I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
42
,
No
.
1
,
A
p
r
il
20
26
,
p
p
.
30
~
39
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
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1
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cs
.v
4
2
.
i
1
.
pp
30
-
3
9
30
J
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na
l ho
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a
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e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
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co
m
Power
-
a
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g
n
-
for
-
tes
t:
a sur
v
ey
of
DFT
techni
ques a
nd
sca
n chain reo
rde
ring
appro
a
ches
V.
Ra
j
it
ha
Ra
ni
1
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a
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s
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ig
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a
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i
q
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e
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a
n
d
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ra
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K
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w
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r
d
s
:
Desig
n
-
f
o
r
-
test
Min
im
u
m
s
p
an
n
i
n
g
tr
ee
R
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f
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m
en
t le
ar
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T
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CC B
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SA
li
c
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se
.
C
o
r
r
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s
p
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A
uth
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r
:
V.
R
ajith
a
R
an
i
Dep
ar
tm
en
t o
f
E
lectr
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n
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d
C
o
m
m
u
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in
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r
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,
J
NT
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alo
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I
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d
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m
ail:
r
ajith
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an
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u
@
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m
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co
m
1.
I
NT
RO
D
UCT
I
O
N
W
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o
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s
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v
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n
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s
in
s
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o
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n
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(
C
MO
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in
teg
r
ated
cir
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its
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s
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o
w
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s
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Simu
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as
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m
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C
d
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f
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ates
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s
h
if
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ca
p
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e.
Du
r
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f
ac
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an
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test
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.
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test
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to
r
s
ar
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if
ted
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to
th
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p
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th
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s
ca
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f
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as f
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ca
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esp
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p
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f
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r
s
ev
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al
r
ea
s
o
n
s
[
1
]
:
(
i)
lar
g
e
p
o
r
tio
n
s
o
f
s
ca
n
l
o
g
ic
th
at
r
em
ain
in
ac
tiv
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i
n
f
u
n
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al
m
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t
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g
le
h
ea
v
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d
u
r
in
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s
ca
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s
h
if
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g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
o
w
er
-
a
w
a
r
e
d
esig
n
-
fo
r
-
test
:
a
s
u
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ve
y
o
f d
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iq
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es a
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d
s
ca
n
ch
a
in
r
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d
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…
(
V
.
R
a
jith
a
R
a
n
i
)
31
[
2
]
,
(
ii)
h
ig
h
s
h
if
t
f
r
eq
u
e
n
cies
ar
e
ty
p
ically
u
s
ed
to
r
ed
u
ce
te
s
t
ap
p
licatio
n
tim
e,
an
d
(
iii)
p
ar
allel
ac
tiv
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o
f
m
u
ltip
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s
ca
n
c
h
ain
s
f
u
r
th
er
i
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cr
ea
s
es
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witch
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ac
tiv
ity
.
A
s
a
r
esu
lt,
b
o
th
av
e
r
ag
e
an
d
p
ea
k
test
p
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wer
m
u
s
t
b
e
ca
r
ef
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lly
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n
tr
o
lled
.
T
h
e
in
d
u
s
tr
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co
m
m
o
n
ly
em
p
lo
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s
s
ev
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al
tech
n
iq
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to
k
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p
th
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test
p
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with
in
s
af
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lim
its
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alth
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u
g
h
ea
ch
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tr
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d
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s
p
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n
a
lties
in
ter
m
s
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f
co
s
t,
test
tim
e,
o
r
h
ar
d
war
e
o
v
er
h
ea
d
.
−
I
n
cr
ea
s
in
g
th
e
p
o
wer
s
u
p
p
ly
,
p
ac
k
ag
in
g
,
o
r
co
o
lin
g
ca
p
a
city
:
T
h
is
ap
p
r
o
ac
h
m
itig
ate
s
ex
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s
s
iv
e
test
p
o
wer
b
u
t sig
n
if
ican
tly
in
c
r
ea
s
es h
ar
d
war
e
an
d
test
in
f
r
astru
ctu
r
e
co
s
ts
[
3
]
,
[
4
]
.
−
Par
titi
o
n
in
g
th
e
d
esig
n
an
d
tes
tin
g
o
n
e
r
eg
io
n
at
a
tim
e:
T
h
is
r
ed
u
ce
s
in
s
tan
tan
eo
u
s
s
witch
in
g
ac
tiv
ity
an
d
p
r
eser
v
es
th
e
d
etec
tio
n
o
f
d
y
n
am
ic
f
au
lts
,
b
u
t
it
r
e
q
u
ir
es
a
d
d
itio
n
al
h
a
r
d
war
e
s
u
p
p
o
r
t,
i
n
cr
ea
s
es
th
e
test
d
u
r
atio
n
,
an
d
lea
d
s
to
h
ig
h
er
o
v
er
all
en
er
g
y
co
n
s
u
m
p
tio
n
[
5
]
,
[
6
]
.
−
R
ed
u
cin
g
th
e
test
f
r
eq
u
en
c
y
:
L
o
wer
in
g
th
e
f
r
e
q
u
en
c
y
d
ec
r
ea
s
es
th
e
in
s
tan
tan
eo
u
s
p
o
wer
b
u
t
p
r
o
p
o
r
tio
n
ally
in
cr
ea
s
es
th
e
test
tim
e
an
d
m
ay
m
ask
ce
r
tain
d
y
n
am
ic
f
au
lts
,
r
ed
u
cin
g
th
e
d
ef
ec
t
co
v
er
a
g
e
[
7
]
.
I
n
co
n
tr
ast
to
p
r
io
r
lo
w
-
p
o
wer
d
es
ig
n
-
f
o
r
-
test
ab
ilit
y
(
DFT
)
s
u
r
v
ey
s
th
at
b
r
o
a
d
ly
class
if
y
s
c
an
p
o
wer
r
ed
u
ctio
n
tech
n
iq
u
es,
th
is
p
a
p
er
p
r
esen
ts
a
f
o
c
u
s
ed
an
d
u
n
if
ied
tr
ea
tm
en
t
o
f
s
ca
n
ch
ai
n
o
p
tim
izatio
n
an
d
r
eo
r
d
er
i
n
g
as
a
ce
n
tr
al
s
tr
ate
g
y
f
o
r
s
h
if
t
-
p
o
wer
r
ed
u
ctio
n
.
I
t
in
teg
r
ates
class
ical
p
atter
n
r
eo
r
d
e
r
in
g
,
g
r
ap
h
-
b
ased
m
eth
o
d
s
(
MST
/TSP)
,
m
etah
eu
r
is
tics
,
an
d
em
er
g
in
g
m
ac
h
in
e
lear
n
in
g
an
d
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
ap
p
r
o
ac
h
es
with
in
a
s
in
g
le
co
m
p
ar
ativ
e
f
r
a
m
ewo
r
k
.
T
h
e
s
u
r
v
ey
f
u
r
th
er
h
i
g
h
lig
h
ts
q
u
an
titativ
e
p
o
wer
-
r
ed
u
ctio
n
tr
e
n
d
s
,
test
-
tim
e
im
p
li
ca
tio
n
s
,
an
d
f
au
lt
-
c
o
v
e
r
ag
e
p
r
eser
v
atio
n
,
o
f
f
er
in
g
p
r
ac
tical
in
s
ig
h
ts
ap
p
licab
le
to
au
to
m
atic
test
p
atter
n
g
en
er
atio
n
(
AT
PG
)
-
b
ased
,
b
u
ilt
-
in
s
elf
-
test
(
B
I
ST
)
-
b
ased
,
an
d
m
ix
e
d
-
m
o
d
e
test
en
v
ir
o
n
m
en
ts
.
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws.
Sectio
n
2
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e
liter
atu
r
e
s
u
r
v
ey
,
s
u
m
m
ar
izin
g
m
aj
o
r
a
d
v
an
ce
m
en
ts
in
lo
w
-
p
o
wer
DFT
tech
n
iq
u
es
an
d
r
ev
iewin
g
s
tate
-
of
-
t
h
e
-
ar
t
m
eth
o
d
s
f
o
r
r
ed
u
cin
g
test
-
m
o
d
e
p
o
wer
.
Sectio
n
3
p
r
o
v
id
es
a
d
etailed
r
ev
iew
o
f
s
ca
n
-
ch
ai
n
o
p
tim
izatio
n
r
esear
ch
,
in
clu
d
in
g
class
ical
alg
o
r
ith
m
s
,
h
eu
r
is
tic
ap
p
r
o
ac
h
es,
an
d
r
e
ce
n
t
m
ac
h
in
e
-
lear
n
in
g
–
b
ased
s
o
lu
tio
n
s
.
