virtual overlay metrology for fault detection supported ...€¦ · virtual overlay metrology for...
TRANSCRIPT
Virtual overlay metrology for fault detection supported with integrated metrology and machine learning
Emil Schmitt-Weaver
Public
MATLAB Expo 2016 Benelux – June 28th
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Outline Slide 4
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
How the function works Slide 5
• Function input comes from TWINSCAN metrology & context;
Public
Wafer Alignment metrology for all colors (NIR, FIR, red, green)
• Residuals with respect to color & model used
• Wafer quality
Wafer Leveling metrology
TWINSCAN Context • Chuck number • Field position • Target position
Function f : 3 inputs 1 output
1
2
3
Input Output
5 nm
m+3sx: 9.0 nmy: 7.4 nm
Predicted (F2N)
Outline Slide 6
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
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Outline Slide 10
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
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Outline Slide 13
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
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Outline Slide 18
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
Precision of Trained Function as a vector map Slide 19 • Noise between the measured and predicted overlay is relatively
consistent for both Training and Testing groups • Consider the error as a plus or minus contribution per wafer coordinate
position of any prediction from the trained function
Public
1 nmm+3sx: 1.9 nmy: 1.6 nm
Training Group = Avg Measure-Predcited Error
1 nmm+3sx: 2.4 nmy: 1.9 nm
Testing Group = Avg Measure-Predcited Error
1 nmm+3sx: 1.7 nmy: 1.4 nm
Training - Testing Groupa b c
Point - Point delta between a) and b)
1 nmm+3sx: 1.9 nmy: 1.6 nm
Training Group = Avg Measure-Predcited Error
1 nmm+3sx: 2.4 nmy: 1.9 nm
Testing Group = Avg Measure-Predcited Error
1 nmm+3sx: 1.7 nmy: 1.4 nm
Training - Testing Group
Outline Slide 20
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
Results – Measured Data Slide 21 • SK hynix provided the on product overlay data for our proof book analysis.
• Process for the 20nm DRAM layer was intentionally manipulated as it was prepared for high volume production by the integration team
Public
200 400 600 800 1000 1200 1400 1600
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Overlay X m3s (nm) Measured per Wafer
Ck1 7= 9.61 nmCk2 7= 9.26 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
6
8
10
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14Overlay X m3s (nm) Prediction per Wafer
Testing Wafers R=0.86675
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10
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16Overlay X m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.49 nmCk2 7= 4.26 nm
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mea
sure
d m
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9Lo
t 20
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1Lo
t 22
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t 26
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t 28
Lot 2
9
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Lot 3
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Overlay Y m3s (nm) Measured per Wafer Ck1 7= 6.60 nmCk2 7= 5.78 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
2
4
6
8
10Overlay Y m3s (nm) Prediction per Wafer
Testing Wafers R=0.63485
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10Overlay Y m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.36 nmCk2 7= 4.77 nm
Black lines denote start and end to lot Green lines denote wafers testing group
Wafer Sequence
Mean per chuck of m3s from all wafers measured
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Overlay X m3s (nm) Measured per Wafer
Ck1 7= 9.61 nmCk2 7= 9.26 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Prediction per Wafer
Testing Wafers R=0.88059
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.41 nmCk2 7= 4.15 nm
a
b
c
Results – Overlay X Slide 22
Public Measured Testing w705
a
5 nm
m+3sx: 11.3 nmy: 8.5 nm
Measured (F2N)
Predicted Testing w705
b
5 nm
m+3sx: 9.0 nmy: 7.4 nm
Predicted (F2N)
Residual Testing w705
c
5 nm
m+3sx: 6.6 nmy: 5.1 nm
Residual
• Measured, Predicted & Residual Integrated Metrology (IM)
testing group R value
• With Predictions we identify jumps where process was intentionally manipulated
• IM Residuals are used to flag changes to process not covered by training input
• Select same wafer (705) from testing group
Results – Overlay Y Slide 23
Public
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1Lo
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5Lo
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Lot 6
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t 68
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Lot 8
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Lot 8
8
Overlay Y m3s (nm) Measured per Wafer Ck1 7= 6.60 nmCk2 7= 5.78 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
2
4
6
8
10Overlay Y m3s (nm) Prediction per Wafer
Testing Wafers R=0.63485
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10Overlay Y m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.36 nmCk2 7= 4.77 nm
d
e
f
5 nm
m+3sx: 11.4 nmy: 8.9 nm
Measured (F2N)
Measured Testing w1278
d
5 nm
m+3sx: 9.3 nmy: 4.9 nm
Predicted (F2N)
Predicted Testing w1278
e
5 nm
m+3sx: 4.4 nmy: 5.7 nm
Residual
Residual Testing w1278
f
• Measured, Predicted & Residual Integrated Metrology (IM)
testing group R value
• With Predictions we identify jumps where process was intentionally manipulated
• Residual are used to flag changes in process conditions
• Select same wafer (1278) from testing group
Outline Slide 24
