electro-chemical model for material removal during copper CMP– Experimentally investigate different components of the model
• Using above model develop a framework for pattern dependency effects.
Polished material
Planarization, Uniformity, Defects
Incoming topography
CMPModel
1. Passivation Kinetics2. Mechanical Properties
of Passive Film3. Abrasive-copper Interaction
Frequency & Force
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Application: Polishing induced stress
Pressure concentrated locally (about 300 psi)
Risk of cracking in the sub layers
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FEM Analysis: Model
LOW-K LayerE = 5 – 20 GPa ; α = 0.25
TANTALUM LayerE = 185.7 GPa ; α = 0.34
COPPER LayerE = 129.8 GPa ; α = 0.34
LOADS:- Downward Constant Pressure – 2psi- Horizontal Shear (friction) stress – 0.7psi
BOUNDARY CONDITIONS:- Fixed at the bottom- Periodic Boundary Conditions (symmetry)
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FEM Analysis in CMPVon Mises stresses
Low-k: E = 5GPa
Step1
Step2
Step3
Step3
Low-k: E = 20GPa
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Modeling Challenges• Present methods treat CMP process as a black box; are blind to process &
consumable parameters• Need detailed process understanding
– For modeling pattern evolution accurately• Present methods do not predict small feature CMP well
– For process design (not based on just trail and error)• Multiscale analysis needed to capture different phenomena:
– At sufficient resolution & speed • CMP process less rigid than other processes: possibility of optimizing
consumable & process parameters based on chip design– MfD & DfM
• Source of pattern dependence is twofold: – Asperity contact area (not addressed yet)– Pad hard layer flexion due to soft layer compression (addressed by
previous models)
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Source: Praesegus Inc.
Extensive test/measurements required
Specific to particular processing conditions
• captures only 1 source of pattern dependency• coarse (resolution ~10µm)
Present Approach (Praesegus/Cadence, Synopsys)
• Helps in dummy fill-- Design improvement but no process optimization
• Optimization should be across process & design: - Need to be able to tune all the available control knobs
Model:
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Pattern Related DefectsLow pattern
densityHigh pattern
density
erosion & dishing
residue film
Initial topography
Non-uniform removal
Local planarization
End point
Over polishing
Nominal Pattern density = Area(high features) / (Total Area)
• ρ(x,y) calculated as a convolution of a weighted function (elliptic) over evaluation window.
• Evaluation window size (R) determined empirically.
),(),(
yxKyxMRR
ρ=
R
Time stepevolution
• MRR(x,y) = material removal rate at (x,y)• K = Blanket MRR• ρ(x,y) = effective pattern density at (x,y)
Present Approach
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Need a “GoogleEarth” view of modeling
We are here
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Head
Platen
Pad
dow
n-
forc
e
slurry supply
rotation of wafer
head
Wafer 4-12”
Copper
Feature
pad asperity
abrasive particles
100nm-10µm
~1µm
1-10µmPad asperity
Abrasive
Pad/Wafer
Die
Feature/Asperity
Abrasive Contact
CMP phenomena at different scales
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Pattern Evolution Framework
),(),(
yxKyxMRR
ρ=
R
Time stepevolution Asperity contact area (µm)
Empirically fit, based onpad flexion (scale=mm)
Space Discretization: Data Structure
STI oxide evolution* 0.112µm/0.1681µm
before 40sec CMP
*Choi, Tripathi, Dornfeld & Hansen, “Chip Scale Prediction of Nitride Erosionin High Selectivity STI CMP,” Invited Paper, Proceedings of 11th CMP-MIC, 2006
• Consumables• Polishing Conditions
Material Removal Model
∫ +=τ
τρ 00 )( dttti
nFMRR Cu
Small feature prediction problems
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Effects to Capture
• Multiscale Behavior– Material removal operates on different scales and contributes to the
net material removed in the CMP process– Material removal at any location is affected by its position in
different scales– Different models need to be used to capture behavior at different
scales
• Far-field Effects– Most IC manufacturing processes are only dependant on local
features– CMP performance depends on both local as well as far-field
features
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CMP Model Tree• Tree based data structure will encapsulate both wafer features
and pattern evolution at various scales
Pad/Wafer (~m)
Die (~cm)
Asperity (~µm)
Feature (45nm-10µm)
Abrasive contact (10nm)
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Data Structure• Efficient surface representation is
required– Mesh-based representations allow for fast
processing, and have been widely used
• Need to capture repeating features– Use tiles/modular units– For “similar” features, use property
inheritance from modular features
• Multiscale analysis– Use multiresolution meshes – allow for
querying in mm/um/nm scales– Also support querying of far-field features
along with local features
nm
mcm
mm
µm
Multiresolution meshes will allow for querying in different scales -resolution will be determined by feature scales; tiling will be used
for repeating features.
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Data Structure
accu
racy
analysis time
Leve
l of D
etail
Tradeoffs between LOD, analysis time, and accuracy
Resolve intosmaller features
Resolve intolarger features
• Model precision vs. Level of Detail– Identify tradeoffs between speed
of analysis and the accuracy of the models used
• Data Structure design motivated by physical considerations– Tree levels ≡ phenomenon scale– object properties ≡ physical
phenomena.
• Inheritance:– Inherit properties from parents at
higher levels of tree and from generic object at that level Example of property inheritance from parent features
and base features applied in CMP process model
Asperity(feature)
CMP Process Model on
Asperity Scale
Pad(parent)
Properties inherited from pad
Specific asperity
properties
Generic Asperity
Properties inherited from generic feature
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Multiscale Optimization Example
• Address WIDNU at different levels depending on available flexibility:– Change pad hardness (tree level 1)
• Inflexibility: scratch defects, pad supplier
– Dummy fill (chip, array level)• Inflexibility: design restrictions
– Change incoming topography (feature level)
• Inflexibility: deposition process limitation
– Change chemical reactions, abrasive concentration (abrasive level)
Within die non-uniformityNitride Thinning in STI
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Thank you for your attention!