Post on 11-Aug-2020
transcript
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Tom Hoogenboom, Marcel Beems
apc|m conference 2018
April 18, 2018 | Dresden, Germany
Lithography control is data hungry
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ASML
int. ref. D607280
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ASML is a patterning/lithography company
Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
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Summary
Objectives
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ASML is a patterning/lithography company
Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
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Summary
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It’s hard to imagine a world without chipsSlide 4
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A global presence
Veldhoven
Wilton (CT)
Chandler (AZ)
Korea
Taiwan
San Diego (CA)
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A global presence
4,300 employees
Source: ASML Full Year 2017
Offices in over 60 cities in 16 countries worldwide
10,500 employees 4,300 employees
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Big data company - summarized
19,299 collegues
16 countries
~20 customers
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2004Total
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2015Est.Total
Total NAND flash units [B]
NAND Flash price 1 GB [$]
Reduction of cost per bit drives market growthPublic
Digital
cameras
MP3
playerSolid state
hard drivesSmart
phones
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Smaller chips, bigger machines
1980s:
PAS 2000/5000
1990s:
PAS 5500
2000s:
TWINSCAN
2010s:
NXE EUV
Design rule:
1 um 100 nm 10 nm
+- 0.1 um +- 10 nm +- 1nm
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ASML is a patterning/lithography company
Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
Public
Summary
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2015TWINSCAN NXT:1980Di
Our most advanced
immersion system
1984PAS 2000
ASML’s first stepper
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Keeping up with Moore’s Law
2017TWINSCAN NXE:3400B
High volume
EUV system
Wavelength:
13.5 nanometer
Resolution:
≤ 22 nanometer
Overlay:
1.0 nanometer*
Wafer size:
300 mm Ø
Productivity:
125 wafers per hour*
*) use case dependent
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2015TWINSCAN NXT:1980Di
Our most advanced
immersion system
1984PAS 2000
ASML’s first stepper
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Keeping up with Moore’s Law
2017TWINSCAN NXE:3400B
High volume
EUV system
Wavelength:
13.5 nanometer
Resolution:
≤ 22 nanometer
Overlay:
1.0 nanometer
Wafer size:
300 mm Ø
Productivity:
125 wafers per hour
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Control at nm level is really fine-grained
1 mm motion
on scale of Saxony (300 km Ø)
1:300,000,000
1 nm motion
on scale of wafer (300 mm Ø)
1:300,000,000
300 km
↔ 1 mm
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Control at nm level is really fine-grained
1 mm motion
on scale of Saxony (300 km Ø)
1:300,000,000
1 nm motion
on scale of wafer (300 mm Ø)
1:300,000,000
300 km
↔ 1 mm
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nm control is based on lots and lots of data
Up to 300 wafers per hour,
> million per year per machine
Lithography: 10,000 samples per wafer
TeraBytes of data per year
Metrology tool: 100-10,000 samples per wafer
TeraBytes of data per year
We use this to control every square mm2 of the wafer (300 mm Ø):
100,000 inputs, 6 axis = 600,000 inputs/wfr
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nm control is based on lots and lots of data
Up to 300 wafers per hour,
> million per year per machine
Lithography: 10,000 samples per wafer
TeraBytes of data per year
Metrology tool: 100-10,000 samples per wafer
TeraBytes of data per year
We use this to control every square mm2 of the wafer (300 mm Ø):
100,000 inputs, 6 axis = 600,000 inputs/wfr
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Introduction ASML ApplicationsDeliver performance and control through holistic approach
Ensure stability
during HVM production
Ensure optimal
yield by design
Ensure metrology
efficiency and effectiveness
Slide 17
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ASML is a patterning/lithography company
Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
Public
Summary
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Clever sampling strategies reduce metrology time
Before control:
• Metrology for verification
• After the fact
Metrology for control:
• Dense sampling is ideal but takes time and has a cost
• Sparse sampling may not capture every variation
• Clever combination of sparse+dense is optimal
Example: dense metrology
Example: sparse metrology
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Clever sampling strategies reduce metrology time
Before control:
• Metrology for verification
• After the fact
Metrology for control:
• Dense sampling is ideal but takes time, has a cost and takes real-estate
• Sparse sampling may not capture every variation
• Clever combination of sparse+dense is optimal
Example: dense metrology
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Clever sampling strategies reduce metrology time
Before control:
• Metrology for verification
• After the fact
Metrology for control:
• Dense sampling is ideal but takes time and has a cost
• Sparse sampling may not capture every variation
• Clever combination of sparse+dense is optimal
Example: dense metrology
Example: sparse metrology
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Clever sampling strategies reduce metrology time
Before control:
• Metrology for verification
• After the fact
Metrology for control:
• Dense sampling is ideal but takes time and has a cost
• Sparse sampling may not capture every variation
• Clever combination of sparse+dense is optimal
Example: dense metrology
Example: sparse metrology
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One strategy is to separate dense and sparse in time
Dense Sparse
Capture stable
fingerprint at
intervals
Capture
lot to lot
changes more
frequently
Combine dense and sparse to
estimate total variation
to feedback/feedforward
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A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)
Approach:
• Start with the dense sampling plan
• Remove samples until just those remain
that best represent the source(s) of variation:
Dense SSO (200pts)
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A A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)
Approach:
• Start with the dense sampling plan
• Remove samples until just those remain
that best represent the source of variation:
Dense SSO (200pts)
• All 200 samples
estimate x-y
‘translation’
• Edge samples
estimate
‘magnification’
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A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)
Approach:
• Start with the dense sampling plan
• Remove samples until just those remain
that best represent the source of variation.
