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Increasing Learning Rate
On Copper Processes
by Jeff Lin, Yield Enhancement Engineer, Motorola, APRDL
In the yield-learning phase of a new
process, such as copper dual damascene,
one of the challenges is efficient defect
detection. A novel statistical method for
optimization of copper inspections and
sampling strategies was introduced which
facilitated increased yield learning rates on
the copper process at Motorola APRDL.
KLA-Tencor 2138 inspections on copper
processes were initially optimized for maximum sensitivity to all defect types. As
understanding of copper grew, the tendency for non-killer defects to outnumber
killer defects became evident. Because a
random sample of defects was sent on for
further review, the percent killer defects in
the sample was important. Looking
beyond the standard methods for increasing the percent killer defects captured in
the sample was one of the keys to bringing
the copper process to yield quickly.
Killer defects are those most likely to compromise the functionality of the device.
They include large particles, bridging
defects, missing patterns, residues, corro-
sion, and defects of unknown origin or
composition. Non-killer defects are those
less likely to affect a device, such as small
defects, small particles, polish slurry, color
variation, and other nuisance defects.
Examples of typical killer and non-killer
defects for copper processes are shown in
figures 1 and 2.
The yield enhancement tool set included a
KLA-Tencor 2138 inspection system with
IMPACT/Online ADC. Data was downloaded and analyzed using the Klarity
analysis system.
Methodology
There are a lot of methods available to
reduce the capture rate of non-killer
defects on the 2138, including raising the
sensitivity threshold, filtering out smaller
defects, and using a larger pixel size to
lower the system’s resolution. Image
smoothing through filters could reduce the
non-killer defect count, as could using a
tuned segmented auto-threshold (SAT)
inspection. ADC could be used to sort nuisance defects; then the sample could be
a
b
c
a
b
d
e
f
c
d
F i g u re 1. Killer def ect exampl es fr om Motorola APRDL’s copper process. In p icture 1a,
F i g u re 2 . Non-kill er defect examples from Motor o l a
a la rge particl e has shorted s evera l m etal l ines. Picture 1b shows a scratch. Picture s
A P R D L’s copper process. Pictur e 2a sh ows s lurry
1c an d 1f are exa mples of residue defe cts. Picture 1d shows a section of missing
res idue. Picture 2 c shows a small parti cle out in the
metal. Picture 1e shows brid gin g of a few metal lines.
fi eld area. Pictures 2 b and 2d are color variation .
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F
Parameter Set #
Relative Sensitivity
Settings
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A T
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S
Size Sieve
1
high
SAT, not filtered, low threshold
no
2
medium/high
SAT, filtered, low threshold
no
3
medium
SAT, not filtered, high threshold
no
4
medium/low
Fixed, medium threshold
yes
5
low
Fixed, high threshold
no
Table 1. Details of the five parameter sets used to optimize for killer ratios.
taken from the non-nuisance bins.
Motorola APRDL decided to try a novel
statistical approach to determine which
method or combination of methods was
optimal, taking the sample plan and
inspection time into consideration.
Four metrics were used to evaluate the
effectiveness of an inspection:
• The percent killer defects detected, or
killer sensitivity, measures the percentage
of killer defects detected by the inspector,
referenced to the number of killer defects
known to be on the wafer. For example, if
a 2138 scan found 40 killer defects on a
wafer with 50 known killers, the killer
sensitivity would be 80 percent. The
number of known killers is determined by
scanning the wafer using a significantly
more sensitive inspection technology.
Motorola APRDL chose to use the
SEMSpec as the standard.
used for comparison to the 2138 inspection
data collected later in the experiment. A
SEMSpec was chosen as the reference
inspection technology because its scanning
electron microscope-based technology provides significantly more sensitivity than
that provided by the optical-based 2138.
F i g u re 3A. Metal 2: killer/non-kill er d efects by work week. Green bars re p resent normalized def ect counts o f the samp led non -killers, while orange bars are the kill ers.
The improvement in s ampl ing of killers is si gnificant aft er the new method was imple-
• The killer-to-total ratio, or signal-to-noise
ratio, calculates the ratio of killers found to
the total number of defects detected by the
inspector. For example, if the 2138 found
that 40 defects out of a total of 100 were
killers, the signal-to-noise ratio would be
40 percent.
• The killer normalized defect density extrapolates the killer defect density of the sample over the entire population of defects
found on the wafer.
• The fourth measurement is the inspection
time, including wafer handling, alignment,
scanning and ADC image collection time.
The first step was to collect reference data
to determine the number of “known” killer
defects on the wafer. This data set would be
mented on the Metal 2 wafer.
Thus, a SEMSpec was used to scan a Metal
2 copper wafer and determine how many
detected defects were killers. The assumption was that the SEMSpec caught all the
killers that the 2138 would have been able
to find, and more; any defects unique to
the 2138 scan were assumed non-killer.
