Transcript CD Uniformity in Semiconductor Manufacture

Sub-65 nm CD Uniformity in
Semiconductor Manufacture
Terrence E. Zavecz
[email protected]
1
Agenda
2
• ITRS Roadmap Discussion
– Double Patterning & Overlay Implications
• Reticle Response
– Reticle signatures
– Variables included by Simulation
– Design for Manufacture (DFM) Problems
• CDU Signatures
– Process Behavioral Models
– Variance Budget
• The Approach to APC
• CDU Methods Discussion
TEA Systems
Yield Enhancement thru Modeling
ITRS 2005 Methods Lithography
3
TEA Systems
Yield Enhancement thru Modeling
Technology Requirements
4
EUV Problem:
Lens materials
TEA Systems
Yield Enhancement thru Modeling
Hyper NA immersion limits
IMEC status paper 2006
5
TEA Systems
Yield Enhancement thru Modeling
Double Patterning for 32 nm half-pitch
•1 nm overlay error = 1 nm CD change
•Cross-talk failures from rotation
IMEC status paper 2006
TEA Systems
Yield Enhancement thru Modeling
6
Poly DP Solution
7
193i – 1.2 NA
X-Y Polarized light
K1 = 0.28
Pitch 90 nm
TEA Systems
Yield Enhancement thru Modeling
Exposure Tool Solutions
8
Double Patterning
TEA Systems
Yield Enhancement thru Modeling
Immersion Driving the way @ 193 nm
KrF
9
Overlay
6 nm to
Immersion n<=1 (H20)
12 nm
Immersion Hyper NA ( >1 to 1.45)
Development
Immersion Hyper NA ( >1 to 1.5)
Double Patterning (32 nm node)
EUVL 13.5 nm
4nm
12 nm
TEA Systems
Yield Enhancement thru Modeling
CD Metrology Technology
10
TEA Systems
Yield Enhancement thru Modeling
Overlay Technology
11
TEA Systems
Yield Enhancement thru Modeling
Reticle Response
12
TEA Systems
Yield Enhancement thru Modeling
Reticle Enhancement Techniques
13
• “What you See” is not “What you get”
• Design for Manufacture (DFM) Problems
– Simulation
• Neglect of most Process & Optic-stack signatures
– Optical Proximity Correction
• High-Frequency Limitations of Optical System
– Inverse Lithography
• Luminescent
– Phase Shift Masks
• Etch depth bias
• Phase-Element Uniformity
TEA Systems
Yield Enhancement thru Modeling
Chrome Reticle 4:1
14
100 nm BCD Feature Width
1:1 Loading
1:2 Loading
1:3 Loading
Chrome Oxide
TEA Systems
Yield Enhancement thru Modeling
What Effect on Wafer?
15
MoSiON Fingerprint on Reticle
Nanometrics Atlas M
Scatterometry
TEA Systems Weir PW
Data Analysis
TEA Systems
Yield Enhancement thru Modeling
Photoresist & BCD due to Moly Signature
Raw Photoresist Thickness
16
Modeled BCD
MoSiON Signature
• Photoresist
– 1:1 90 nm features
Modeled BCD
• BCD Full-Field Symmetric Model
– BCD component due to MoSiON
– Piston & high-order removed
TEA Systems
– 4.5 nm Range
Yield Enhancement thru Modeling
Soft Metrology Errors
• Target acquisition
• Charging
• Film-stack changes
17
90 nm line, end-Gap metrology
Target acquisition error
– BARC, Reflectivity etc
• Recipe errors
• Proximity
– Nearest neighbor lying 2.5 * image size
• Impact:
– In-Line sampling
– Process signature derivation
TEA Systems
Yield Enhancement thru Modeling
Modeling Wafer Bias
CDsem
Reticle
Wafer
18
Scatter
Reticle
TEA Systems
All Dimensions scaled to wafer final size
Yield Enhancement thru Modeling
Residuals to Bias-Piston
19
Note stable signature
• Remove the Offset coefficient of every column
• Plot the higher order bias.
