lvts dose&focus recognition by image
TRANSCRIPT
Is it possible to make the life
of Litho Engineer easier or how
to trace DOSE and FOCUS by
image
Vladislav Kaplan, July 2008
Agenda• Classical FE’s
▫ Purpose
▫ Mathematical presentation
▫ Algorithms and models
• Way of Control – CD (CDSEM)
▫ Methodology of measurements
• Is it another way…?
▫ Methodology of Image Processing
▫ Dose/Focus correlated parameters
▫ Classification
▫ SW presentation
• Things to be concerned with…and next steps
▫ Litho tool impact
▫ Metrology tool impact
▫ Layer/Mask impact
Classical FE
FE
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
Classical FE’s
• Choose most sensitive feature. (Could be more than one)
• Run FE.
• Define best dose and focus based on allowable process window.
• Control it with CDSEM.
Classical FEFE
140.0
145.0
150.0
155.0
160.0
165.0
170.0
175.0
180.0
185.0
190.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
Wide process window.
• PW – 160-180 nm
• Dose2Focus5 optimal.
• Still in PW up to +/-2 steps for focus
and up to 1 step for dose
fluctuation.
Tight process window.
• PW – 170-180 nm
• Dose2Focus5 still optimal.
• Still in PW up to +/-1 steps for focus
and no allowable dose fluctuation.
CDSEM.
• For CD measure 175nm dose
fluctuation could from Dose0 to
Dose2 including and +-2 step of
focus, so CDSEM could not be a
”definitive tracer” of litho tool dose
and focus stability.
CDSEM measurement
Grayscale level approach for CD measurement
• Build radial vector.
• Apply LPF alongside the vector.
• Find cross over threshold location.
• Calculate distance from center to location.
• Repeat it N times with different angles.
• Find average.
CDSEM pros and cons.
• Excellent for predefined feature measurement.
• Good for edge location definition.
• Bad for form of feature characterization.
• 90 percent of pixel information in image lost.
Morphology
• Discipline in Image processing specialized on
extraction and characterization of binary images.
Could be very helpful for form description and
recognition.
Examples of Morphology measurement
• Threshold image – get binary one. (0 – black or 1 – red)
• Fill holes
• Reduce particle of particular area or shape – irrelevant for decision making
• Calculate morphology parameters for each remaining particle.
• Parameters could be:
• Elongation, Orientation, Type factor, Waddler disk diameter, Heywood
circularity factor, Compactness factor, Different moments of inertia etc..
ConclusionIn order to increase amount of information used for processing
need to add morphology operations and particle analysis.
FE after morphology operation
1. By replacing CD measurement by particle morphology area
measurement be getting better separation per dose.
FE
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
FE per Via area
0
1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8
Focus
Are
a
D0
D1
D2
D3
D4
D5
FE after morphology operation
FE
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
1. By replacing CD measurement by particle orientation
measurement we getting strong separation between negative and
positive focus.
FE per Via Orientation
0
50
100
150
200
1 2 3 4 5 6 7 8
Focus
De
gre
es
of r
ota
tio
n
D0
D1
D2
D3
D4
D5
FE after morphology operation
FE
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
1. By replacing CD measurement by particle Area*sign[orientation]
measurement we getting strong separation between negative and
positive focus as well as better dose separation..
FE per Via Area combined Orientation
-6000
-4000
-2000
0
2000
4000
6000
1 2 3 4 5 6 7 8
Focus
Area*sig
n[O
rie
ntatio
n]
D0
D1
D2
D3
D4
D5
FE after morphology operation
FE
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
Focus
steps
Focus
CD
's
Dose0
Dose1
Dose2
Dose3
Dose4
Dose5
Dose6
1. By replacing CD measurement by particle Elongation
measurement we getting visible separation of dose.
FE by Elongation
1
1.1
1.2
1.3
1.4
1.5
1 2 3 4 5 6
Dose
Elo
ng
ati
on
Focus0
Focus1
Focus2
Focus3
Focus4
Focus5
Focus6
Focus7
What we are trying to characterize
1. Via changing orientation from <90 degree to > than 90 degree as result of
focus changes
2. Via changes its area as result of focus change
3. Via changes its elongation as result of focus change
4. For every other dose this process has different rate.
Engine for classification
1. Two obvious ways for recognition of Focus/Dose imaging sets are fuzzy
classifier and Minimal Mean Distance classifier.