The next step involved setting up multiple
inspections on the 2138, with varying sensitivity parameters. Five sets of scans were
performed each day with four different
pixel sizes, for a total of 20 scans. Table 1
shows the details of the five recipe settings.
Under the assumption that a large defect
has a higher probability of being a killer
defect, the goal was to quantify the tradeAutumn 1999
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F i g u re 3B. Metal 2: percen t killers in sample. Th e
i m p rovement i n sampling of killers after th e new
method is introduced is more evident when per c e n t
kill ers i n the samp le is plotted for the Meta l 2
w a f e r.
off between reduced scan time and sensitivity so that the largest pixel size (shortest
scan time) providing adequate defect capture could be used. In setting up the 2138
inspections, the origin marks were chosen
to duplicate the origin marks on the
SEMSpec scan. Inspection comparison percentages were calculated by overlaying the
2138 data on the SEMSpec data using the
Defect Source Analysis algorithm on
Klarity.
Results from a comparison of the five scan
types, for each of the four pixel sizes,
emphasized that optimizing a 2138 for
high sensitivity to all defects — the old
method — does not provide the highest
ratio of killer to nuisance defects (signalto-noise ratio). A compromise in overall
sensitivity can create a detected defect population that more accurately reflects the
entire killer defect population on the
wafer. After the 20 scans indicated which
inspection was best suited to inspection of
the Metal 2 wafer, the scan recipe was
refined further using standard recipe optimization procedures, to improve killer
ratio and capture rate.
Results
F i g u re 4A. Metal 3: killer/non-killer defects by
work week. Green b ars re p r esent no rm a l i z e d
defect counts of the sampl ed non-killers , while
orange ba rs are th e kil lers. The impr ovement in
sampling of killers is evident for Meta l 3, alth ough
not as large as for the Metal 2 wafer.
Figure 3A shows the improvement in percent killers found within the defect sample
after implementation of the method
described above, for a Cu Metal 2 layer.
Generated from the Klarity pareto charts,
this chart uses green bars to represent normalized defect counts of the sampled nonkiller defects, while the orange bars represent normalized defect counts of sampled
killers. The bias towards sampling of nonkillers is evident in the data taken before
the new methodology was applied. After
the new method was implemented, a higher percentage of killers was detected.
Figure 3B shows the data in terms of percent killers found within the sample plan.
The improvement in percent killers sampled is even more visible.
F i g u re 4B. Metal 3: percent kil lers in sa mple. The
i m p rovement in sampling of kil lers after the new
method is introduced is sm all but significa nt f or the
Metal 3 wafer.
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When this method was applied to other
layers, improvement in percent killers
detected was demonstrated further. Metal
3 zone data, shown in figure 4A, shows a
clear rise in sampling of killer types after
implementation of the new method. In
terms of percent killers found within a
sample (figure 4B), the change for Metal 3
was not as noticeable as with Metal 2; the
sampling of killers and non-killers tended
to be more consistent. This can probably
be attributed to the higher probability of
detecting defects from previous levels at
higher metal levels.
Results from the Metal 5 zone again show
some improvement after implementation of
the new method (figure 5A). For Metal 5
the change in percent killers is more noticeable than for the Metal 3 zone (figure 5B).
Summar
y
F i g u re 5A. Metal 5: killer /non-killer d efects by
work week.
Green b ars re p resent no rm a l i z e d
defect count s of the s ampl ed non-killers, while
orange bars are th e killers. Th e improvement in
samp lin g of killers is clearly demonstrated for
At Motorola APRDL, a new methodology
for increasing capture and sampling of
killer defects has been developed and tested. First, a reference data set was determined; Motorola APRDL chose to use a
SEMSpec scan as the reference. Then multiple inspection setups were created for the
2138, with various sensitivities, and various wafers were scanned using these parameters. The 2138 data was overlaid to the
SEMSpec data by using the Defect Source
Analysis algorithm on Klarity, with a userdefined tolerance radius. Killer sensitivity
and signal-to-noise ratios were generated.
After the best inspection recipe was chosen
it was refined further to improve killer
ratio and capture rate.
This study demonstrated that using a statistical approach allows the user to choose
optimal inspection parameters for
increased sensitivity to killer defects. In
particular, Motorola APRDL found that
implementation of this method increased
the yield learning rate on new copper
processes. Wafer review and data analysis
became more productive, and killer defect
density paretos became more accurate and
understandable — especially valuable
improvements in an R&D environment
such as APRDL. ❈
Meta l 5.
F i g u re 5B. Metal 5: percent killers in sa mple. The
i m p rovement in sampling of killers after the new
method is introduced is demonstrated for the Meta l
5 waf er.
cir cle RS#039
This paper was first presented at KLA-Tencor’s Yield
Management Solutions Seminar at SEMICON/West in July
1999. It was edited for this publication by Rebecca
Howland Pinto, Ph.D., in KLA-Tencor’s WIN Division.
F i g u re 6. I nspection Optm ization Methodol ogy
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