TEA Systems
Yield Enhancement thru Modeling
Wafer Bias, Scan Direction Changes
20
Population Density (Boxplot)
-5.5 -5.9 -5.1 -5.0 -6.1 -7.1 -6.5 nm
• Fixed-Focus Matrix
• Modeled Field Row
• Wafer bias
– each slit position
• Shown: center row
End-of Scan
Velocity change
TEA Systems
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Critical Feature Uniformity
Signatures
21
TEA Systems
Yield Enhancement thru Modeling
22
Motivation/Introduction
• Sub-65 nm lithography CD budget approaches few
nm...
– active CD control is required to minimize CDU error
budget components
• which are the largest CDU error budget components?
• can we correct CD errors in the same way we do for overlay?
• Intra-wafer appears to be the largest CDU error
component
– Uncontrollable disturbances (process variations) are
impossible to prevent or completely eliminate
TEA Systems
Yield Enhancement thru Modeling
Approach to intra-wafer CDU control
23
• Process uncontrollable disturbances do not vary
across the wafer in a completely random way
– can be represented as stochastic processes with clear
dependence on the spatial wafer coordinates
• Disturbance dependence on intra-wafer spatial
coordinates is determined by time-characteristics
(how the wafer is processed in time)
– whole wafer is processed at a time
• variation tends to be a slow and continuous function
– part of the wafer (I.e., single die) is processed at a time
• variation is a discrete, non-continuous function
TEA Systems
Yield Enhancement thru Modeling
24
Problem Formalization
• Relate process disturbance to a feature response
– linear function with a constant coefficient describing the
feature-disturbance sensitivity
FR a   a * D m
– feature response to disturbances with both continuous and
discrete wafer spatial characteristics
FR a ,  x , y   IFp  x , y   Wp ( x , y )  DD ( x , y )  r  Wp ( x , y )  DD ( x , y )  r
– model feature response components through high order
polynomial fitting6
6
FR a ( x , y ) i , j 

FR a ( x , y ) i , j 
i0
i6
i
ri 
j 6
 r 
 a
i0
j 0
j
j0
i, j
xi y
j
* cos( j  )   j * sin( j 

j
TEA Systems
Yield Enhancement thru Modeling
25
Procedure - raw data analysis
• Measured data in the form of:
Raw MSE data
MSE filter
Data: fieldi ; xi , yi ; FRa
Field Average
All filters
All+REC filters
Wp (x,y)
wafer periodic
model
r (x,y)
wafer random
error
DD(x,y)
field model
Slit & scan
CD profiles
TEA Systems
Yield Enhancement thru Modeling
26
Signatures Outline
• Experimental with feature response matching
to process disturbances
• Matching
– characterization metrics
– covariance
TEA Systems
Yield Enhancement thru Modeling
27
Experimental conditions
• Exposures
• typical 193nm litho process for 90nm features
– AT1100 scanner, 0.75NA with annular illumination
– 90nm gratings at 1:1; 1:2 and 1:3 with full field coverage
– 240nm resist on 78nm Barc on Si
• OCD metrology: NI, rotating polarized light (Nano9030)
• diffractive optical metrology (scatterometry) - outputs spectral intensity
changes of 0th order diffracted light intensity
• modeled grating parameters
–
–
–
–
bottom CD;
resist thickness (Tr) and Sidewall Angle (SWA)
bottom arc thickness (Tbarc)
mean square errror (MSE)
TEA Systems
Yield Enhancement thru Modeling
28
OCD output parameters
• Each fitted OCD feature response has a
characteristics spatial distribution
Bottom CD
Tresist
SideWall Angle
Tbarc