Sectio
n
4
d
is
cu
s
s
es
th
e
R
esu
lts
an
d
h
o
w
s
ca
n
-
ch
ain
r
e
o
r
d
er
i
n
g
is
an
ef
f
ec
tiv
e
lo
w
-
p
o
wer
DF
T
s
tr
ateg
y
.
Fin
ally
,
s
ec
tio
n
5
co
n
cl
u
d
es
th
e
p
ap
er
with
k
ey
o
b
s
er
v
atio
n
s
a
n
d
o
u
tlin
es
p
o
ten
tial
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
i
n
lo
w
-
p
o
wer
s
ca
n
-
b
ased
test
in
g
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y:
O
VE
RVI
E
W
O
F
L
O
W
-
P
O
WE
R
DF
T
T
E
CH
NIQU
E
S
Fo
r
s
ca
n
-
b
ased
test
in
g
,
test
p
atter
n
s
ar
e
ty
p
ically
g
e
n
er
ate
d
eith
er
b
y
an
AT
PG
to
o
l
o
r
b
y
a
B
I
ST
p
atter
n
g
e
n
er
ato
r
.
Sh
if
t
-
m
o
d
e
p
o
wer
ca
n
b
e
r
e
d
u
ce
d
th
r
o
u
g
h
s
tr
u
ctu
r
al
m
o
d
if
icatio
n
s
t
o
th
e
d
esig
n
o
r
b
y
o
p
tim
izin
g
th
e
test
p
atter
n
s
th
em
s
elv
es.
Nu
m
er
o
u
s
tech
n
iq
u
es
h
av
e
b
ee
n
p
r
o
p
o
s
ed
i
n
th
e
liter
atu
r
e
to
ef
f
ec
tiv
ely
m
in
im
ize
s
h
if
t
p
o
wer
d
u
r
in
g
s
ca
n
o
p
e
r
atio
n
s
.
T
h
e
f
o
llo
win
g
s
u
b
s
ec
tio
n
s
d
elv
e
in
to
th
ese
d
is
tin
ct
ap
p
r
o
ac
h
es,
s
tar
tin
g
with
AT
PG
-
b
ased
m
eth
o
d
s
.
Sco
p
e
o
f
s
u
r
v
ey
: T
im
ef
r
am
e
(
1
9
9
8
–
2
0
2
5
)
,
f
o
cu
s
o
n
p
o
wer
-
a
war
e
DFT
an
d
s
ca
n
r
eo
r
d
er
in
g
.
2
.
1
.
Sca
n po
wer
re
du
ct
io
n t
ec
hn
iq
ues
in DF
T
(
AT
P
G
-
b
a
s
ed
m
et
ho
ds
)
AT
PG
-
b
ased
lo
w
-
p
o
wer
DF
T
tech
n
iq
u
es
f
o
cu
s
o
n
s
tr
ate
g
ies
ap
p
lied
d
u
r
in
g
o
r
af
ter
g
en
er
atin
g
d
eter
m
in
is
tic
test
p
atter
n
s
.
T
h
ese
m
eth
o
d
s
r
ed
u
ce
th
e
s
witch
in
g
ac
tiv
ity
w
h
en
e
x
ter
n
ally
ap
p
lied
test
p
atter
n
s
ar
e
s
h
if
ted
to
th
e
s
ca
n
ch
ain
s
.
2
.
1
.
1
.
Sca
n
a
rc
hite
ct
ure
-
lev
e
l t
ec
hn
iqu
e
s
A.
Use o
f
s
pecia
l sca
n c
ells
Sp
ec
ial
s
ca
n
f
lip
-
f
lo
p
s
a
r
e
d
e
s
ig
n
ed
to
b
lo
c
k
o
r
g
ate
u
n
n
ec
ess
ar
y
tr
an
s
itio
n
s
d
u
r
in
g
th
e
s
ca
n
s
h
if
t
.
T
h
ey
p
r
ev
en
t
t
r
an
s
itio
n
s
f
r
o
m
p
r
o
p
ag
atin
g
in
to
c
o
m
b
in
atio
n
al
lo
g
ic
wh
ile
m
ai
n
tain
in
g
s
tan
d
ar
d
s
ca
n
f
u
n
cti
o
n
ality
.
R
ep
r
esen
tativ
e
wo
r
k
s
in
clu
d
e
m
o
d
if
ied
s
c
an
f
lip
-
f
l
o
p
s
,
g
ate
d
o
r
is
o
latio
n
s
ca
n
ce
lls
,
an
d
tr
im
o
d
al
s
ca
n
ce
lls
[
3
]
,
[
4
]
.
Fig
u
r
e
1
s
h
o
ws
a
ce
ll
th
at
u
s
es
s
ep
ar
ate
latch
es.
T
h
is
tec
h
n
iq
u
e
s
ig
n
if
ican
tly
r
ed
u
ce
s
s
h
if
t
-
in
d
u
ce
d
to
g
g
les;
h
o
wev
er
,
it in
t
r
o
d
u
ce
s
ad
d
itio
n
al
ar
ea
an
d
d
elay
o
v
er
h
ea
d
.
B
.
Sca
n c
ha
in s
eg
m
ent
a
t
io
n a
nd
s
t
a
g
g
er
ed
clo
ck
ing
Seg
m
en
tatio
n
d
iv
id
es
a
lo
n
g
s
ca
n
ch
ain
in
to
s
m
aller
s
eg
m
en
ts
th
at
ar
e
clo
ck
ed
s
ep
ar
ately
o
r
in
a
non
-
o
v
er
lap
p
in
g
m
a
n
n
er
,
as
s
h
o
wn
in
Fig
u
r
e
2
.
T
h
is
en
s
u
r
e
s
th
at
n
o
t
all
th
e
s
ca
n
ce
lls
s
wi
tch
s
im
u
ltan
eo
u
s
ly
,
th
er
eb
y
l
o
wer
in
g
t
h
e
in
s
tan
ta
n
eo
u
s
p
ea
k
p
o
we
r
.
E
x
a
m
p
les
in
clu
d
e
s
k
ip
p
i
n
g
in
ac
tiv
e
s
eg
m
en
ts
o
r
f
r
eq
u
e
n
cy
-
s
ca
led
s
eg
m
en
ted
ar
ch
itectu
r
e
s
[
5
]
–
[
8
]
.
T
h
is
tech
n
iq
u
e
r
ed
u
ce
s
th
e
p
ea
k
s
h
if
t
p
o
wer
a
n
d
m
itig
ates
th
e
I
R
-
d
r
o
p
;
h
o
wev
er
,
it r
eq
u
i
r
es a
d
d
itio
n
al
co
n
tr
o
l lo
g
ic
an
d
th
e
u
s
e
o
f
m
u
ltip
le
clo
ck
p
h
ases
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
26
:
30
-
3
9
32
Fig
u
r
e
1
.
Scan
ce
lls
with
g
ate
d
f
u
n
ctio
n
al
o
u
t
p
u
t d
u
r
in
g
s
h
if
tin
g
,
ce
lls
with
s
ep
ar
ate
latch
es f
o
r
f
u
n
ctio
n
al
an
d
s
ca
n
p
ath
[
4
]
Fig
u
r
e
2
.
Stag
g
e
r
ed
clo
c
k
o
p
e
r
atio
n
[
6
]
C.
Sca
n
pa
t
h m
o
difica
t
io
n
(
Rec
o
nfig
ura
ble
s
ca
n a
rc
hite
ct
ure
)
Mo
d
if
y
in
g
th
e
s
ca
n
p
ath
s
tr
u
ctu
r
e
allo
ws
f
o
r
th
e
d
ec
o
u
p
lin
g
o
f
th
e
s
ca
n
ch
ain
s
ec
tio
n
s
o
r
th
e
s
elec
tiv
e
ac
tiv
atio
n
o
f
p
o
r
tio
n
s
o
f
th
e
ch
ain
.
R
ec
o
n
f
ig
u
r
ab
l
e
s
ca
n
ar
ch
itectu
r
es
an
d
clo
ck
-
g
ated
s
ca
n
g
r
o
u
p
s
f
all
u
n
d
er
th
is
ca
teg
o
r
y
[
9
]
,
[
1
0
]
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
id
es
d
ir
ec
t
co
n
tr
o
l
o
v
e
r
s
witch
in
g
p
r
o
p
ag
atio
n
;
h
o
we
v
er
,
its
r
o
u
tin
g
o
v
er
h
ea
d
d
em
an
d
s
ca
r
ef
u
l
in
teg
r
ati
o
n
.