• Result is still close to the
‘true’ values measured using
the dense plan.
• Measurement time is reduced.
• The 200 pts in the example
can be measured in-line
reducing cycle time.
SSO (200pts)
• All 200 samples
estimate x-y
‘translation’
• Edge samples
estimate
‘magnification’
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Two more optimizations are possible
Same results as all-dense sampling are possible:
1) running dense at intervals,combining with more frequent sparse sampling
2) running an optimal subset of sparse and dense (SSO)
Two ways to further optimize knowledge about patterning variations:
3) Bring in additional metrology data to interpolate the missing dense points
• E.g. from sensors in the lithography tool
4) Separate detection from control
• fast measurements locate sites in need of control
• more accurate but slower metrology determines the control move
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Two more optimizations are possible
Same results as all-dense sampling are possible:
1) running dense at intervals,combining with more frequent sparse sampling
2) running an optimal subset of sparse and dense (SSO)
Two ways to further optimize knowledge about patterning variations:
3) Bring in additional metrology data to interpolate the missing dense points
• E.g. from sensors in the lithography tool
4) Separate detection from control
• fast measurements locate sites in need of control
• more accurate but slower metrology determines the control move
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Concept: add metrology data from scanner to compute
wafer maps for non-measured wafers (ref: presentation Paul Böcker)
After-develop metrology
few wafers only
Scanner metrology
per wafer
On product wafers:
Process Fingerprint
On product wafers:
Metrology maps for 100% of the wafers
Computational metrology maps
for 100% of the wafers
Sampled wafers Sampled wafers
|
All wafers
+ ||
All wafers
processing
Alignment fingerprints
Leveling maps
Servo maps
Lens distortion coefficients
Monitoring (wafer) maps
focus / overlay map
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Two more optimizations are possible
Same results as all-dense sampling are possible:
1) running dense at intervals,combining with more frequent sparse sampling
2) running an optimal subset of sparse and dense (SSO)
Two ways to further optimize knowledge about patterning variations:
3) Bring in additional metrology data to interpolate the missing dense points
• E.g. from sensors in the lithography tool
4) Separate detection from control
• fast measurements locate sites in need of control
• more accurate but slower metrology determines the control move
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Separate detection from control:optical metrology preselects areas for e-beam metrology
optical
Design data
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Separate detection from control:optical metrology preselects areas for e-beam metrology
optical
Design data
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ASML is a patterning/lithography company
Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
Public
Summary
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Even with ‘big data’ approach clever sampling is needed
The 4 ways to arrange sampling for control, reducing metrology time:
1) run dense at intervals, combine with more frequent sparse sampling
2) run an optimal subset of sparse and dense (SSO)
3) bring in additional metrology to interpolate missing data
4) separate detection from control
Still not good for ‘big data’ approach:
- The most prominent ‘feature’ is the choice of sampling plan:
- So we need to further condition the input data
parameters = data not available (75%)= data available (25%)
time
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Why even bother about ‘big data’?
“Big” data and multivariate relationships cannot be handled
by a human analyst/domain expert.
Data science/machine learning sciences provide powerful and
efficient methods to demonstrate relationships,
without physical understanding.
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Some thoughts on industrialization
In a well-controlled industrial
process >99% of the data is
within limits.
Only outliers are potentially
interesting: scarce data after
all…
Big data, really? Slide 36
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Some thoughts on industrialization
In a well-controlled industrial
process >99% of the data is
within limits.
Only outliers are potentially
interesting: scarce data after
all…
We use ‘big data’ algorithms to
help find off-spec wafers
Big data, really? Slide 37
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Use case: Group wafers by clamped wafer shape
The Lithography machine clamps a wafer on a stage.
This deforms the surface (at nm scale).
Groups of wafers show similar behavior.
To spot an off-spec wafer, it is useful to group the wafers first,
because scanner sensor data is related to the shape ‘as clamped’
Use case for machine learning:
- Group wafers by their shape ‘as clamped’
- Find out-of-spec wafers by comparing with average wafer in group
based on the samples from scanner alignment / UVLS (UV level sensor)
Approach [1]*:
. Train a deep feedforward neural network to measured overlay metrology
The training can be done based on a dense sample plan.
. Compare new wafers using neural network classify as in/out-of-spec
*) see next slide for SPIE reference
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Reference for this topic
[1] Full paper presented at SPIE 2017:
Emil Schmitt-Weaver, Venky Subramony, Zakir Ullah, Masazumi
Matsunobu, Ravin Somasundaram, Joel Thomas, Linmiao
Zhang, Klaus Thul, Kaustuve Bhattacharyya, Ronald Goossens,
Cees Lambregts, Wim Tel, Chris de Ruiter,
"Computational overlay metrology with adaptive data analytics“,
Proc. SPIE 10145, Metrology, Inspection, and Process Control
for Microlithography XXXI, 101450V (28 March 2017).
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[1]
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Lithography control is data hungry
Clever sampling strategies reduce metrology time
Machine learning is one of our tools
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Recap
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