MSE
TEA Systems
Yield Enhancement thru Modeling
29
Process Behavioral Signatures
• Process disturbances with characteristics spatial
distributions and their feature response
Disturbance
Continuous and slow variation within wafer
PEB Temperature
PEB time
BARC Thickness
Development time
Discrete, non-continuous variation within wafer
Dose
Focus
Scan direction
Feature response
Bottom CD
Resist Thickness (Tresist)
Sidewall Angle (SWA)
Bottom CD
Resist Thickness (Tresist)
Sidewall Angle (SWA)
TEA Systems
Yield Enhancement thru Modeling
Process Disturbances - feature response
• Disturbance with slow and continuous spatial
distribution
30
• PEB plate to CD
PEB temp range of 1deg =
CD variation range of 7nm
PEB temp range of 0.5deg = CD
variation range of 4nm
• Disturbance with discrete spatial distribution
• programmed dose and defocus errors to CD
TEA Systems
Yield Enhancement thru Modeling
Disturbance - feature response sensitivity
31
• Experimental CD-sensitivity to process - expected
Disturbance
CD sensitivity
PEB temp PEB time
7.2nm/C 0.7nm/sec
Develop time
0.25nm/sec
(~ nominal time)
Tbarc
1.2nm/nm
Dose
6nm/mj
Defocus
~5nm/100nm
(+/- BF)
• Experimental Tr and SWA sensitivity to exposure
process
Tresist response to FEM
SWA response to FEM
TEA Systems
Yield Enhancement thru Modeling
32
Spatial Characteristics
• CD spatial response to PEB variations
Std PEB
+ 4% PEB Temp - 4% PEB Temp
-10% PEB time
• CD spatial response to defocus variations
-0.1um defoc
Best Focus
-0.1um defoc
TEA Systems
Yield Enhancement thru Modeling
33
Signatures Outline
• Experimental with feature response matching
to process disturbances
• Matching
– characterization metrics
– covariance
TEA Systems
Yield Enhancement thru Modeling
Radial or XY intra-wafer modeled profiles
34
• Wafers with PEB process disturbances (slow
variation)
-26
-30
-28
-32
Radial CD Profile
Radial CD Profile
• Radial plot inter-field model CD
-30
-32
-34
-36
-38
-34
-36
-38
-40
-40
0
20
40
60
80
0
100
20
Wafer radius (mm)
40
60
80
100
Wafer radius (mm)
– XY plot inter-field model CD
10
3
8
2
Fitted CD
Fitted CD
6
4
2
1
0
0
-1
-2
-2
-4
-100
-50
0
Wafer X-axis
50
100
-100
-50
0
Wafer X-axis
50
100
TEA Systems
Yield Enhancement thru Modeling
Intra-wafer modeled profiles coefficients I
35
• Wafers with PEB process disturbances
– slow variation
PEB Time
2
3
CD N + 4%deg
2
1
0
-1
CD N - 4%deg
-100
-75
-50
-2
-25
0
25
50
Wafer Diameter (mm)
1
0
CD N +10%s
-1
-2
75
100
-100
-75
-50
-25
0
25
50
Wafer Diameter (mm)
75
100
0.0015
0.01
Triagonal
Fitted Coefficients
CD N -10%s
Diff fitted C D Profile (nm)
Differential Fitted CD Profile
Fitted Profiles
PEB Temperature
0.0010
0.00
0.0005
-0.01
0.0000
-0.01
-0.0005
-0.02
-0.0010
-0.0015
-10%time
-0.02
Nominal
-0.03
+10%time
+5deg
-10%time
-0.0020
Nominal
-0.0025
X^1
Y^1
Model Coefficients (1st order)
+10%time
+5deg
X^2
Y^2
Model Coefficients (2nd order)
TEA Systems
Yield Enhancement thru Modeling
Intra-wafer modeled profiles coefficients II
36
• Wafers with Defocus process disturbances
– slow variation
0
-2
-4
CD N+01def
CD N-01def
-8
0
80
1.5
8
T3 N+01def
4
T3 N-01def
0
8.00E-02
-0.02
-0.04
X^1
y^1
20
40
60
Wafer Radius (mm)
80
-0.1+off
-0.1
BF
+0.1
SWA N+01def
0.5
SWA N-01def
0
0
100
0.004
X^1
Y^1
6.00E-02
4.00E-02
2.00E-02
0.00E+00
-0.08
1
-0.5
0
100
Tresist Model Coeff
CD Modeled Coeff
Fitted Linear
Coefficients
12
-4
Poly.