D.
Sca
n
cha
in o
rder
ing
ba
s
e
d o
n lo
g
ic
co
nn
ec
t
iv
it
y
Scan
ce
lls
ar
e
s
t
itch
ed
b
ased
o
n
lo
g
ic
co
n
n
ec
tiv
ity
o
r
to
p
o
l
o
g
y
,
s
u
ch
th
at
tr
an
s
itio
n
s
in
o
n
e
ce
ll
d
o
n
o
t
p
r
o
p
ag
ate
ex
ce
s
s
iv
ely
in
t
o
h
ig
h
ly
ac
tiv
e
l
o
g
ic
c
o
n
es,
a
s
s
h
o
wn
in
Fig
u
r
e
3
.
L
o
C
C
o
-
b
ased
an
d
to
p
o
l
o
g
y
-
b
ased
ch
ain
s
titch
in
g
d
em
o
n
s
tr
ate
th
is
p
r
in
cip
le
[
1
1
]
–
[
1
4
]
.
T
h
is
m
eth
o
d
r
ed
u
ce
s
u
n
n
ec
es
s
ar
y
in
ter
n
al
to
g
g
les
d
u
r
in
g
th
e
s
h
if
t; h
o
we
v
er
,
it r
e
q
u
ir
es
a
d
etailed
g
ate
-
lev
el
co
n
n
ec
tiv
ity
an
aly
s
is
.
Fig
u
r
e
3
.
Scan
s
titch
in
g
p
r
o
p
o
s
ed
in
[
1
2
]
an
aly
ze
s
th
e
f
an
o
u
t o
f
ce
lls
an
d
p
u
ts
f
lo
p
s
with
g
r
ea
ter
in
f
lu
en
ce
at
th
e
f
r
o
n
t o
f
s
ca
n
c
h
ain
s
an
d
l
o
wer
in
f
lu
en
ce
v
alu
es a
t th
e
en
d
o
f
th
e
s
ca
n
ch
ain
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
o
w
er
-
a
w
a
r
e
d
esig
n
-
fo
r
-
test
:
a
s
u
r
ve
y
o
f d
ft tec
h
n
iq
u
es a
n
d
s
ca
n
ch
a
in
r
eo
r
d
erin
g
…
(
V
.
R
a
jith
a
R
a
n
i
)
33
E
.
Sca
n
cha
in o
rder
ing
ba
s
e
d o
n pa
t
t
er
n c
o
nte
nt
T
h
e
o
r
d
er
o
f
th
e
s
ca
n
ce
lls
is
o
p
tim
ized
u
s
in
g
t
h
e
e
x
p
ec
ted
test
p
atter
n
co
n
ten
ts
,
s
u
ch
as
t
h
e
ca
r
e
b
it
d
en
s
ity
o
r
ex
p
ec
ted
tr
a
n
s
itio
n
s
eq
u
en
ce
s
[
1
5
]
.
T
h
is
tech
n
iq
u
e
is
h
ig
h
ly
ef
f
ec
tiv
e
wh
en
th
e
p
atter
n
co
n
ten
ts
ar
e
k
n
o
wn
,
b
u
t it
is
p
att
er
n
-
d
ep
en
d
en
t a
n
d
r
eq
u
i
r
es a
cc
ess
to
AT
PG
-
g
en
er
ated
p
atter
n
s
.
F.
Sca
n
ce
ll r
eo
rder
ing
ba
s
ed
o
n t
ra
ns
it
io
n we
ig
hts
Af
ter
lo
w
-
tr
an
s
itio
n
p
atter
n
s
ar
e
g
en
er
ated
,
t
h
e
s
ca
n
ce
lls
ar
e
r
eo
r
d
er
ed
b
ased
o
n
th
e
“scan
in
f
lu
en
ce
”
o
r
tr
an
s
itio
n
weig
h
t
m
etr
ic,
en
s
u
r
in
g
m
in
im
al
p
r
o
p
a
g
atio
n
o
f
to
g
g
les
in
to
c
o
m
b
in
atio
n
al
lo
g
ic
[
1
6
]
–
[
1
9
]
.
T
h
is
tech
n
iq
u
e
is
h
ig
h
ly
ef
f
ec
tiv
e
with
in
th
e
B
I
ST
b
ec
au
s
e
of
th
e
p
r
ed
eter
m
in
ed
p
atter
n
o
r
d
e
r
;
h
o
wev
er
,
it
r
eq
u
ir
es
a
d
etailed
an
aly
s
is
o
f
th
e
tr
an
s
itio
n
weig
h
ts
.
2
.
1
.
2
.
AT
P
G
-
lev
el
t
ec
hn
iqu
e
s
A.
pa
t
t
er
n m
o
difica
t
io
n
v
ia
X
-
f
illing
T
h
e
d
o
n
’
t
-
ca
r
e
b
its
in
AT
PG
p
atter
n
s
ca
n
b
e
f
illed
in
wa
y
s
th
at
m
in
im
ize
s
h
if
t
t
o
g
g
les.
T
ec
h
n
iq
u
es
s
u
ch
as
co
r
r
elatio
n
-
b
ased
f
ill,
weig
h
ted
f
ill,
an
d
p
o
wer
-
aw
ar
e
f
ill
r
ed
u
ce
th
e
tr
an
s
itio
n
s
b
etwe
en
s
u
cc
ess
iv
e
b
its
[
2
0
]
.
T
h
is
m
eth
o
d
in
cu
r
s
n
o
h
ar
d
wa
r
e
co
s
t
an
d
wo
r
k
s
with
ex
is
tin
g
p
atter
n
s
,
b
u
t
its
im
p
ac
t
is
lim
ited
wh
en
th
e
X
-
b
it
d
en
s
ity
in
p
att
er
n
s
is
lo
w.
B
.
P
a
t
t
er
n
re
o
rder
ing
f
o
r
s
hift
-
po
wer
re
du
ct
io
n
T
est
v
ec
to
r
s
ca
n
b
e
r
eo
r
d
er
ed
to
m
in
im
ize
to
g
g
les
b
etwe
en
co
n
s
ec
u
tiv
e
p
atter
n
s
b
ased
o
n
h
am
m
in
g
d
is
tan
ce
m
in
im
izatio
n
o
r
en
e
r
g
y
-
awa
r
e
h
eu
r
is
tics
[
2
1
]
–
[
2
6
]
.
T
h
is
ap
p
r
o
ac
h
r
ed
u
ce
s
th
e
ag
g
r
eg
ate
s
h
if
t
p
o
wer
ac
r
o
s
s
th
e
test
s
es
s
io
n
s
;
h
o
wev
er
,
its
ef
f
ec
tiv
en
ess
m
ay
i
n
ter
ac
t
with
p
atter
n
c
o
m
p
ac
ti
o
n
o
r
co
m
p
r
ess
io
n
tech
n
iq
u
es.
C.
L
o
w
-
t
ra
ns
it
io
n t
est
pa
t
t
er
n g
ener
a
t
o
rs
(
T
P
G
s
)
L
o
w
-
to
g
g
le
T
PGs
,
s
u
ch
as
b
i
t
-
s
wap
p
in
g
L
FS
R
s
,
J
o
h
n
s
o
n
co
u
n
ter
s
,
a
n
d
s
in
g
le
i
n
p
u
t
ch
an
g
e
(
SIC
)
p
atter
n
g
en
e
r
ato
r
s
,
re
d
u
ce
th
e
n
u
m
b
er
o
f
tr
a
n
s
itio
n
s
b
etwe
en
p
atter
n
s
[
2
7
]
–
[
2
9
]
.
T
h
is
tech
n
iq
u
e
r
e
d
u
ce
s
b
o
t
h
th
e
s
h
if
t
an
d
ca
p
tu
r
e
p
o
wer
in
B
I
ST
b
u
t
o
f
f
er
s
lim
ited
co
n
tr
o
l
o
v
e
r
f
au
lt
co
v
e
r
ag
e
u
n
less
ad
d
itio
n
al
en
h
an
ce
m
e
n
t m
ec
h
an
is
m
s
ar
e
ap
p
lied
.
2
.
1
.
3
.
B
I
ST
-
ba
s
ed
lo
w
-
po
we
r
t
ec
hn
iqu
e
s
A.
