Scanner matching using pupil intensity control between scanners in ...

Scanner matching using pupil intensity control between scanners
in 30 nm DRAM device
Jongwon Jang a , Daejin Park a , Jaeseung Choi a , Areum Jung a , Gyun Yoo a , Jungchan Kim a ,
Cheol-kyun Kim a , and Donggyu Yim a
a R&D Division, Hynix Semiconductor Inc.
San 136-1, Ami-ri, Bubal-eub, Ichon-si, Kyungki-do 467-701, Korea
Tel : 82-31-630-3833, Fax : 82-31-630-4548, E-mail : jongwon.jang@hynix.com
Junwei Lu b , Seunghoon Park b , Zongchang Yu b , Venu Vellanki b , Wenkin Shao b , and Chris Park c
b. Brion technologies Inc., an ASML Company
c. ASML
ABSTRACT
Scanner mismatch has become one of the critical issues in high volume memory production. There are several
components that contribute to the scanner CD mismatch. One of the major components is illumination pupil difference
between scanners. Because of acceleration of dimensional shrinking in memory devices, the CD mismatch became more
critical in electrical performance and process window.
In this work, we demonstrated computational lithography model based scanner matching for sub 3x nm memory devices.
We used ASML XT:1900Gi as a reference scanner and ASML NXT:1950i as the to-be-matched scanner. Wafer
metrology data and scanner specific parameters are used to build a computational model, and determine the optimal
settings by model simulation to minimize the CD difference between scanners. Nano Geometry Research (NGR) was
used as a wafer CD metrology tool for both model calibration and matching result verification. The extracted pupil
parameters from measured source map from both before and after matching are inspected and analyzed. Simulated and
measured process window changes by applying the matching sub-recipe are also evaluated.
Key words: scanner matching, pupil mismatch, CD difference, process margin
1. INTRODUCTION
Device technology node shrink for production cost reduction has become a common trend to memory chip makers. This
constant effort brought about not only shrink of pattern CD but also shrink of allowable pattern CD variation. As
minimum half pitch is getting smaller, the required CD tolerance is becoming tighter. The pattern CD mismatch
between scanners eventually aggravates the electrical performance of whole devices. It indicates that a few nm
differences which were acceptable in the past have now become no longer tolerable.
In high volume production fabs, it is common that more than one scanner is used to expose the same layer. The pattern
CD differences between scanners may become an issue. In node development speed point of view, scanner tool-to-tool
mismatch is one of the biggest hurdles in process transfer from research center to high volume production fab. 1
Scanner matching is generally understood as reduction of wafer pattern CD differences between scanners. In other
words, scanner matching can be substituted to print all possible patterns with the same CDs as the reference scanner by
using available scanner manipulators (tunable knobs). 2 Although there are a lot of factors which cause CD mismatch, in
this particular case it was largely caused by the differences in illumination part of the lithography tools. The matching
process introduced in this paper attempts to minimize the CD mismatching using all available tunable knobs.
Optical Microlithography XXIV, edited by Mircea V. Dusa, Proc. of SPIE Vol. 7973,
79730S · © 2011 SPIE · CCC code: 0277-786X/11/$18 · doi: 10.1117/12.879570
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2. BASIC THEORY FOR SCANNER MATCHING
2.1 Model-based scanner matching methodology
The target scanner which shows the desired pattern CDs and process margin is called the reference scanner (ASML XT
1900Gi), and the scanner which shows different CD trend from the reference scanner is called the TBM (to-be-matched)
scanner (ASML NXT 1950i). ASML’s Pattern Matcher FullChip (PMFC) is used as the simulation and optimization
engine in this paper. PMFC uses computational lithography approach in layout simulation and scanner tuning. It
optimizes the TBM scanner settings in order to match the reference scanner on a given set of target patterns. Unlike
traditional scanner matching methods which are based entirely on wafer CD measurements, PMFC uses lithographic
simulation together with wafer CD measurements to provide quick and accurate matching solution.
Lithographic model is the central element in this approach. The matching process starts from building a model that
captures characteristics in lithography and resist processes. Tachyon FEM+ TM modeling engine takes scanner optics and
FEM (Focus Exposure Matrix) wafer CD measurements to calibrate OPC quality model. The model not only needs to
have CD prediction ability, but it also needs to have good optic-resist separability. In other words, scanner tuning
requires the model to predict CD not only under nominal optical condition (numerical aperture and sigma) and nominal
process condition (best focus and best dose), but also under the off-nominal optical and process conditions, such as
perturbed NA, sigma. This is important in scanner tuning because the tuning needs to evaluate CDs across entire
scanner tunable space. A pupil prediction algorithm has been developed for generating pupil source map from the
original measured one in order to satisfy the accuracy requirements for scanner tuning. The algorithm can generate
arbitrary pupil map according to perturbation of sigma and other source related knobs.