20 (CD N-40
60
01def) Wafer Radius (mm)
Poly. (CD
N+01def)
0.00
-0.06
2
SWA Model Coeff
-6
16
Diff fitted SWA (deg)
Diff fitted Tresist (nm)
Diff fitted CD (nm)
Radial Profile
statistics
2
-0.1+off
-0.1
BF
+0.1
20
X^1
40
60
Wafer Radius (mm)
80
100
Y^1
0.003
0.002
0.001
0.000
-0.001
-0.1+off
-0.1
BF
+0.1
TEA Systems
Yield Enhancement thru Modeling
Intra-wafer modeled profiles coefficients III
37
• CD feature response to combined intra-wafer
disturbances slow (PEB) and discrete (dose and
defocus)
Dose variation +/- 2%
1
0
-dE
-1
+dE
-2
Focus variation +/- 0.05 um
-3
0.5
-4
115deg
120deg
125deg
Offset Field delta CD
Offset Field delta CD
2
0
-0.5
-dF
+dF
-1
-1.5
-2
115deg
120deg
125deg
TEA Systems
Yield Enhancement thru Modeling
Intra-wafer modeled profiles with
coefficients III
38
• 2-feature response to combined intra-wafer
disturbances with slow (PEB) and discrete variation
(defocus)
• average field statistics for CD and SWA feature responses
Focus variation +/- 0.05 um
Dose variation +/- 2% um
0.00
2
-dF
-0.20
+dF
-0.40
-0.60
SWA
BCD
Field statistics
Field statistics
0.20
1.5
1
0.5
0
-dE
+dE
-0.5
-1
-1.5
-2
SWA
BCD
TEA Systems
Yield Enhancement thru Modeling
Covariance between two feature responses
to same process disturbance
39
– Statistical values
– Spatial characteristics
1
Covariance Coeff
0.8
0.6
-0.1defoc
0.4
BF
0.2
+0.1defoc
0
Fitted Linear Coefficients of
modeled intra-wafer profiles
0.000
-0.2
0.005
Y^1 - CD
-0.005
-0.4
0.004
Y^1 SWA
-0.010
CD-SWA
CD-Tr
-0.015
0.003
-0.020
0.002
-0.025
0.001
-0.030
-0.035
0.000
-0.1+off
-0.1
BF
+0.1
TEA Systems
Yield Enhancement thru Modeling
40
UP-DOWN scan profiles
– Scan direction: discrete intra-wafer disturbances
UP
DOWN
95.20nm
95.74nm
Scan position
Scan position
– W2W scan disturbances - corrected for REC, wafer
average
slot1
1.0
Delta Scan CD (nm)
slot3
slot5
0.5
slot7
slot9
0.0
slot11
slot13
-0.5
slot15
slot17
slot19
-1.0
1
2
3
4
Position in scan direction
5
slot21
slot23
TEA Systems
Yield Enhancement thru Modeling
Summary: within wafer CDU components
16%
34%
interfield w afer
interfield scanner (U-D)
intrafield (reticle)
Main components
- reticle
- scanner
- wafer process
- residuals
intrafield (slit+scan)
14%
resids
1%
35%
22%
9%
4%
9%
barc
development
PEB (temp/time)
Within wafer CDU
- barc thickness
AF+leveling
dose
9%
41
resids
47%
Note: above data describes a specific process and scanner and could vary
in a different environment
- PEB
- develop
- focus/dose
- residuals
TEA Systems
Yield Enhancement thru Modeling
CDU Variance Signatures
Reticle
42
Scanner
Track & Process
Transmission
Exposure Dose
BARC Uniformity
Uniformity
Slit Uniformity
Resist Film Uniform
Flatness
Focus Stability
PEB Temp & Time
Feature CD
Chuck Flatness
Develop
Up-Down Scan
Soft Metrology Error
Substrate
Wafer flatness
Device topography
Scan Linearity
Effective Focus
Effective Dose
CDU
• Primary disturbances causing CD Uniformity
(CDU) variations grouped upon their sources.