L
o
w
-
po
wer
t
ec
hn
iqu
e
s
in
B
I
S
T
a
rc
hite
ct
ures
B
I
ST
ar
ch
itectu
r
es
g
en
er
ate
p
atter
n
s
o
n
-
ch
ip
u
s
in
g
L
F
SR
s
o
r
o
th
er
p
s
eu
d
o
-
r
an
d
o
m
p
atter
n
g
en
er
ato
r
s
.
T
h
eir
n
atu
r
ally
h
i
g
h
to
g
g
lin
g
r
ate
r
eq
u
ir
es
s
p
ec
ialized
lo
w
-
p
o
wer
p
atter
n
s
an
d
s
ca
n
o
r
g
an
izatio
n
[
3
0
]
.
W
h
ile
v
a
r
io
u
s
tech
n
i
q
u
e
s
co
n
tr
ib
u
te
to
l
o
w
-
p
o
wer
DFT,
s
ca
n
ch
ain
o
p
tim
izatio
n
an
d
r
eo
r
d
er
in
g
s
tan
d
o
u
t
d
u
e
to
th
eir
s
ig
n
if
ican
t
i
m
p
ac
t
o
n
s
h
if
t
p
o
wer
,
with
o
f
ten
m
in
im
al
h
a
r
d
war
e
o
v
er
h
ea
d
.
T
h
e
f
o
llo
win
g
s
ec
t
io
n
p
r
o
v
id
es
a
d
etailed
ex
am
in
atio
n
o
f
th
e
k
ey
r
esear
ch
ad
v
an
ce
m
e
n
ts
in
th
is
s
p
ec
if
ic
d
o
m
ain
,
ex
p
lo
r
in
g
h
o
w
d
if
f
e
r
en
t a
lg
o
r
ith
m
ic
ap
p
r
o
ac
h
es h
av
e
tack
led
th
e
c
h
allen
g
e
o
f
ef
f
icien
t scan
c
h
ain
co
n
f
ig
u
r
atio
n
.
3.
DE
T
AI
L
E
D
R
E
V
I
E
W
O
F
S
CAN
CH
AIN O
P
T
I
M
I
Z
AT
I
O
N
RE
S
E
ARCH
Scan
ch
ain
o
p
tim
izatio
n
is
an
ev
o
lv
in
g
r
esear
ch
ar
ea
t
h
at
in
clu
d
es
p
atter
n
r
eo
r
d
e
r
in
g
,
g
r
ap
h
-
b
ase
d
s
ca
n
r
eo
r
d
er
i
n
g
,
m
etah
e
u
r
is
tics
,
an
d
ML
/R
L
-
b
ased
s
tr
ateg
ies.
E
x
is
tin
g
s
ca
n
ch
ain
r
eo
r
d
er
in
g
ap
p
r
o
ac
h
es
ar
e
lar
g
ely
h
eu
r
is
tic
-
d
r
iv
en
a
n
d
s
tatic,
with
lim
ited
s
ca
lab
ilit
y
,
ad
ap
tab
ilit
y
,
an
d
g
e
n
er
aliza
t
io
n
ac
r
o
s
s
d
iv
er
s
e
cir
cu
it st
r
u
ctu
r
es.
T
h
ey
t
y
p
ical
ly
o
p
tim
ize
a
s
in
g
le
o
b
jectiv
e
with
o
u
t e
x
p
licitly
m
o
d
elin
g
p
h
y
s
ical
ef
f
ec
ts
s
u
ch
as
in
ter
co
n
n
ec
t
d
elay
o
r
en
ab
lin
g
d
y
n
a
m
ic
p
o
wer
–
wir
elen
g
th
tr
ad
e
-
o
f
f
s
.
Mo
r
eo
v
er
,
th
e
a
b
s
en
ce
o
f
lear
n
in
g
an
d
f
ee
d
b
ac
k
m
ec
h
an
is
m
s
p
r
e
v
en
ts
k
n
o
wled
g
e
tr
an
s
f
er
a
n
d
co
n
tin
u
o
u
s
im
p
r
o
v
em
en
t a
cr
o
s
s
d
esig
n
s
.
3
.
1
.
AT
P
G
-
lev
el
t
ec
hn
iqu
es:
pa
t
t
er
n/v
ec
t
o
r
re
o
rder
ing
a
nd
X
-
F
ill
1
.
P.
Gir
ar
d
,
C
.
L
an
d
r
au
lt,
S.
Pra
v
o
s
s
o
u
d
o
v
itch
,
D.
Sev
er
ac
,
“Red
u
cin
g
p
o
wer
co
n
s
u
m
p
tio
n
d
u
r
in
g
test
ap
p
licatio
n
b
y
test
v
ec
to
r
o
r
d
e
r
in
g
,
”
P
r
o
c.
I
E
E
E
I
S
C
A
S
,
1
9
9
8
[
3
1
]
.
An
ea
r
ly
in
f
lu
en
tial
p
ap
er
f
o
r
m
alize
d
th
e
id
ea
o
f
m
in
i
m
izin
g
s
witch
in
g
d
u
r
in
g
s
ca
n
s
h
if
ts
by
r
eo
r
d
er
i
n
g
th
e
test
v
ec
to
r
s
[
3
2
]
,
[
3
3
]
.
T
h
e
y
co
n
s
id
er
ed
th
e
Ham
m
in
g
d
is
tan
ce
b
etwe
en
co
n
s
ec
u
tiv
e
v
ec
t
o
r
s
an
d
p
r
o
p
o
s
ed
h
eu
r
is
tics
to
r
ed
u
ce
th
e
n
u
m
b
e
r
o
f
tr
an
s
itio
n
s
.
R
e
s
u
lts
s
h
o
wed
th
at
o
r
d
er
i
n
g
ca
n
s
ig
n
if
ican
tly
r
ed
u
ce
s
witch
in
g
a
n
d
p
ea
k
cu
r
r
en
t in
b
e
n
ch
m
ar
k
s
.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Fo
u
n
d
atio
n
al;
d
em
o
n
s
tr
ates
b
asic
ef
f
ec
tiv
en
ess
o
f
o
r
d
er
in
g
.
I
t
d
o
es
n
o
t
ex
p
lo
r
e
a
d
v
an
ce
d
g
r
ap
h
/m
eta
h
eu
r
is
tic
s
ee
d
in
g
an
d
ig
n
o
r
es
t
h
e
d
o
n
’
t
-
ca
r
e
f
illi
n
g
.
I
t
s
er
v
es a
s
a
u
s
ef
u
l
b
aselin
e
an
d
h
is
to
r
ical
an
c
h
o
r
.
2
.
D.
So
n
g
et
a
l
.
,
“M
T
R
-
f
ill:
s
im
u
lated
an
n
ea
lin
g
-
b
ased
x
-
f
illi
n
g
to
r
ed
u
ce
test
p
o
wer
d
is
s
ip
atio
n
,
”
I
E
I
C
E
Tr
a
n
s
.
,
2
0
0
8
[
3
4
]
,
[
3
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
26
:
30
-
3
9
34
Pro
p
o
s
es
a
s
im
u
lated
an
n
ea
li
n
g
ap
p
r
o
ac
h
to
f
ill
in
th
e
d
o
n
’
t
-
ca
r
e
b
its
(
X)
i
n
AT
PG
p
atter
n
s
to
m
in
im
ize
tr
an
s
itio
n
s
(
b
o
th
s
h
i
f
t
an
d
ca
p
t
u
r
e)
.
Fo
r
m
u
lates
co
s
t
an
d
u
s
es
SA
s
ea
r
ch
to
s
et
Xs.
R
esu
lt
s
s
h
o
w
a
s
ig
n
if
ican
t r
ed
u
ctio
n
in
ca
p
tu
r
e
an
d
s
h
if
t p
o
wer
wh
en
m
an
y
Xs ar
e
p
r
esen
t.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
X
-
f
ill
is
v
er
y
ef
f
ec
tiv
e
wh
en
AT
P
G
y
ield
s
m
an
y
Xs;
SA
y
iel
d
s
b
etter
s
o
lu
tio
n
s
th
an
g
r
ee
d
y
X
-
f
ills
b
u
t
is
co
m
p
u
tatio
n
ally
h
ea
v
ier
.
T
h
is
is
h
ig
h
ly
r
elev
an
t
wh
en
y
o
u
wa
n
t
to
co
m
b
in
e
o
r
d
er
i
n
g
with
X
-
f
ill.
3
.
C
.
R
.
Su
p
r
eta
De
v
i
et
a
l.
