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On the road to automated production
workflows in the back end of line
Gilles Tabbone, Kokila Egodage, Kristian Schulz, Anthony
Garetto
Gilles Tabbone, Kokila Egodage, Kristian Schulz, Anthony Garetto, "On the
road to automated production workflows in the back end of line," Proc. SPIE
10775, 34th European Mask and Lithography Conference, 107750N (19
September 2018); doi: 10.1117/12.2326908
Event: 34th European Mask and Lithography Conference, 2018, Grenoble,
France
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On the road to automated production workflows in the back end of
line
Gilles Tabbone, Kokila Egodage, Kristian Schulz, Anthony Garetto
Carl Zeiss SMT, Carl-Zeiss-Promenade 10, 07745 Jena, Germany
1. ABSTRACT
The technical roadmap adopted by the semiconductor industry drives mask shops to embrace advanced solutions to
overcome challenges inherent to smaller technology nodes while increasing reliability and turnaround time (TAT). It is
observed that the TAT is increasing at a rapid rate for each new ground rule [1, 2]. At the same time, productivity and
quality must be ensured to deliver the perfect mask to the customer. These challenges require optimization of overall
manufacturing flows and individual steps, which can be addressed and improved via smart automation [2].
Ideally, remote monitoring, controlling and adjusting key aspects of the production would improve labor efficiency and
enhance productivity. It would require collecting and analyzing all available process data to facilitate or even automate
decision-making steps. In mask shops, numerous areas of the back end of line (BEOL) workflow have room for
improvement in regards to defect disposition, reducing human errors, standardizing recipe generation, data analysis and
accessibility to useful and centralized information to support certain approaches such as repair. Adapting these aspects
allows mask manufacturers to control and even predict the TAT that would lead to an optimized process of record.
Keywords: AIMS, MeRiT, review, repair, SEM, automation, FAVOR, BEOL
2. INTRODUCTION
One of the major challenges in the BEOL is having to constantly adapt to the varying levels of complexity especially
when repairing masks. This is a critical aspect since a poorly executed repair can quickly lead to several
repair/verification cycles and drastically increase the TAT. In fact, the TAT tends to explode exponentially with an
increase in complexity mainly due to two causes: 1) more difficult defect types needing repair, 2) larger number of
masks being produced. As a solution, it has been shown that introducing automation to the current workflow can have an
attenuating effect on the explosive behavior of the TAT [3]. Moreover, including complete automation may require
drastic changes to already established manufacturing workflows. Thus targeting specific sections of the BEOL can still
have a significant impact on reducing the overall TAT [4].
A cost productive theoretical model [3] has been previously established to quantify the overall turnaround time of masks
along with the probability of an error occurring during the manufacturing process. Such a model allows identifying
process segments that have the potential for automation and benefit from increased productivity. Using this model, it is
possible to calculate the theoretical effect of automation on TAT reduction depending on the difficulty of repair. Five
situations, a to e, were considered where the defect mix and difficulty of repair are gradually increased. Turnaround
times are calculated for both ‘no automation’ and ‘full automation’ cases and the results are summarized below in Table
1. The immediate effects prompted by defects labelled ‘difficult’ to repair on the TAT variation are due to the lack of
automation whereas the use of automation enables the containment of TAT resulting in a dampening behavior (Figure 1).
34th European Mask and Lithography Conference, edited by Uwe F.W. Behringer,
Jo Finders, Proc. of SPIE Vol. 10775, 107750N · © 2018 SPIE
CCC code: 0277-786X/18/$18 · doi: 10.1117/12.2326908
Proc. of SPIE Vol. 10775 107750N-1
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Scenario
Simple A
Defect Count
Simple B Complex Difficult No Auto.
TAT`
Full Auto. Reduction
a 1 1 1 0 21.3 16.8
21.30%
b 8 5 2 1 46.8 35.8
23.40%
c 8 5 3 1 58.8 37.1
36.80%
d 10 7 4 2 91.4 50.2
45.10%
e 12 8 6 4 209.1 75.3
64.00%
* [time unit]
250
TAT -No Automation
200
O TAT - Full Automation
Expon. (TAT -No
Automation)
Linear (TAT - Full
Automation)
150
100
50
a b c
Defectivity Complexity
d e
Table 1: Influence of scenarios a to e on the TAT, without and with automation
Figure 1. Comparing TAT evolution vs Complexity without and with full automation
In addition to the theoretical model, a real world example [2] shows a similar trend. However, this model follows the
increase in mask load (incoming masks to the BEOL) and its impact on the turnaround time [4]. Russell et al.
demonstrated the different contributions of system automation by utilizing AIMS
TM
AutoAnalysis (AAA) in the
production environment. AAA performs an automated analysis of AIMS
TM
images via a direct tool connection. The time
savings are illustrated by comparing a manual to an automated workflow which clearly shows the impact on cycle time.