TEA Systems
Yield Enhancement thru Modeling
BARC modeled wafer uniformity
SPIE Vol. 5378-11
43
TEA Systems
Yield Enhancement thru Modeling
Signatures across the wafer
T3 (PR)
TCD
BARC
Variation after
exposure
44
SWA
BCD
TEA Systems
Yield Enhancement thru Modeling
BARC Thickness & SWA
45
But what happens when the process drifts?
Normal process process wafer
TEA Systems
Yield Enhancement thru Modeling
Scan Direction Artifacts
Focus = -1.5
-
46
-1.0 -0.05 0.0 +0.05 +1.0 1.5
+
-
+
-
+
-
+
+
-
+
-
+
-
+
Reticle Scan Direction
TEA Systems
Yield Enhancement thru Modeling
Profile variation with Focus
Lens Before cleaning
SPIE: 5754-87
47
Lens After cleaning
Scan direction
Top CD
-
+
+
-
+
+
-
+
-
+
-
+
-
+
+
-
-
+
+
-
-
+
+
-
+
Slope
Focus = -1.5 -1.0 -0.05 0.0
+0.05 +1.0 +1.5
Focus = -1.5 -1.0 -0.05 0.0
+0.05 +1.0 +1.5
Bottom CD
Process window
TEA Systems
Yield Enhancement thru Modeling
Slope vs Dose across the slit
Lens Before cleaning
48
Lens After cleaning
Up +Scan
5
18 19
6
9
10 11 12
Down -Scan
TEA Systems
Yield Enhancement thru Modeling
49
Signatures Summary
• Litho-process intra-wafer CDU characteristics were analyzed
as feature-responses to process disturbances
– analysis done as statistical values and spatial distributions
– Statistical values
• After removing reticle contribution, the largest CDU error component
appears to come from the process - PEB and Tbarc
– Information from Spatial Patterns identifies common spatial
dependencies to CDU error sources (process disturbance)
• Process components are characterized by ‘low-frequency’ variation
Could be modeled by 6th degree XY polynomial fitting
– possible metrics: fitted profile, fitted coefficients
• Scanner components are randomly distributed with localized variations.
These effects are more difficult to be extracted due to interference with
process variations
– scan UP and DOWN and W2W intrafield fingerprints do NOT contribute to
this CDU error budget!
TEA Systems
Yield Enhancement thru Modeling
Approach to APC
Feedback in Lithography
Process A
• Metrology
– Many but not ALL steps
– Lithography
51
Measurement A
Process B
• Critical Dimension
• Overlay
Process N
Measurement N
• Overlay
– Positional registration
– Current-to-previous level
Feed-Back
Litho
Process
(one tool)
• Critical Dimension
– Feature size control
– Current level
CD &
Overla
y
Modeled
Analysis
Rework
P/F
Next Process
TEA Systems
Yield Enhancement thru Modeling
Feed Forward lithography controls
Process A
• Models, Simulation and a
database are needed
• Add in process
dependencies.
• Objective:
52
Measurement A
Process B
Process N
Measurement N
Feed-Forward
Litho
Process
(one tool)
– Anticipate corrections to
new lots BEFORE exposure.