,
“
A
n
o
v
el
ap
p
r
o
ac
h
f
o
r
x
-
f
illi
n
g
an
d
r
eo
r
d
er
in
g
test
v
ec
to
r
s
f
o
r
p
o
we
r
r
e
d
u
ctio
n
,
”
R
esea
r
ch
Jo
u
r
n
a
l o
f A
p
p
lied
S
cien
ce
s
,
2
0
1
2
[
3
6
]
.
C
o
m
b
in
es X
-
f
ill an
d
r
e
o
r
d
er
i
n
g
h
eu
r
is
tics
to
jo
in
tly
r
e
d
u
ce
s
h
if
t a
n
d
ca
p
tu
r
e
tr
a
n
s
itio
n
s
.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Pra
ctic
al
ap
p
r
o
ac
h
;
s
h
o
ws
jo
in
t
o
p
tim
izatio
n
is
b
en
ef
icial.
T
h
is
m
eth
o
d
was
u
s
ed
as a
p
r
ac
tical
co
m
p
a
r
ativ
e
m
eth
o
d
.
3
.
2
.
G
ra
ph
-
ba
s
ed
o
rder
ing
(
M
S
T
/K
rus
k
a
l,
T
SP
a
pp
ro
x
i
m
a
t
io
ns
)
4
.
C
lu
s
ter
-
MST
ap
p
r
o
ac
h
es
-
(
r
ep
r
esen
tativ
e)
M.
Na
v
in
Ku
m
ar
et
a
l.
,
“Cl
u
s
ter
-
b
ased
test
v
ec
to
r
r
e
-
o
r
d
er
in
g
f
o
r
r
ed
u
ce
d
p
o
wer
d
i
s
s
ip
atio
n
in
d
ig
ital c
ir
cu
its
,
”
A
u
to
ma
tika
,
2
0
2
3
[
3
7
]
.
T
wo
-
s
tag
e
m
eth
o
d
-
clu
s
ter
test
v
ec
to
r
s
b
y
s
im
ilar
ity
,
th
en
co
m
p
u
te
MST
(
v
ia
Kr
u
s
k
al)
in
s
id
e
clu
s
ter
s
an
d
lin
ea
r
ize
MST
to
o
b
tain
an
o
r
d
er
in
g
.
E
d
g
e
weig
h
ts
u
s
e
th
e
Ham
m
in
g
d
is
tan
ce
(
o
p
tio
n
a
lly
weig
h
ted
b
y
b
it
s
ig
n
if
ican
ce
)
.
R
esu
lts
r
ep
o
r
t
≈
1
0
–
1
2
%
r
e
d
u
ctio
n
in
s
witch
in
g
o
n
I
SC
AS’
8
9
b
en
c
h
m
ar
k
s
with
o
u
t
ch
a
n
g
in
g
p
atter
n
s
o
r
co
v
er
ag
e
.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
MST
/Kr
u
s
k
al
p
r
o
v
id
es
a
f
ast,
d
eter
m
in
is
tic,
h
ig
h
-
q
u
ality
b
ac
k
b
o
n
e;
clu
s
ter
in
g
s
ca
les to
lar
g
e
p
atte
r
n
s
ets.
T
h
is
is
th
e
m
ain
ca
n
d
i
d
ate
f
o
r
t
h
e
MST
ch
o
ice.
3
.
3
.
M
et
a
heuris
t
ics (
G
A,
SA,
P
SO
,
ACO
)
5
.
S.
Alam
an
d
R
.
R
o
y
,
“G
en
etic
alg
o
r
ith
m
b
ased
o
p
ti
m
izatio
n
o
f
test
v
ec
to
r
s
f
o
r
lo
w
p
o
wer
test
in
g
,
”
Micr
o
elec
tr
o
n
ics Jo
u
r
n
a
l
,
2
0
2
0
[
3
8
]
.
GA
to
ev
o
lv
e
p
er
m
u
tatio
n
s
o
f
th
e
test
v
ec
to
r
s
by
m
in
i
m
izin
g
th
e
av
er
ag
e
Ham
m
in
g
co
s
t
an
d
o
p
tio
n
ally
th
e
p
ea
k
tr
an
s
itio
n
s
.
C
h
r
o
m
o
s
o
m
e
=
v
ec
to
r
o
r
d
er
in
g
;
f
itn
ess
=
p
o
wer
m
etr
ic.
R
esu
lt
s
s
h
o
w
G
A
o
f
ten
o
u
tp
er
f
o
r
m
s
g
r
ee
d
y
h
eu
r
is
tics
o
n
s
m
all
to
m
ed
iu
m
p
att
er
n
s
ets.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Go
o
d
s
o
lu
tio
n
q
u
ality
b
u
t
h
ig
h
r
u
n
tim
e
f
o
r
lar
g
e
s
ets;
u
s
ef
u
l
as
a
g
o
ld
-
s
tan
d
ar
d
b
aselin
e
o
r
r
ef
in
e
m
e
n
t stag
e
s
ee
d
ed
b
y
MST
.
6
.
B
alwin
d
er
Sin
g
h
et
a
l.
,
“Pa
r
ticle
s
war
m
o
p
tim
izatio
n
f
r
a
m
ewo
r
k
f
o
r
lo
w
p
o
wer
test
in
g
o
f
VL
SI
c
ir
cu
its
,
”
(
ar
Xiv
:1
1
1
1
.
1
5
6
4
)
,
2
0
1
1
[
3
9
]
.
PS
O
f
o
r
o
r
d
er
in
g
;
p
ar
ticles
r
ep
r
esen
t
p
er
m
u
tatio
n
s
en
co
d
ed
v
ia
co
n
tin
u
o
u
s
v
ec
to
r
s
m
ap
p
in
g
to
th
e
p
er
m
u
tatio
n
v
ia
s
o
r
tin
g
.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Simp
ler
to
im
p
lem
en
t
th
an
GA;
v
ar
i
ab
le
p
er
f
o
r
m
a
n
ce
.
I
t
o
f
f
er
s
an
alter
n
ativ
e
m
etah
eu
r
is
tic
b
aselin
e.
7
.
Hy
b
r
i
d
an
t c
o
l
o
n
y
+
MST
(
r
ec
en
t w
o
r
k
s
/r
ep
r
esen
tativ
e:
2
0
2
4
–
2
0
2
5
AC
O+
MST
p
ap
er
s
)
.
S.
Ash
a
Po
n
an
d
V.
J
ey
al
ak
s
h
m
i,
“T
est
p
atter
n
o
p
tim
izati
o
n
s
ch
em
e
b
ased
o
n
h
y
b
r
id
an
t
co
lo
n
y
o
p
tim
izatio
n
,
”
S
cie
n
tifi
c
R
ep
o
r
ts
,
v
o
l.
1
5
,
Ar
ticle
n
o
.
3
4
4
5
3
,
2
0
2
5
[
4
0
]
,
[
4
1
]
.
Use
MST
to
s
ee
d
p
h
er
o
m
o
n
e
in
itializatio
n
;
AC
O
r
ef
in
es
o
r
d
er
in
g
b
y
p
h
er
o
m
o
n
e
u
p
d
ates
d
r
iv
en
b
y
tr
an
s
itio
n
co
s
t.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Hig
h
-
q
u
ality
r
esu
lts
in
ex
p
e
r
im
en
t
s
,
b
u
t
co
m
p
u
tatio
n
al
c
o
s
t
an
d
tu
n
i
n
g
r
eq
u
ir
ed
; sh
o
ws g
o
o
d
s
y
n
er
g
y
with
MST
s
ee
d
in
g
.
3
.
4
.
Sim
ula
t
i
o
n
-
ba
s
ed
a
nd
RP
RF
a
na
ly
s
is
(
L
B
I
ST
)
8
.
Y.
Su
n
,
“N
o
v
el
test
p
o
in
t in
s
er
tio
n
ap
p
licatio
n
s
in
L
B
I
ST,
”
Ph
D
T
h
esis
,
Au
b
u
r
n
U
n
iv
er
s
ity
,
2
0
1
8
[
3
0
]
.
Deta
iled
s
tu
d
y
o
f
R
PR
Fs
an
d
T
PI
tar
g
eted
f
o
r
L
B
I
ST;
in
clu
d
es
p
r
o
b
a
b
ilis
tic
an
aly
s
is
o
f
r
an
d
o
m
d
etec
tab
ilit
y
an
d
T
P selectio
n
to
im
p
r
o
v
e
L
B
I
ST
co
v
er
a
g
e.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
R
PR
F
wo
r
k
is
th
e
g
o
-
to
r
ef
er
e
n
ce
f
o
r
L
B
I
ST
-
o
r
ie
n
ted
T
PI;
if
y
o
u
tar
g
et
L
B
I
ST
o
r
r
an
d
o
m
p
atter
n
s
,
R
PR
F T
PI
i
s
ess
en
tial.