2. Advantages for Fuzzy classifier –
• Strait forward – just define the required range of values per Dose/Focus
imaging set.
• Simple – no need for complicated mathematical operation, for example
normalizations and decompositions.
3. Disadvantages for Fuzzy classifier –
• Build as is –SW need to be recompiled in order to correct/change any
additional parameter or information.
• Highly Non-linear, rigid structure – problematic tool for research and
definition for new parameters
We choose MMDC – for the research purposes and relative simplicity in
reconfiguration.
Simple MMDC classifier
SW demonstration - Cool
Results
Focus0Dose0 Focus2Dose0 Focus1Dose5 Focus5Dose3 Focus5Dose0 Focus4Dose0
Focus0Dose1 Focus2Dose1 Focus0Dose0 Focus3Dose1 Focus3Dose0 Focus5Dose0
Focus0Dose3 Focus0Dose2 Focus2Dose1 Focus3Dose0 Focus5Dose1 Focus5Dose4
Focus0Dose2 Focus0Dose1 Focus2Dose3 Focus3Dose4 Focus3Dose3 Focus4Dose1
Focus0Dose4 Focus1Dose3 Focus0Dose5 Focus3Dose0 Focus4Dose1 Focus5Dose4
Focus3Dose5 Focus1Dose5 Focus0Dose2 Focus3Dose2 Focus4Dose2 Focus4Dose3
Focus0Dose0 Focus1Dose0 Focus3Dose1 Focus3Dose0 Focus4Dose0 Focus4Dose0
Focus0Dose1 Focus2Dose2 Focus1Dose0 Focus3Dose1 Focus3Dose0 Focus5Dose0
Focus1Dose5 Focus2Dose4 Focus2Dose1 Focus3Dose0 Focus5Dose1 Focus4Dose3
Focus0Dose2 Focus2Dose4 Focus2Dose3 Focus3Dose1 Focus3Dose3 Focus4Dose4
Focus0Dose4 Focus1Dose3 Focus2Dose5 Focus4Dose2 Focus4Dose4 Focus5Dose4
Focus0Dose5 Focus1Dose5 Focus1Dose4 Focus3Dose4 Focus5Dose3 Focus5Dose5
Focus0Dose0 Focus1Dose0 Focus3Dose1 Focus3Dose0 Focus4Dose0 Focus4Dose0
Focus0Dose1 Focus2Dose1 Focus2Dose1 Focus3Dose1 Focus3Dose0 Focus5Dose0
Focus0Dose3 Focus0Dose1 Focus2Dose1 Focus3Dose0 Focus5Dose1 Focus4Dose1
Focus0Dose2 Focus1Dose2 Focus2Dose3 Focus3Dose1 Focus3Dose3 Focus4Dose4
Focus0Dose4 Focus1Dose3 Focus2Dose5 Focus5Dose3 Focus4Dose4 Focus5Dose4
Focus0Dose5 Focus1Dose5 Focus1Dose4 Focus3Dose4 Focus5Dose3 Focus4Dose5
Sign [Orientation]*Area*Elongation
Sign [Orientation]*Area; Area; Area*Elongation
Sign [Orientation]*Area*Elongation; Area
For all cases high
probability to find
Dose/Focus value
within one step of
dose or/and focus
error
Next stepsNeed significant amount of FE data in order to:
• Characterize stability of measurement factors.
• Create real MMDC – not just on one set of FE’s
• Create robust normalization of parameters in SW – Fisher decomposition?
Need significant amount of real CDSEM images from production recipes:
• Full verification process
Need CDSEM recipe optimization – close 0 nm shifts in array
• Characterize stability of measurement factors.
• Full verification process
ConclusionSimilar process of focus/dose characterization possible to perform on any closed
shape feature, not just via. In that case amount of information that we could
extract with the help of morphology operations are greater significantly.