Rework
CD &
Overla
y
Process
Dependencies
Modeled
Analysis
Feed-Back
Next Process
TEA Systems
Yield Enhancement thru Modeling
Product Cycle through lithography
Photoresist
Deposition
Etch or Film
Deposition
Thickness
Metrology
53
Exposure
Tool
Corrections
Rework: Lots that Fail
Critical
Dimension
Overlay
Particle
Defect
Pattern
Defect
Metrology
Sequence #2
TEA Systems
Yield Enhancement thru Modeling
Cost of Doing Business
54
Hardware
•
•
•
•
•
Optical Metrology
CD-SEM metrology
Defect Inspection
APC Investment
Total
$1.5 million
$2.5 million
$2.5 million
$3.5 million
$10 million
One Lithograph Cell
Time
•
•
•
•
Optical Metrology
CD-SEM
Defect Inspection
Total
hours
1.5 hours
3.0 hours
2.5 hours
7.0
TEA Systems
Yield Enhancement thru Modeling
55
Signature based lot gating
Mean + 3 Sigma
0.18
Influence of Sample Size
• Problem: Sparse data
collection
Typical Production Sample Size
0.16
0.14
– Uncertainty rises as
sample-size drops
Typical Test Sample True Distribution
0.12
0.10
0
5
10
15
20
25
30
Number of data points Measured per Exposure Field
35
40
True
Data
Good Lots Lost to
Distribution
Re-Work
 Sparse Sampling & Lot Yield
Loss of good lots to false-entry
into rework loop.
 Result
Yield Loss
Capacity Loss
SPC
Binomial
Distribution
True Maximum
Mean+ 3 Sigma
TEA Systems
Yield Enhancement thru Modeling
Feed Forward Techniques
56
• EWMA
– Exponentially Weighted Moving Average
– Assumes a stable, continuous process
• Enhanced EWMA
– Include separate estimates and trend charts for:
• Exposure Tool combinations
• Process steps
– Problems
• ASIC or Logic fabs with many processes and devices
• Each process-stream must maintain separate tracking and trends for
each process and reticle set.
• Short Lifetime devices
• Long device start intervals
• Behavioral Model Signatures
– Include systematic modeling of tools and process
– Compensate by adding systematic signatures prior to exposure.
• Signatures for processes and tools used in lot history.
• Signatures for target tools of next process step.
TEA Systems
Yield Enhancement thru Modeling
EWMA Feed-Forward
Tool # 3
57
TRANSLATION
Translation as recorded
0.1
0.05
0.3
X+ 3 s = 0.0847
0
0.2
-0.05
0.15
-0.1
-0.15
0.25
0.1
X+ 3 s = 0.0736
-0.2
0.05
0
-0.25
-0.05
-0.3
-0.1
EWMA improved by 10 nm
TEA Systems
Yield Enhancement thru Modeling
Modeled Systematic Error Tracking
58
0.1
0.08
0.06
0.04
X Translation
0.02
0
-0.02
-0.04
-0.06
-0.08
Y Translation
-0.1
31 10 20 30 09 19 01 11 21 31 10 20 30 10 20 30
/D /J
/J
/J
/F /F /M /M /M /M /A /A /A /M /M /M
p
p
p
ec an an an eb eb ar ar a
a
a
a
a
/9
/9
/9
/9
/9
/9 r/9 r/9 r/9 r/9 r/9 y/9 y/9 y/9
/9
/9
9
9
9
9
9
9
9
9
9
9
9
9
8
0:
0: 9 .. 9 .. 9 ..
0:
0:
0:
0:
0: 0:
0: 0:
0:
0: 0:
.
.
0
0
0
0
0
0
00
0
0 00 .
0 00 00 00 00
0
0
0 00
TEA Systems
Yield Enhancement thru Modeling
Seeing the Forest . . .
1. A single tool
aberration
signature.
2. Identical
signatures
with varying
frequency and
amplitude.
3. “Random”
noise in daily
Trend Charts
59
1.
2.
3.