9
.
Simu
l
atio
n
-
b
ased
T
PI
wo
r
k
s
(
v
ar
io
u
s
ac
ad
em
ic
f
lo
ws)
R
.
S.
Veth
am
u
th
u
an
d
S.
Siv
an
an
th
am
,
“No
v
el
test
p
o
in
t
in
s
er
tio
n
m
ec
h
an
is
m
u
s
in
g
tim
in
g
-
awa
r
e
an
aly
s
is
to
tar
g
et
ch
allen
g
es
i
n
h
ig
h
-
co
m
p
lex
ity
So
C
d
esig
n
s
,
”
I
n
ter
n
atio
n
al
J
o
u
r
n
al
o
f
I
n
n
o
v
ativ
e
Scien
ce
an
d
Ad
v
a
n
ce
d
E
n
g
in
ee
r
i
n
g
(
I
J
I
SAE)
,
J
u
l.
2
0
2
3
[
4
2
]
.
I
t
p
r
esen
ts
a
tim
in
g
-
aw
ar
e
T
PI
m
eth
o
d
[
4
3
]
–
[
4
6
]
th
at
u
s
es
p
o
s
t
-
s
y
n
th
esis
tim
in
g
an
d
f
au
l
t
-
co
v
er
ag
e
an
aly
s
is
to
in
s
er
t
o
n
ly
h
ig
h
ly
ef
f
ec
tiv
e
p
o
in
ts
,
m
in
im
izin
g
a
r
ea
o
v
er
h
ea
d
wh
ile
r
etain
in
g
co
v
er
ag
e.
I
t
r
e
p
o
r
ts
lar
g
e
r
ed
u
ctio
n
s
in
th
e
T
PI
ar
ea
o
v
e
r
h
ea
d
an
d
v
ec
to
r
co
u
n
t.
Go
o
d
r
ef
er
en
ce
f
o
r
p
r
ac
tical,
lo
w
-
o
v
er
h
ea
d
T
PI
f
o
r
lar
g
e
So
C
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
o
w
er
-
a
w
a
r
e
d
esig
n
-
fo
r
-
test
:
a
s
u
r
ve
y
o
f d
ft tec
h
n
iq
u
es a
n
d
s
ca
n
ch
a
in
r
eo
r
d
erin
g
…
(
V
.
R
a
jith
a
R
a
n
i
)
35
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
C
ap
tu
r
es
s
eq
u
en
tial
d
y
n
am
ics
m
is
s
ed
b
y
s
tatic
SC
OAP
;
ex
p
e
n
s
iv
e
an
d
s
en
s
itiv
e
to
v
ec
to
r
q
u
ality
-
u
s
ef
u
l w
h
en
d
y
n
am
ic
b
eh
av
i
o
r
d
o
m
in
ates.
3
.
5
.
M
a
chine
lea
rning
a
nd
deep
re
info
rc
em
ent
lea
rning
(
T
P
I
a
nd
s
ca
n o
ptim
iza
t
io
n)
1
0
.
Z
.
Sh
i
et
a
l.
,
“De
ep
T
PI:
test
p
o
in
t in
s
er
tio
n
with
d
ee
p
r
ei
n
f
o
r
ce
m
e
n
t le
ar
n
in
g
,
”
ar
Xiv
/I
E
E
E
,
2
0
2
2
[
4
7
]
.
T
h
e
m
o
d
el
cir
cu
it
is
a
d
ir
ec
ted
g
r
ap
h
t
h
at
u
s
es
a
g
r
ap
h
n
eu
r
al
n
etwo
r
k
(
GNN
)
f
o
r
n
o
d
e
e
m
b
ed
d
in
g
s
an
d
d
ee
p
Q
-
l
ea
r
n
in
g
(
DQN)
[
4
5
]
,
[
4
8
]
as
th
e
v
alu
e
esti
m
at
o
r
.
Fig
u
r
e
4
s
h
o
ws
th
e
o
v
er
v
i
ew
o
f
th
e
d
ee
p
T
PI
R
L
ag
en
t.
T
h
e
ag
en
t
s
eq
u
en
ti
ally
in
s
er
ts
C
P
/OP
to
m
ax
im
ize
th
e
co
v
er
a
g
e
g
ain
m
in
u
s
th
e
co
s
t.
I
t
in
clu
d
es
test
ab
ilit
y
-
awa
r
e
atten
tio
n
an
d
p
r
etr
ain
ed
e
m
b
ed
d
in
g
s
.
R
esu
lts
s
h
o
w
th
at
th
ese
m
eth
o
d
s
o
u
tp
er
f
o
r
m
b
aselin
e
co
m
m
er
cial
h
e
u
r
is
tics
o
n
s
ev
e
r
al
b
en
c
h
m
ar
k
s
in
ter
m
s
o
f
co
v
er
ag
e
im
p
r
o
v
em
en
t
a
n
d
p
atte
r
n
r
e
d
u
ctio
n
u
n
d
er
an
ar
ea
b
u
d
g
et.
Pro
s
/C
o
n
s
/R
elev
an
ce
:
Stro
n
g
e
v
id
en
ce
th
at
lear
n
in
g
jo
in
tly
s
elec
ts
T
Ps
b
etter
th
an
g
r
ee
d
y
r
u
le
s
; b
aselin
e
f
o
r
y
o
u
r
T
PI
R
L
ch
o
ice.
C
o
s
t: tr
ain
in
g
c
o
m
p
l
ex
ity
an
d
e
n
g
in
ee
r
i
n
g
in
te
g
r
atio
n
is
s
u
es
.
Fig
u
r
e
4
.
T
h
e
o
v
er
v
iew
o
f
d
ee
p
T
PI
a
g
en
t
[
4
7
]
1
1
.
J
.
Z
h
a
n
g
et
a
l
.
,
“GN
N
-
T
PI
: G
r
ap
h
n
e
u
r
al
n
etwo
r
k
b
ased
test
p
o
in
t o
p
tim
izatio
n
,
”
DA
C
,
2
0
2
3
[
4
9
]
.
G
NN
ap
p
lied
to
T
PI
as
a
s
u
p
er
v
is
ed
o
r
r
ein
f
o
r
ce
m
e
n
t
s
ch
em
e
s
h
o
ws
an
im
p
r
o
v
e
d
s
elec
tio
n
o
f
n
o
d
es
with
a
b
etter
co
v
e
r
ag
e
-
o
v
er
h
e
ad
tr
ad
eo
f
f
.
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
R
ein
f
o
r
ce
s
th
e
GNN+
R
L
d
ir
ec
tio
n
;
p
r
o
v
id
es
ar
c
h
itectu
r
e
an
d
s
ca
lin
g
in
s
ig
h
ts
lik
ely
u
s
ef
u
l if
ad
a
p
t
in
g
to
s
ca
n
r
eo
r
d
er
in
g
.
1
2
.
R
L
f
o
r
s
ca
n
o
r
d
er
in
g
&
p
atter
n
s
ch
ed
u
lin
g
-
e.
g
.
,
R
L
-
b
ased
B
I
ST
p
atter
n
g
en
er
atio
n
(
R
ah
m
an
et
a
l.
,
VT
S
2
0
2
1
)
[
5
0
]
–
[
5
4
]
.
Use
R
L
ag
en
ts
(
DQN/
PP
O)
to
s
ch
ed
u
le
p
atter
n
s
o
r
ac
tiv
ate
ch
ain
s
to
m
in
im
ize
to
g
g
les/
p
e
ak
p
o
wer
s
u
b
ject
to
co
v
er
ag
e
c
o
n
s
tr
ain
ts
Pro
s
/C
o
n
s
/R
e
lev
an
ce
:
Dem
o
n
s
tr
ates
th
at
R
L
i
s
ap
p
licab
le
wh
en
th
e
ac
tio
n
s
p
ac
e
is
lar
g
e
(
s
eq
u
en
ce
b
u
ild
in
g
)
;
p
r
o
v
id
es b
en
c
h
m
ar
k
s
f
o
r
r
ewa
r
d
s
h
ap
in
g
an
d
en
v
ir
o
n
m
en
t m
o
d
elin
g
.
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
ab
le
1
s
u
m
m
ar
izes
th
e
ex
is
tin
g
m
eth
o
d
s
f
o
r
s
ca
n
p
o
wer
o
p
tim
izatio
n
,
co
n
s
id
er
in
g
v
a
r
io
u
s
p
ar
am
eter
s
.