• Introduction • How the function works • Data separation into Training and Testing groups • Training with Bayesian Automated Regularization • Prediction Vs. Measured Overlay as regression plots • Precision of Trained Function as a vector map • Results • Conclusion
Public
Conclusion 1 Slide 25
Public
• With the Predictions we identify jumps in the overlay data where process was intentionally manipulated by the integration team
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mea
sure
d m
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m)
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Lot 1
1
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2Lo
t 13
Lot 1
4Lo
t 15
Lot 1
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7Lo
t 18
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Lot 2
0Lo
t 21
Lot 2
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6Lo
t 27
Lot 2
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Lot 4
2Lo
t 43
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t 56
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t 58
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9Lo
t 60
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1Lo
t 62
Lot 6
3
Lot 6
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Lot 6
5Lo
t 66
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7Lo
t 68
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9Lo
t 70
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2Lo
t 73
Lot 7
4
Lot 7
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Lot 7
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Lot 7
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Lot 8
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Lot 8
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Lot 8
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Lot 8
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Lot 8
8
Overlay X m3s (nm) Measured per Wafer
Ck1 7= 9.61 nmCk2 7= 9.26 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Prediction per Wafer
Testing Wafers R=0.88059
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.41 nmCk2 7= 4.15 nm
5 nm
m+3sx: 11.5 nmy: 8.2 nm
Predicted Wafer 151 (F2N)
5 nm
m+3sx: 10.0 nmy: 4.6 nm
Predicted Wafer 215 (F2N)
5 nm
m+3sx: 14.2 nmy: 13.0 nm
Predicted Wafer 1493 (F2N)
5 nm
m+3sx: 12.5 nmy: 7.6 nm
Predicted Wafer 1583 (F2N)
a b c d
a b c d
Conclusion 2 Slide 26
Public
• With Residuals we flag wafers from IM. • Something other then inputs we trained with is effecting the overlay signature • This can be used to remove a wafer from APC or to trigger an investigation
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Lot 1
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Lot 1
2Lo
t 13
Lot 1
4Lo
t 15
Lot 1
6
Lot 1
7Lo
t 18
Lot 1
9
Lot 2
0Lo
t 21
Lot 2
2
Lot 2
3
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4
Lot 2
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Lot 2
6Lo
t 27
Lot 2
8
Lot 2
9
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0
Lot 3
1
Lot 3
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Lot 3
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Lot 3
4Lo
t 35
Lot 3
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9Lo
t 40
Lot 4
1
Lot 4
2Lo
t 43
Lot 4
4
Lot 4
5
Lot 4
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Lot 4
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Lot 4
8
Lot 4
9
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0
Lot 5
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Lot 5
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Lot 5
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5Lo
t 56
Lot 5
7Lo
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Lot 5
9Lo
t 60
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1Lo
t 62
Lot 6
3
Lot 6
4
Lot 6
5Lo
t 66
Lot 6
7Lo
t 68
Lot 6
9Lo
t 70
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1
Lot 7
2Lo
t 73
Lot 7
4
Lot 7
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Lot 7
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Lot 8
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Lot 8
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Lot 8
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Lot 8
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Lot 8
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Lot 8
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Lot 8
8
Overlay X m3s (nm) Measured per Wafer
Ck1 7= 9.61 nmCk2 7= 9.26 nm
200 400 600 800 1000 1200 1400 1600
pred
icte
d m
3s (n
m)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Prediction per Wafer
Testing Wafers R=0.88059
Wafer Sequance200 400 600 800 1000 1200 1400 1600
resi
dual
m3s
(nm
)
2
4
6
8
10
12
14
16Overlay X m3s (nm) Residual of Measured Wafers minus Prediction
Ck1 7= 4.41 nmCk2 7= 4.15 nm
5 nm
m+3sx: 7.5 nmy: 6.9 nm
Residual Wafer 146
5 nm
m+3sx: 7.7 nmy: 5.6 nm
Residual Wafer 811
5 nm
m+3sx: 8.4 nmy: 7.9 nm
Residual Wafer 1112
5 nm
m+3sx: 7.2 nmy: 6.8 nm
Residual Wafer 1567
a b c d
a b c d
Moving forward Slide 27
• Work on this subject is open to users with interest in exploring the application, both in production and development environments
• Topics of interest include exploring effect; • Fab context from outside the lithocluster has on the overlay
prediction • Increasing the number of parallel works and neurons has toward
improving the R value (correlation coefficient) between the predicted output and target values in the testing dataset
Public
Public
: The authors would like to thank ASML colleagues Jan Mulkens, Marcel Beems, Wolfgang Henke, Henry Megens, Peter ten Berge, Paul Luehrmann, Dick Verkleij, Frank van de Mast, Christophe Fouquet & coworkers in the Holistic Applications Development group for there assistance & feedback
: Hong-Goo Lee, Min-Suk Kim, Sang-Jun Han, Myoung-Soo Kim, Won-Taik Kwon & Sung-Ki Park
: Kevin Ryan, Thomas Theeuwes, Kyu-Tae Sun, Young-Wan Lim, Daan Slotboom, Michael Kubis & Jens Staecker