TEA Systems
Yield Enhancement thru Modeling
Process and Tool overlay offsets
60
Modeled Translation between two tools
0.06
0.05
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
Tool 2
Tool 2
Met3
Via3
M3
Via2
M2
Via1
M1
Contact
Poly
Tool 2
0.20
0.19
0.18
0.17
0.16
0.15
0.14
0.13
0.12
0.11
0.1
0.09
Production variations, and tool setup, are a
function of:
• Reticle
• Film Stress
• Residual Matching
• Film Alignment Sensitivity
TEA Systems
Yield Enhancement thru Modeling
Differentiate Tool Match & Reticle Errors
0.1
0.05
Tool_3
Reticle 1
Tool_3
Reticle 6
61
Metal Level
Tool_4
Reticle 1
0.2
0.0276
0.15
-0.0288
0
0.1
-0.0422
0.0244
-0.05
0.05
-0.0259
-0.0288
-0.1
0
-0.15
-0.05
-0.2
-0.1
Equipment
Tool 4 - Tool3
Ret 06 - Ret 01
X_shift
-9.1
56.4
Y_shift
-10.4 nm
50.3 nm
TEA Systems
Yield Enhancement thru Modeling
Statistical Tools
62
0.100
0.075
Translation
0.050
Bad InterField Stage
Precision.
0.025
CMP Mark
“SMEAR”
0.000
-0.025
-0.050
-0.075
-0.100
-0.125
•
CMP Mark
“SMEAR”
Model based statistics -- Global Alignment, Precision – are used to:
– Qualify tool process capability
– Provide data set quality values for Advanced Process Control
TEA Systems
Yield Enhancement thru Modeling
Implementing the Feed-Forward model
63
• Aberration signatures add as vectors
• Apply feed-forward as:
Pr ocess
Re ticle
Tool
Coef
Tlevel
 n  A  B * ( T level n )  C * ( T level n )  D * (T level n )
– Where “T” is the ith vector coefficient ( Translation, dose,
mag etc.) of the process model that is similarly
implemented as:
k
dP   Ti * p i
i 0
– A,B,C,D are derived dependency or weighting factors
– Tlevel-n values are the vector-coefficients of the process,
reticle and tools sets for level “n” with the reference layer
“m” coefficients subtracted, or
T leveln  T coefficient _ level"n"  T coefficient _ referenceto"n"
TEA Systems
Yield Enhancement thru Modeling
Putting it together - Tool & Reticle!
Reticle Used
Before Corrections
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
Lot Overlay
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
Exposure Tool
Improvement: (Mean +3Sigma)
Before:
After:
•
X axis
0.150
0.073
Y axis
0.108
0.084
um
um
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
•
64
Data includes multiple
Exposure Tools and
reticles in a single
layer.
Corrections added
included only:
– reticle &
– Tool Match Residual
After Corrections
Trans X
Trans Y
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
Exposure Tool
TEA Systems
Yield Enhancement thru Modeling
BlueControl etch APC
65
• Blue Control web site
– http://www.bluecontroltech.com
– Cypress Installation
– MIMO Gate control etch in production
• Lot-to-Lot controller (2004 first implementation)
• Future
– W2W
– WIW
TEA Systems
Yield Enhancement thru Modeling
Successful APC Penetration
66
• CMP
• Overlay
– Statistical
– Model based?
• CVD ?
• Critical Dimension
– Develop Etch Bias CD
– Inter-Wafer CD Uniformity?
– Intra-Wafer CD Uniformity
TEA Systems
Yield Enhancement thru Modeling
CDU Methods Discussion
SEM & ODP in the mask shop
68
• TSMC
– Mask shop ODP
– “32nm phase-shift masks. Timbre's solution provides
more information, more precise critical dimensions (CD),
full profiles, including etch depth, and all-in-one
measurement at the highest data integrity and lowest cost
per measurement. Coupled with the speed of the
measurement, ODP was the clear choice for TSMC.”
• Intel mask shop ODP
• UMC
• InLine vs offline metrology?
TEA Systems
Yield Enhancement thru Modeling
Specific CDU Improvement Tools
69
• TEL Zone Plate
– ACT 12
• 30 seconds with 5C swings
• Slow ramp
• Stability only?