4
.
1
.
Sca
n
cha
in re
o
rder
ing
a
s
a
n e
f
f
ec
t
iv
e
lo
w
-
po
wer
DF
T
s
t
ra
t
eg
y
Scan
ch
ain
r
eo
r
d
er
in
g
is
a
lo
w
-
p
o
wer
DFT
tech
n
i
q
u
e
th
at
is
ad
v
an
tag
e
o
u
s
f
o
r
s
ev
er
al
r
ea
s
o
n
s
.
−
No
im
p
ac
t
o
n
f
au
lt
co
v
er
a
g
e
:
r
eo
r
d
er
in
g
d
o
es
n
o
t
m
o
d
i
f
y
test
p
atter
n
s
o
r
f
au
lt
ac
tiv
atio
n
p
ath
s
;
it
o
n
ly
ch
an
g
es th
e
s
h
if
t b
e
h
av
io
r
.
T
h
u
s
,
th
e
co
v
e
r
ag
e
r
em
ai
n
ed
u
n
a
f
f
ec
ted
.
−
Min
im
al
h
ar
d
war
e
o
v
er
h
ea
d
:
u
n
lik
e
s
p
ec
ial
s
ca
n
ce
lls
o
r
s
eg
m
en
tatio
n
,
r
eo
r
d
e
r
in
g
r
eq
u
ir
es
n
o
ad
d
itio
n
al
g
ates o
r
clo
ck
s
wh
en
im
p
lem
e
n
ted
lo
g
ically
at
th
e
s
titch
in
g
t
im
e.
−
C
o
m
p
lem
en
tar
y
to
all
o
th
er
t
ec
h
n
iq
u
es
:
r
eo
r
d
er
in
g
ca
n
b
e
co
m
b
in
ed
with
X
-
f
ill
(
p
atter
n
m
o
d
if
icatio
n
)
,
l
ow
-
tr
an
s
iti
o
n
T
PGs
(
B
I
ST)
,
s
eg
m
en
tatio
n
,
cl
o
ck
g
atin
g
,
an
d
lear
n
in
g
-
b
ased
s
ch
ed
u
lin
g
.
−
Scales
ea
s
ily
to
lar
g
e
d
esig
n
s
:
g
r
ap
h
-
b
ased
o
r
h
eu
r
is
tic
r
eo
r
d
er
in
g
o
p
er
ates
ef
f
icien
tly
o
n
d
esig
n
s
with
ten
s
o
f
th
o
u
s
an
d
s
o
f
s
ca
n
ce
lls
.
−
Sig
n
if
ican
t
r
ed
u
ctio
n
in
s
h
if
t
p
o
wer
:
Stu
d
i
es
co
n
s
is
ten
tly
r
ep
o
r
t
2
0
–
5
0
%
s
h
if
t
p
o
wer
r
ed
u
ctio
n
u
s
in
g
r
eo
r
d
er
i
n
g
alo
n
e,
an
d
u
p
to
6
0
–
7
0
% wh
en
c
o
m
b
in
e
d
with
X
-
f
ill o
r
lo
w
-
tr
an
s
itio
n
p
atter
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
26
:
30
-
3
9
36
−
W
o
r
k
s
f
o
r
AT
PG,
B
I
ST,
an
d
m
ix
ed
-
m
o
d
e
test
f
lo
ws:
th
is
m
ak
es
r
eo
r
d
er
i
n
g
a
u
n
iv
er
s
al
s
o
lu
tio
n
ad
ap
ta
b
le
ac
r
o
s
s
in
d
u
s
tr
ial
DFT
f
lo
ws.
T
ab
le
1
.
Su
m
m
a
r
y
o
f
ex
is
tin
g
m
eth
o
d
s
f
o
r
s
ca
n
p
o
we
r
o
p
tim
izatio
n
S.
N
o
T
ec
h
n
i
q
u
e
(rep
res
en
t
a
t
i
v
e
ref
re
n
s
)
T
e
s
t
t
i
me
Sw
i
t
ch
i
n
g
act
i
v
i
t
y
Po
w
er
co
n
s
u
mp
t
i
o
n
A
re
a
o
v
er
h
ea
d
Co
m
p
l
e
x
i
t
y
Co
v
er
ag
e
i
m
p
ac
t
A
d
v
an
t
a
g
e
s
D
i
s
a
d
v
a
n
t
a
g
e
s
/
N
o
t
es
1
V
ec
t
o
r
reo
r
d
e
ri
n
g
(G
i
rar
d
et
a
l
.
)
-
H
a
mm
i
n
g
-
d
i
s
t
a
n
ce
o
r
d
er
i
n
g
[3
1
]
N
e
u
t
ra
l
(o
r
d
er
i
n
g
o
n
l
y
)
10
–
25%
10
–
20%
N
o
n
e
L
o
w
N
o
i
m
p
ac
t
Si
m
p
l
e
;
n
o
H
W
ch
a
n
g
e
s
;
g
o
o
d
b
a
s
e
l
i
n
e
L
i
mi
t
e
d
w
h
e
n
X
-
d
e
n
s
i
t
y
i
s
l
o
w
;
d
o
e
s
n
o
t
ad
d
re
s
s
cap
t
u
re
p
o
w
er
2
X
-
F
i
l
l
(SA
,
M
T
R)
-
Si
m
u
l
a
t
e
d
an
n
ea
l
i
n
g
X
-
f
i
l
l
[3
4
]
Sl
i
g
h
t
↑
(p
re
p
r
o
c)
/
N
e
u
t
ra
l
a
t
t
e
s
t
20
–
50%
20
–
4
5
%
(s
h
i
f
t
+
cap
t
u
re)
N
o
n
e
Med
i
u
m
–
H
i
g
h
(SA
r
u
n
t
i
me)
Po
t
e
n
t
i
a
l
ri
s
k
i
f
t
h
e
car
e
b
i
t
s
a
re
ch
a
n
g
e
d
i
n
co
rrec
t
l
y
St
r
o
n
g
w
h
en
A
T
PG
p
r
o
d
u
ce
s
man
y
X
s
;
w
o
r
k
s
w
i
t
h
ex
i
s
t
i
n
g
f
l
o
w
Co
m
p
u
t
at
i
o
n
al
l
y
h
ea
v
y
;
effec
t
i
v
e
n
e
s
s
d
e
p
e
n
d
s
o
n
X
-
d
e
n
s
i
t
y
3
J
o
i
n
t
X
-
f
i
l
l
+
Reo
rd
eri
n
g
(D
e
v
i
e
t
a
l
.
)
[3
6
]
N
e
u
t
ra
l
30
–
60%
30
–
55%
N
o
n
e
Med
i
u
m
L
o
w
r
i
s
k
i
f
a
p
p
l
i
e
d
caref
u
l
l
y
Sy
n
er
g
i
s
t
i
c
g
a
i
n
s
v
s
.
s
e
p
ar
at
e
met
h
o
d
s
H
i
g
h
er
t
o
o
l
/
ru
n
t
i
me
co
m
p
l
e
x
i
t
y
t
h
a
n
s
i
n
g
l
e
m
et
h
o
d
s
4
Cl
u
s
t
er
+
M
ST
o
r
d
er
i
n
g
(N
a
v
i
n
K
u
mar)
cl
u
s
t
er
i
n
g
t
h
en
MST
/
K
r
u
s
k
al
[3
7
]
N
e
u
t
ra
l
12
–
30%
10
–
25%
N
o
n
e
L
o
w
–
Med
i
u
m
N
o
i
m
p
ac
t
Fas
t
,
d
e
t
erm
i
n
i
s
t
i
c;
s
ca
l
e
s
t
o
m
an
y
v
ec
t
o
r
s
G
ai
n
s
m
o
d
es
t
v
s
.