This specific case addresses the complexity in terms of the increasing number of mask load. Similar to the previous
example, the TAT increases exponentially without automation. With automation, however, the TAT variability can be
limited to a controllable level.
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4.0
3.5
3.0
2.5
1.0
0.5
0.0
- - - - Manual - - AAA
1 0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Masks per 12 hour shift
9.0 10.0 11.0 12.0
Figure 2: Turnaround time per mask as a function of masks per shift. Using AAA, the TAT only slightly increases with
increasing number of masks per time.
Both theoretical and practical models demonstrate the positive effects of automation. Whether it is a question of
controlling the effects of a sensitive defect or increasing the speed of data analysis while standardizing analysis methods,
automation acts as the variable that minimizes the TAT. A preliminary conclusion allows us to assume that such benefits
can extend to all segments of the BEOL, provided that recipes, results, analysis and decisions can be expressed in digital
form. This paper focuses on various segments of the BEOL and introduces an automation strategy to reduce TATs,
standardize results and improve data reviewing capability.
3. METHODOLOGY & RESULTS
A full repair workflow is performed using a programmed defect mask (PDM) in order to demonstrate the benefits that
software based automated solutions can provide to manufacturers. The tools utilized for the procedure are standard repair
and review tools such as MeRiT
®
and AIMS
TM
. Software applications under the FAVOR
®
brand, such as the aerial
image evaluation software, AAA, and the SEM image evaluation software, SEM AutoAnalysis (SAA), are adapted to
support the different steps of the mask production workflow. These applications conduct image evaluation in parallel to
image capture. Tool activity, applications, masks and defects are tracked throughout the repair process by the
overarching manufacturing enterprise solution, Advanced Repair Center (ARC) that centralizes all process data and
supports quick decision-making. The sequence implemented is a possible and realistic workflow whereas customization
is possible as well.
The starting point of the sequence is a mask inspection report that identifies points of interest on the mask. Thus, the
defectivity status will be known. Five locations on the PDM have been chosen from the design and integrated in an
inspection defect file to mimic an inspection step. While keeping the mask inside the MeRiT
®
tool the newly confirmed
defects are repaired. The mask is then transferred to an AIMS
TM
system that images the repaired defects followed by a
thorough analysis performed by AAA. This produces a detailed report of the repair quality and qualifies the defects with
respect to specifications. In the meantime, all the processes and results are tracked by ARC, which automatically records
the activity of the mask, tools as well as the measurement data and enables quick decision-making. An overview of the
workflow is summarized in Figure 3.
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Figure 3:
P
3.1 Trackin
ARC establi
s
measuremen
t
five location
s
and post-rep
a
AAA results.
to the BEOL
3.2 SEM Di
s
In our case,
S
as a standalo
n
process. SA
A
fail/pass stat
e
the same ima
images are e
x
defined [6].
The five coo
images are c
a
analyzed by
According to
roposed work
the workfl
hes direct c
data. The d
s
). ARC will t
h
ir SEM imag
Acting as a
h
where the da
t
s
position
S
EM images
w
e applicatio
could be se
ment. SAA
ge (single im
x
tracted, ove
r
rdinates in t
a
ptured from
SAA. CD de
the sequence
f
low with relati
v
o
w
o
nnections to
fect tracking
en track the
es generated
ub, all the d
a can be revi
w
ere generate
d
n
. The ‘points
n as an integ
as the ability
ge analysis)
r
laid and corr
e
e Inspection
those five lo
viation is th
above, we o
b
v
e systems and
tools and a
will begin w
mask throug
y the MeRi
a
ta are stored
i
e
we
d
.
by a MeRi
of interest’ d
e
ated solution
to perform a
r from anot
lated before
Defect File
ations inclu
parameter u
b
tained the fo
l
applications.
plications to
th the loadi
out its path a
T
®
system as
w
n one place t
T
®
, which was
livered by t
to the MeRi
T
comparison
er image kno
defects are a
s well as the
ing correspo
der investig
lowing result
track their
g of an insp
ross the BE
w
ell as AIMS
T
give operat
acting as a d
i
e inspection
T
®
system, w
h
b
etween two i
m
n to have n
tomatically
PDM are lo
ding referen
tion. Specifi
s
(Figure 4).