ASML Scanner Fingerprint File generator software is used to obtain scanner design data and also on-tool measurement
data, such as NA, source map, laser bandwidth, lens aberration and mechanical vibration which is bundled together into
a Scanner Fingerprinting File (SFF). SFF makes transfer of scanner metrology data between scanner and Tachyon
seamlessly.
The PMFC matching flow is illustrated in Figure 1. The reference model is used to generate target CDs and derive the
TBM model via wafer CD based differential calibration. The CD mismatch of a given pattern can be predicted by
simulation using reference and TBM models. The TBM model is also used to compute scanner knob sensitivities.
Scanner related constraints will also be needed in scanner tuning. The constraints file contains physical tunable range of
each scanner knob and valid knob offsets defined by scanner calibration.
Figure 1. Schematic flow of scanner matching using PMFC.
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Scanner tuning optimizer takes CD, knob sensitivity and scanner constraint files as input, and performs tuning
optimization on all matching targets. The tool adopts hybrid optimization approach that combines linear sensitivity with
non-linear model-based correction. It can solve non-linearity in some tuning knobs. Degeneracy between knobs is also
being considered in the tuning algorithm. The optimizer will find out the best possible combination of knob offsets
according to user specified matching spec and cost function. The cost function is computed from the CD mismatch and
cost weight of each matching target. Since the tuning is well-constrained by scanner settings, the tuning output (subrecipe),
can be directly applied to the scanner without the need for scanner re-calibration. Post matching TBM model is
also automatically generated and can be used in matching performance verification including process window check.
Matching target pattern selection is also an important step in model-based scanner matching. Since the matching can be
based on simulated CD, it can handle a large number of CDs. However, when it comes to wafer CD based matching and
verification, metrology noise and available tool time have to be considered. Another important step of tuning is to setup
appropriate cost function. Users can assign relative higher weight on patterns that are important in the process while
setting up lower weight on patterns that are insignificant, but still in need of monitoring the CD during tuning. Figure 2
shows the flow that was used in this work. The first two and last steps require wafer CD metrology while the rest can be
done by model simulation without wafer CD metrology. Once the resist model is calibrated, the matching can be done
quickly and automatically.
2.2 Test patterns
Figure 2. Typical scanner matching flow in the PMFC.
The matching target patterns, which evaluate the matching performance in this work, were automatically extracted
according to average duty ratio which is defined in Table 1. The 191 sampling CD patterns representing electrical
performance of the memory device include 15 critical patterns which were manually selected in the most frequently
repeated core area in the full chip layout. The critical patterns are the weak patterns under defocus condition and directly
connected to overall quality of the device. Therefore, the scanner matching work was mainly focused on the 15 critical
patterns. The 191 sampling CD points also include one anchor pattern which is used to determine best focus and
optimized dose conditions. The PMFC matching tool can adjust all other CDs including space CDs by model-based
dose sensitivity accordingly.
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Table 1. The list of selected 191 test patterns including the anchor pattern and definition of average duty ratio.
2.3 Wafer CD metrology
The matching process in this work completely depends on model calibration, so high metrology accuracy is the most
important prerequisite for the process. NGR2100 TM was used as wafer CD metrology tool in this work. NGR2100 TM is a
wide-field pattern inspection system which can compare pattern CDs on a wafer to the design layout. A lot of pattern
CDs can be rapidly and accurately measured by comparing the real printing images with layout at full chip level. 3
The test pattern CDs used for model calibration took the average of the whole data CDs measured from 3 different
exposure fields under same illumination conditions. The measured CD difference from 3 field CD data sets is compared
in Figure 3, and most CD differences are within ±0.5nm. Location dependent trend was not observed between different
fields.
Figure 3. Measured sampling CD differences between exposure fields
3. MATCHING PERFORMANCE VERIFICATION
3.1 Simulated matching performance
The manually and automatically selected 191 CD patterns are also used to demonstrate model based scanner matching
methodology including process window impact. The reference model is calibrated using the measured CDs from a
focus-energy matrix wafer exposed on the reference scanner. Model of to-be-matched scanner is calibrated from
reference model and a few wafer measurements on TBM scanners. The TBM model inherits the resist terms from
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eference scanner model. The matching process tunes NA, sigma center, sigma ring width and other tunable knobs
based on simulated and measured CDs on the two scanners. Tuning optimization algorithm uses an iterative loop to
evaluate the simulated CD changes and find out the optimal combination of scanner tuning knobs. The outcome of
tuning is a sub-recipe which consists of suggested scanner knob offsets on the TBM scanner.