– IMEC & ASML projects
• TEL's first APC product, Ingenio ES TL, was
introduced in October. Ingenio TL ES is a toollevel (TL) system which offers real-time
collection of tool data and analysis.
Lithius I+
• ASML Stability
– DoseMapper
• By individual field or by wafer zone.
– ARC and thickness control
– Acceptance?
• Across field with neutral density filter
– Remote recipe programming
– TEL/ASML joint APC project
– KLA/ASML joint metrology
• Log metrology of status & sensors?
TEA Systems
Yield Enhancement thru Modeling
Photoresist uniformity
as measured by
Scatterometry
Thank You!
TEA
Systems Corporation
http://www.TEAsystems.com
[email protected]
70
Backup Slides
ITRS Lithography Roadmap “A” (2005)
72
TEA Systems
Yield Enhancement thru Modeling
Optical Mask Requirements
73
TEA Systems
Yield Enhancement thru Modeling
Lithography Wafer Metrology Precision
74
TEA Systems
Yield Enhancement thru Modeling
Relative Feature Response - CrOx
75
Comparison of 1:1, 1:2 & 1:3 features on mask. OCD metrology
Range
Sigma
1:2
1:1
1:3
• Number of data points within given
range (Delta) and by Standard Error
Name
100 1:1
100 1:2
100 1:3
Count
Median
121
232.9
121
234.1
121
229.6
TEA Systems
Yield Enhancement thru Modeling
Removing the Reticle Signature
76
• Weir ProMEEF
• Reticle data is
subtracted from
any lot data
• Compensates for
– Culling
– Feature & site
magnification
– orientation
TEA Systems
Yield Enhancement thru Modeling
Wafer Bias of FEM Array
77
• Focus-Dose Matrix
• Bias of Vertical Features
– For Bottom CD (BCD)
• Notice residual fine
structure of fields
TEA Systems
Yield Enhancement thru Modeling
IntraField (IFp) Signature
78
IFp ( x, y)  IFSlit  IFScan  IFRe ticle  r
• IFslit Perturbations
IFslit= Lens Aberrations
IFslit  WL j ( x) 
IFScan 
n

4
 a x
Rows n 0
4
j
a
y
 j r
n
n
– Lens aberrations
– Flare, scatter, proximity
etc.
– Photoresist artifacts
• IFScan Reticle Stage Distortions:
– effective dose
• scan speed
Column 1 j 0
– Effective Focus
• Stage pitch, yaw tilt
• travel height-offset
Raw Data
Cull data
Model
• IFReticle
– Effective feature width
– Photomask Processing
Examine Response
TEA Systems
Yield Enhancement thru Modeling
Bias Modeled FEM
79
• Full-field
systematic
response
TEA Systems
Yield Enhancement thru Modeling
Bias Modeled Range & StDev
Range
80
Standard Deviation
IsoFocal
• Full-Field variation of features
• No “process window” averaging
TEA Systems
Yield Enhancement thru Modeling
Bias Response to Dose
81
Focus = 0
Wafer Bias
Range
IsoFocal
• Bias response at Focus = 0
• Mean Bias with systematic excursion “error bars”
TEA Systems
Yield Enhancement thru Modeling
TCD Isolated Feature Response
82
• Full-field design comparison
• 1:3 isolated, V&H feature
– Blue = field fitted offset coefficient
– Red = Field average systematic error
Modeled TCD Mean +/- SEM
Modeled TCD Piston +/- Resid.(Design)
TEA Systems
Yield Enhancement thru Modeling
Conclusions
83
• DFM and Simulation software
– Require calibration to the target process
– Should be calibrated for full-field, systematic response
• Reticle CD’s as well as film uniformity can be seen
on the wafer image
• Perturbation signatures from process and toolsets are
stable
– Can be accurately derived if the proper models and data
handling are implemented
• Reticle signatures, removed from wafer data, clarify
process and OPC response
TEA Systems
Yield Enhancement thru Modeling