lobal activit
ction defect
O
L collecting
i
T
M
images fol
l
rs and engin
sposition too
d
efect file are
h
ich would gi
v
m
ages i.e. a r
e
o
defec
t
s. Crit
i
dentified an
ded simulta
c
e images, w
h
cations are g
and genera
ile (in our c
nspection rel
l
owed by the
e
ers a single
p
combined
run through
v
e fast and dir
e
ference ima
g
i
cal contours
assessed ac
n
eously into
M
h
ich are then
i
ven at 5% (
v
t
es meta- an
d
se, it contain
ated data, pre
correspondin
oint of acces
ith SAA, use
he dispositio
ect access to
e, either fro
from the SE
c
ording to pre
M
eRiT
®
. SE
M
automaticall
s Reference
d
s
-
g
s
d
n
a
m
M
-
M
y
)
.
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Figure 4: I
m
Identificat
i
From the pr
e
labelled the
r
operator wo
u
locations an
d
direct conne
c
3.3 Repair
Repairs carri
e
vertical exte
n
geometry wa
ensures the a
u
age of the co
i
on of the defe
c
e
vious image
s
egion as a d
u
ld know that
they all hav
c
tion to ARC
i
d out on the
n
sions and th
s used to per
f
tomatic tran
tour extractio
c
tive area (d).
s
(a, b, c, d)
fect. This i
this ‘
p
oint o
f
been labele
s to be imple
m
ask were
pe
e mask mate
r
f
orm repai
r
s
o
s
fer of genera
t
of the referen
it can
b
e see
n
formation ca
f
interes
t
’ is
n
defective b
m
ented.
rformed by t
r
ial is MoSi.
o
n the defects
t
ed images to
e image (a) a
n
that SAA
h
n
then be su
m
ow consider
y
SAA. Full
i
h
e MeRiT
®
to
A standard
M
(Figure 5).
D
the applicati
d the defect i
as detected
m
marized to
a
d a defect.
ntegration o
ol previously
oSi etch re
D
irect connec
t
o
n after each
r
age (b). Image
t
he extension
result statu
his procedur
f
SAA with
M
used for disp
o
c
ipe, optimiz
e
t
ion between
M
epair has bee
of the correlat
b
etween the
displayed a
was repeate
M
eRiT
®
syste
m
sition. The
d for the m
M
eRiT
®
syst
e
n
completed.
d contours (c).
two lines an
‘FAIL’. Th
with all fiv
m
s along wit
h
fi
ve defects ar
e
terial and th
e
ms and AR
C
d
e
e
h
e
e
C
,
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CI
0.462
0.400
4000 -
I
0.035
0.030
2000 -
0.300
0.020
E
0-
E
0.200
-2000 -
0.010
0.100
-4000 -
0.039 0.000
-4000 -2000 0 2000 4000
nm
-4000 -2800
nm
2000
-
4000
Figure 5. Repair workflow: Defect being processed by a MeRiT
®
system. From left to right, In-Field-of-View pattern copy
creation, creation of shape to be repaired, post-repair image. The scale bars in all images are 2µm.
It should be mentioned that the goal of the repair step is not to realize a perfect repair, but to generate data that can be
tracked automatically. The five repairs yielded pre- and post-repair images that were tracked and displayed by ARC.
3.4 Review
Review was carried out jointly by an AIMS
TM
system and AAA. Both tool and application benefit from a direct
connection that enables a full integration of the solution in a production workflow. Mask measurements performed by the
AIMS
TM
tool were automatically analyzed by AAA as soon as they were generated. The AAA recipe was configured so
that a 4000×4000μm region of interest centered on the repair area was evaluated. Any deviation in CD value above 5%
relative to a reference would be shown in red. In our case, the analysis shows no deviation from the given specifications
(Figure 6).
Figure 6. Post-repair AIMS
TM
defect image with the ROI overlaid. The analysis shows no deviation shown in green (left) and the
difference image (right).
3.5 Tracking defects
All results were automatically communicated to ARC for review purposes. Figure 7 below shows a defect tracking
window where the five repaired defects are listed chronologically along with the relevant data in adjacent columns such
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b
q o
-loco
E ®
®0C
imo
as X and Y
c
results. The
A
yielded three
and ID#5).
Figure 7.