It is generally understood that line CD varies in inverse proportion to NA change. However, the actual CD variation
trend may differ due to higher diffraction orders as shown in Figure 4. The simulated CD differences are grouped in
order of average duty ratio along X-axis. The relatively dense patterns are more sensitive to NA variation than relatively
isolated patterns except for NA insensitive patterns which are shown in the box of dotted line in the Figure. This is
because that the test patterns which are insensible to NA change are surrounded by isolated patterns, though their own
average duty is relatively low. As shown in Figure 5, test pattern CDs globally varied at the change of sigma ring width.
The decreased sigma ring width increases off axis illumination effects, and it brings the improved resolution and the
declined DOF margin, like when NA increases. Therefore, sigma ring width is proportion to CD variation trend. Based
on this CD variation trend, CD difference between scanners can be selectively reduced by suitable combination of NA,
sigma manipulators.
Figure 4. Simulated pattern CD variation trend according to NA perturbation.
(Duty group A: duty ratio≤1, Duty group B: 12.5)
Figure 5. Simulated pattern CD sensitivity of sigma ring width.
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As a result of matching process, we generated two different sub-recipes as listed in Table 2. The sub-recipe using pupil
control module (PUPICOM) Spoke has relatively smaller offsets of regular tuning knobs, NA and sigma, compare to
sub-recipe without the PUPICOM Spokes. Both sub-recipes met the matching performance requirement, however, we
chose the sub-recipe without PUPICOM Spokes in wafer CD based verification, because it has accomplished the
intended goal using simple modulation of center sigma, width of sigma, and NA.
Table 2. New matching illumination condition for scanner matching.
Figure 6 shows predicted matching performance using the sub-recipe without PUPICOM Spokes. Pre-matching CD
differences are measured, and post matching CDs are simulated by model. The pre-match difference increases with linewidth.
Also, patterns with line width in 140nm ~ 180nm have relatively smaller pre-match difference compared to other
patterns. It is because those patterns are in close proximity to very isolated dummy patterns. This trend is removed after
tuning. The matching achieved +/-1.0 nm spec for all critical patterns except for 1 point which has initial CD mismatch
of almost 3nm. Therefore, the matching process can selectively adjust the CD to keep all pattern CDs within target.
Figure 6. Projected matching improvement using the sub-recipe without the PUPICOM Spokes.
3.2 Matching Performance
The measured matching performance compared with the simulated results previously described is shown in Figure 7.
The number of test patterns which were out of spec before the matching process was reduced from 39 to 1. The
measured CD trend on the wafer after matching is well matched to simulation. Mismatch of all critical patterns are
within ±1nm spec except for one pattern, although it shows about 60% CD matching improvement.
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Figure 7. Simulated and measured matching improvement.
3.3 Analysis of PFM parameter variation for matching process
In this study, we only exposed wafer using the sub-recipe without PUPICOM Spokes. As mentioned previously, even
without the PUPICOM Spokes, the matching can achieve the required spec. However, we would like to look into the
extended tuning capability to further reduce mismatch between the two scanners. In general, the PUPICOM Spokes is
used to compensate for illumination pupil ellipticity by inserting spokes into the pupil to adjust pupil pole intensity
distribution. The matching performance of the sub-recipe with PUPICOM Spokes shows small improvement compared
to the sub-recipe without PUPICOM Spokes in this matching case. However, it is valuable to study how the previous
illumination pupil has actually been modified by applying the sub-recipe with the PUPICOM Spokes. The sub-recipe
with PUPICOM Spokes shown in the Table 2 is used to analyze pupil shape variation and matching performance.
Figure 8 shows measured pupil shapes from reference scanner, pre-match and post match TBM scanner. The calibrated
pupil shape applying PUPICOM Spokes offset is shown in Figure 8 (c). X-dipole from post match pupil shows visible
difference compared to the pre-match pupil.
(a) Reference scanner (b) TBM scanner (Pre-match) (c) TBM scanner (Post- match)
Figure 8. Measured pupil shapes from (a) reference scanner, (b) TBM scanner before matching, and (c) TBM scanner
after applying sub-recipe with PUPICOM Spokes.
Pupil Fit Model (PFM) was used to compare the specific parameters of the measured pupils. PFM uses a parametric
method to describe pupil shapes, such as back ground intensity, inner edge width, and outer edge width. 4,5 Figure 9
indicates the pupil shape of the TBM scanner was measured twice at an interval of 3 weeks to check how much scanner
pupil had been changed in time. It is confirmed that there is no pupil shape drift on the TBM scanner during our
matching exercise. Figure 9 also shows the variation of PFM parameters before and after applying PUPICOM Spokes
on the TBM scanner. As the plot indicates, the ‘location’, ‘location error’, and ‘width of dipole’ are the major PFM
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parameter changes. The PFM analysis indicates that the TBM pupil’s X-dipole is shifted to the right and Y-dipole is
shifted to the bottom compared to the REF scanner. It is seen that pupil parameters after matching varies in the direction
of compensating pupil parameter mismatch for location errors. Also, intensity ellipticity was introduced due to the
PUPICOM Spokes.