h
ea
v
y
met
ah
eu
r
i
s
t
i
c
s
;
d
e
p
e
n
d
s
o
n
cl
u
s
t
er
i
n
g
q
u
al
i
t
y
5
G
en
e
t
i
c
A
l
g
o
r
i
t
h
m
(G
A
)
-
o
r
d
er
i
n
g
b
y
G
A
[
3
8
]
N
e
u
t
ra
l
/
Po
s
s
i
b
l
e
↑
(s
ea
rc
h
t
i
me)
25
–
55%
25
–
50%
N
o
n
e
H
i
g
h
N
o
i
m
p
ac
t
H
i
g
h
-
q
u
a
l
i
t
y
s
o
l
u
t
i
o
n
s
;
f
l
e
x
i
b
l
e
o
b
j
e
ct
i
v
e
d
e
s
i
g
n
H
i
g
h
ru
n
t
i
me,
p
o
o
r
s
ca
l
a
b
i
l
i
t
y
t
o
h
u
g
e
p
at
t
er
n
s
e
t
s
8
RPRF
/
L
BI
ST
-
o
r
i
e
n
t
e
d
T
PI
(Su
n
,
t
h
e
s
i
s
)
[3
0
]
May
↑
(ex
t
ra
p
a
t
t
ern
s
)
15
–
35%
15
–
30%
Sma
l
l
–
Mo
d
er
at
e
H
i
g
h
(an
a
l
y
s
i
s
)
Po
t
e
n
t
i
a
l
l
y
i
m
p
r
o
v
es
ran
d
o
m
-
fau
l
t
co
v
era
g
e
Imp
ro
v
e
s
L
B
IS
T
d
e
t
ec
t
a
b
i
l
i
t
y
;
t
h
eo
ry
-
b
ac
k
e
d
Co
m
p
l
e
x
p
r
o
b
ab
i
l
i
s
t
i
c
an
a
l
y
s
i
s
,
s
p
ec
i
al
i
z
ed
t
o
L
BI
ST
9
T
i
mi
n
g
-
a
w
are
s
i
mu
l
a
t
i
o
n
-
b
a
s
e
d
T
PI
(V
e
t
h
am
u
t
h
u
et
al
.
,
2
0
2
3
)
[4
2
]
T
e
s
t
t
i
me
n
e
u
t
ral
/
may
↑
(T
PI
t
e
s
t
s
)
20
–
40%
20
–
35%
Sma
l
l
H
i
g
h
L
o
w
r
i
s
k
(t
i
mi
n
g
-
aw
a
re
p
re
s
er
v
e
s
co
v
era
g
e
)
T
ar
g
e
t
s
h
i
g
h
-
co
m
p
l
e
x
i
t
y
S
o
C
s
;
l
o
w
o
v
e
rh
ea
d
T
PI
E
x
p
en
s
i
v
e
s
i
mu
l
a
t
i
o
n
;
s
e
n
s
i
t
i
v
e
t
o
v
ec
t
o
r
q
u
a
l
i
t
y
10
D
ee
p
T
PI
(G
N
N
+
D
Q
N
)
[4
7
]
N
e
u
t
ra
l
/
T
ra
i
n
i
n
g
t
i
me
↑
30
–
60%
25
–
50%
Sma
l
l
(p
o
i
n
t
s
i
n
s
e
rt
e
d
)
H
i
g
h
(M
L
t
ra
i
n
i
n
g
+
i
n
fra)
T
y
p
i
ca
l
l
y
i
m
p
r
o
v
es
co
v
era
g
e
u
n
d
er
b
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g
e
t
L
ea
rn
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n
u
a
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ce
d
t
ra
d
e
-
o
ff
s
;
o
u
t
p
er
fo
r
ms
h
e
u
r
i
s
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i
cs
h
i
g
h
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v
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o
p
me
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/
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ra
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t
;
rep
r
o
d
u
c
i
b
i
l
i
t
y
co
n
cer
n
s
11
GNN
-
T
PI
(D
A
C
2
0
2
3
)
[4
9
]
N
e
u
t
ra
l
/
T
ra
i
n
i
n
g
↑
25
–
50%
20
–
45%
Sma
l
l
H
i
g
h
Imp
ro
v
e
s
t
ra
d
e
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o
ff
co
v
era
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o
v
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h
ea
d
Scal
ab
l
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n
g
ap
p
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h
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o
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emp
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Co
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n
d
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o
o
l
c
h
a
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n
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n
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e
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ra
t
i
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n
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i
re
d
12
RL
fo
r
s
ca
n
o
r
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er
i
n
g
AND
s
c
h
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l
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n
g
(V
T
S
/
D
A
C
w
o
r
k
s
h
o
p
)
[4
0
],
[
5
1
]
,
[5
3
]
Can
red
u
ce
t
e
s
t
t
i
me
30
–
65%
30
–
60%
N
o
n
e
–
Sma
l
l
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i
g
h
Mu
s
t
b
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v
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l
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Fl
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ad
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;
h
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p
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Req
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s
h
ap
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s
h
ar
d
5.
CO
NCLU
SI
O
N
AND
F
U
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RE
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ARCH
DIR
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L
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Am
o
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s
ca
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a
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ly
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37
m
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test
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lo
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ased
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ased
tech
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tim
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q
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−
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o
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tu
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ly
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co
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s
ca
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h
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r
es
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−
Scalab
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to
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p
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:
Scan
o
p
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tec
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with
m
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p
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s
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I
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f
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in
ter
ac
tio
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s
b
etwe
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r
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to
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F
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No
f
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in
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was r
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m
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ip
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AUTHO
R
CO
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T
h
is
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s
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to
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ip
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co
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Aut
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M
So
Va
Fo
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D
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Vi
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V.
R
ajith
a
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an
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✓
✓
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Ma
m
ath
a
Sam
s
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✓
✓
✓
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✓
C
:
C
o
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p
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u
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:
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So
f
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p
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ter
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o
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f
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en
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k
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DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
d
o
es n
o
t a
p
p
l
y
to
th
is
p
ap
e
r
as n
o
n
ew
d
ata
wer
e
cr
ea
ted
o
r
an
aly
ze
d
in
t
h
is
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
P
e
d
r
a
m,
“
P
o
w
e
r
mi
n
i
mi
z
a
t
i
o
n
i
n
1
c
d
e
si
g
n
:
p
r
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n
c
i
p
l
e
s
a
n
d
a
p
p
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i
c
a
t
i
o
n
s,
”
A
C
M
T
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n
s
a
c
t
i
o
n
s
o
n
D
e
si
g
n
A
u
t
o
m
a
t
i
o
n
o
f
El
e
c
t
r
o
n
i
c
S
y
st
e
m
s
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
3
–
5
6
,
J
a
n
.
1
9
9
6
,
d
o
i
:
1
0
.
1
1
4
5
/
2
2
5
8
7
1
.
2
2
5
8
7
7
.
[
2
]
K
.
M
.
B
u
t
l
e
r
,
J.
S
a
x
e
n
a
,
T
.
F
r
y
a
r
s,
G
.
H
e
t
h
e
r
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n
g
t
o
n
,
A
.
Ja
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n
,
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n
d
J.
Le
w
i
s,
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i
n
i
m
i
z
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n
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p
o
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r
c
o
n
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m
p
t
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o
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sc
a
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t
e
s
t
i
n
g
:
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a
t
t
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r
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t
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o
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a
n
d
D
F
T
t
e
c
h
n
i
q
u
e
s,
”
i
n
Pr
o
c
e
e
d
i
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s
-
I
n
t
e
rn
a
t
i
o
n
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l
T
e
s
t
C
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n
f
e
re
n
c
e
,
2
0
0
4
,
p
p
.
3
5
5
–
3
6
4
,
d
o
i
:
1
0
.
1
1
0
9
/
t
e
st
.
2
0
0
4
.
1
3
8
6
9
7
1
.
[
3
]
S
.
P
o
t
l
u
r
i
,
A
.
S
.
Tr
i
n
a
d
h
,
C
.
S
.
B
a
b
u
,
V
.
K
a
ma
k
o
t
i
,
a
n
d
N
.
C
h
a
n
d
r
a
c
h
o
o
d
a
n
,
“
D
F
T
a
ssi
s
t
e
d
t
e
c
h
n
i
q
u
e
s
f
o
r
p
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k
l
a
u
n
c
h
-
to
-
c
a
p
t
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r
e
p
o
w
e
r
r
e
d
u
c
t
i
o
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d
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r
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n
g
l
a
u
n
c
h
-
on
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s
h
i
f
t
a
t
-
sp
e
e
d
t
e
st
i
n
g
,
”
A
C
M
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ra
n
s
a
c
t
i
o
n
s
o
n
D
e
si
g
n
A
u
t
o
m
a
t
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o
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o
f
El
e
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r
o
n
i
c
S
y
st
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m
s
,
v
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l
.
2
1
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n
o
.
1
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2
0
1
5
,
d
o
i
:
1
0
.
1
1
4
5
/
2
7
9
0
2
9
7
.
[
4
]
S
.
A
h
l
a
w
a
t
a
n
d
J
.
T.
Tu
d
u
,
“
O
n
mi
n
i
mi
z
a
t
i
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8
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J.
C
l
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9
]
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.
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