T
The data set
f
Each tile is
s
AIMS™ ima
g
Figure 8.
T
configura
b
(a) SEM P
r
As the o
p
era
t
with the cont
e
oordinates,
AA column
passes and t
T
he ARC Defe
c
rom the Def
eparately co
g
es are set in
he Data Revie
le review whil
r
e-Repair imag
t
o
r
goes throu
g
e
n
t
s of the co
r
efect classifi
displays the
o fails. For t
t Tracking wi
ct Tracking
figured so t
one panel.
panel displa
going throug
e, (b) SEM Po
h the defect
responding
cation, repai
r
review of th
e
e operator, t
dow chronolo
anel can be d
e layout ca
s data made a
the list of pro
s
t-Repair imag
e
list displayed
d
efec
t
s, allow
i
information,
repair. One
e next step
ically displays
isplayed and
reflect the
ailable throug
c
essed defects.
e
, (c) AIMS™
R
in the Defect
ng a fast revi
AIMS™ me
can see that
ould be to tr
the workflow.
rranged acc
n
eeds of the
o
the Defect Tr
On this examp
l
eference ima
Tracking pa
w of both M
asuremen
t
s a
n
his attempt
y
and repai
r
t
h
rding to a co
perator. In
cking panel a
e, one can see
g
e, (d) AIMS™
el, the Data
e
RiT
®
and AI
d the corres
t repairing t
e two failed
n
figurable til
e
ur case, bot
n
d allows a qui
c
a possible layo
Defect image.
R
eview panel
i
MS
TM
results
.
onding AA
e five defect
ttempts (ID#
e
-based layou
t
h
MeRiT
®
an
d
c
k and
u
t of 4 items:
i
s updated
.
A
s
1
t
.
d
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BEOL segment
Defect disposition
Repair
Review
Workflow
To dispositio
McRiT
AI MST"
AI MST" AutoAn alysi s
7 data
V data /
7 data
/
Advanced
Repair
Center
V data /
ly data /
J data /
4. CONCLUSION
Our experiment
followed a predefined pattern similar to that in a production environment and that each section requires a
decision from an operator or an engineer. The workflow used on this paper can be summarized by the flowchart
shown below (Figure 9).
Figure 9. Flowchart
representing the workflow undergone by the PDM.
This partly automated BEOL segment demonstrates the effective data reviewin
g capability achieved by the use of
sophisticated software applications and tool connections. For the purpose of our example, the BEOL has been
divided into three distinct segments: defect disposition, repair and review (Figure 9). Each of these segments can
benefit from a dedicated digital solution: in the case of defect disposition SAA automates the image analysis process
whereas AAA the review segment. These are specific solutions that allow standardization, reliability and speed for an
overall automation. In the second step, the use of a global solution such as ARC allows tools, applications and
data generated in each segment to be collected, linked and correlated. This gives the operator an overview of the
BEOL within a few clicks. Moreover, being able to gather results, process parameters, predicting outcomes, deter
delicate repair situations and generally speed up the handling of the defectivity provides a higher level of control over
the repair processes. By having an overall overview followed by comprehensive reporting information can be reviewed
without any inconveniences.
REFERENCES
[1] The Mask Maker Survey 2017, eBeam Initiative.
[2] M. Malloy, „Mask Industry Survey” Proc. SPIE 8880, Photomask Technology 2013, 88800K, Monterey,
California, 2013.
[3] K. Schulz, K. Egodage, G. Tabbone, A. Garetto, "Improving back end of line productivity through smart
automation", Proc. SPIE 10451, Photomask Technology.
Proc. of SPIE Vol. 10775 107750N-8
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[4] A. Watts, C. Huang, Y. Wang, C. Huynh, C. Benson, A. Smith, „Photomask automation improvement to
eliminate manufacturing errors”, Proc. SPIE 6283, Photomask and Next-Generation Lithography Mask
Technology XIII, p. 628303, 2006.
[5] Russell G., Jenkins D., Goonesekera A., Dornbusch K., Sargsyan V., Zachmann H., Buttgereit U., "Automated
defect disposition with AIMS AutoAnalysis," Proc. SPIE 10451, Photomask Technology, 1045113, 2017.
[6] Schulz K., Egodage K., Tabbone G., Ehrlich C., Garetto A., "SEM AutoAnalysis: enhancing photomask and NIL
defect disposition and review," Proc. SPIE 10446, 33rd European Mask and Lithography Conference, 104460I,
2017.
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