Figure 9. The comparison of the extracted Pupil parameters before and after matching using pupil fit model.
The effect of CD variation due to Pupil parameter change is shown in Figure 10. In the graph, the simulated matching
CD difference is compared to the each parameter’s independent contribution to pattern CDs. Although the most
dramatic changes of pupil parameters are location of dipole and location error of dipole, their contributions to matching
performance are minimal. Horizontal-vertical intensity ellipticity, which caused x-axis dipole thinning in Figure 8 (c), is
the dominant factor in improving the scanner matching. The parameter value of intensity ellipticity has been decreased
after applying pupil calibration. As the ellipticity value decreased, horizontal pole intensity of a pupil shape has
relatively diminished. It brings the result that CDs of horizontal patterns shrank and CDs of vertical patterns grew as
shown Figure 11.
Figure 10. The pupil parameters’ contribution to the matching performance. (Each calibrated parameter is independently
applied to pupil fit model)
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Figure 11. Simulated CD variation trend due to intensity ellipticity change.
4. PROCESS WINDOW VERIFICATION
One of the most important aspects in scanner matching process is to preserve or improve process window of the TBM
scanner. It is not acceptable to reduce scanner mismatch with price of reducing process margin for high volume
production. The weakest pattern under defocus condition was selected as a test pattern to evaluate process margin
impact. The test pattern having target CD of 70nm which is not included in sampling patterns for model calibration is
selected to evaluate the process margin of post-matching illumination condition. It is commonly understood that DOF
margin is in inverse proportion to the square of increasing amount of NA by simple Rayleigh equation. 6 The variable
range of the regular tuning knobs, NA, can be limited by the margin degradation.
Table 3 lists calculated process window variation by applying sub-recipe without PUPICOM to TBM scanner for
matching compared to reference scanner. At a little sacrifice of energy latitude margin, depth of focus margin has
slightly increased after the matching process. The simulated and measured focus margin is shown in Figure 12. The real
source map had been measured from reference scanner and post match TBM scanner. Each of the calibration models
can be generated from the two measured source maps. The simulated process window results using previously
calibrated models are shown in Figure 12 (a), and the numbers in the table indicate defect count for bridging and
necking under defocus conditions. The measured process window in Figure 12 (b) shows good agreement with the
model prediction.
Table 3. Calculated process window for sub-recipe without PUPICOM Spokes.
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(a) Simulated process window (b) DOF margin comparison before and after scanner matching
Figure 12. Simulated and exposed process window. (The numbers indicate defect count for bridging and necking)
5. Summary
As a result of model based matching process in this test case, scanner mismatch between ASML XT:1900Gi and ASML
NXT:1950i has been improved by about 55% in RMS for the manually and automatically selected 191 test patterns in
sub 30nm technology node for memory devices. The simulated matching performance is in-line with the wafer
measurements. The CD variation trend corresponding to NA and ring width change is analyzed. NA sensitivity of the
test patterns is related to average duty ratio of the patterns, and it is found that the surrounded pattern density as well as
its own duty ratio can affect the NA sensitivity. Also, the DOF margin of the TBM scanner improved after matching and
was verified with simulation and on wafer.
Pupil parameters were extracted and compared from the measured source map from reference scanner and the prematched
and post-matched TBM scanner. The individual effect of pupil parameters on pattern CDs was studied with
simulation. The scanner mismatch was reduced either by changing NA and Sigma or by changing the intensity
ellipticity of the source using PUPICOM Spokes.
6. ACKNOWLEDGMENTS
The authors would like to gratefully acknowledge ASML for their supports in evaluation for new version of scanner
matching tool.
7. REFERENCES
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Reliance on CD Metrology”, SPIE Vol. 7640, 764014,(2010)
2. Tsung-Chih Chien., et al “Model-based scanner tuning for process optimization”, SPIE Vol. 7520,(2009)
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3. Teahyeong Lee., et al “Hot spot management through design based metrology –Measurement and filtering-”,
SPIE Vol. 7520,(2009)
4. Cheol-kyun Kim., et al “The impacts of scanner modeling parameters for OPC model of sub-40nm memory
device”, SPIE Vol. 1111,(2010)
5. Jim-hyuck Jeon., et al “Analysis of the impact of pupil shape variation by pupil fit modeling”, SPIE Vol.
7640,(2010)
6. Liang Zhu., et al “Depth of focus enhancement for sub-110-nm technology by using KrF double-exposure
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