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C
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1.
I
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RO
D
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I
O
N
An
alo
g
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te
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ated
cir
cu
it
(
I
C
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d
esig
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s
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w
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m
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cir
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co
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t
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s
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m
d
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m
ed
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a
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ev
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th
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[
3
-
6
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.
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ac
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n
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x
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[
7
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.
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p
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[
8
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,
tab
u
s
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r
ch
[
9
]
,
an
d
s
im
u
lat
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n
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[
1
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
132
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I
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1
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13
7
T
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tech
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w
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ap
p
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to
th
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an
alo
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cir
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m
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p
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p
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ted
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m
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if
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ased
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o
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s
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s
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f
r
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m
t
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at
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m
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.
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g
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ar
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p
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ticle
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w
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m
o
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[
1
1
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,
h
ar
m
o
n
y
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ch
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g
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ith
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[
1
2
]
an
d
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t
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o
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tim
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[
1
3
]
.
T
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tech
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tr
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to
p
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v
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e
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[
1
4
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6
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id
e
th
e
b
atter
r
esu
lt
s
.
T
h
e
r
ea
m
i
n
g
p
ar
t
o
f
t
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
Sec
tio
n
2
d
escr
ib
es
th
e
co
m
p
ar
at
o
r
cir
cu
it
s
tr
u
ct
u
r
e
an
d
d
esig
n
s
p
ec
i
f
icatio
n
.
T
h
e
th
ir
d
s
ec
tio
n
p
r
esen
ts
t
h
e
m
at
h
e
m
atica
l
r
ep
r
esen
tatio
n
an
d
th
e
o
p
er
atio
n
s
o
f
s
a
lp
s
w
ar
m
o
p
tim
izatio
n
.
T
h
e
f
o
u
r
t
h
s
ec
t
i
o
n
d
escr
ib
es
th
e
s
i
m
u
latio
n
r
esu
lt
s
an
d
d
is
c
u
s
s
io
n
.
Fin
all
y
,
t
h
e
f
i
f
t
h
s
ec
t
io
n
is
t
h
e
co
n
clu
s
io
n
o
f
w
o
r
k
.
2.
DE
S
I
G
N
SPEC
I
F
I
C
AT
I
O
N
AND
O
B
J
E
CT
I
V
E
F
UNC
T
I
O
N
F
O
R
M
UL
AT
I
O
N
An
o
p
ti
m
al
d
esi
g
n
o
f
C
MO
S c
o
m
p
ar
ato
r
h
a
s
lar
g
e
n
u
m
b
er
o
f
d
esi
g
n
p
ar
a
m
eter
s
.
T
h
e
s
p
ec
ial
k
i
n
d
o
f
d
esig
n
p
r
o
ce
d
u
r
e
r
eq
u
ir
ed
t
o
h
an
d
le
t
h
e
d
esig
n
v
ar
iab
le
s
.
T
h
e
d
esi
g
n
s
p
ec
i
f
icatio
n
s
f
o
r
th
e
co
m
p
ar
ato
r
ar
e
d
c
g
ain
,
s
le
w
r
ate
an
d
p
o
w
er
d
is
s
ip
atio
n
etc.
Fo
r
c
o
m
p
ar
ato
r
d
e
s
ig
n
,
in
p
u
t
b
ias
cu
r
r
en
t,
th
e
tr
an
s
i
s
to
r
len
g
t
h
an
d
w
id
t
h
ar
e
co
n
s
id
er
ed
as
t
h
e
d
esig
n
v
ar
iab
les.
T
h
e
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
ese
v
ar
iab
le
s
u
s
ed
to
i
m
p
le
m
en
t
th
e
d
esi
g
n
p
r
o
ce
s
s
o
f
cir
cu
it.
I
n
o
r
d
er
to
o
b
tain
th
e
o
p
ti
m
al
v
alu
e
o
f
MO
S
tr
an
s
is
to
r
s
ize
s
an
d
b
ias
c
u
r
r
en
t
v
alu
e,
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
is
d
ev
elo
p
ed
f
r
o
m
th
e
d
esig
n
s
p
ec
if
icatio
n
s
o
f
th
e
cir
cu
its
[
1
7
]
.
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
p
r
o
p
o
s
ed
co
m
p
ar
ato
r
is
to
m
i
n
i
m
ize
th
e
to
t
al
ar
ea
o
f
th
e
ch
ip
.
T
h
e
cir
cu
it
s
tr
u
ct
u
r
e
an
d
co
n
f
i
g
u
r
atio
n
s
o
f
th
e
co
m
p
ar
a
to
r
cir
cu
it is
s
h
o
w
n
i
n
Fi
g
u
r
e
1.
Fig
u
r
e
1
.
C
MO
S
t
w
o
s
ta
g
e
co
m
p
ar
ato
r
cir
cu
i
t
2
.
1
.
D
esig
n Cr
it
er
ia
f
o
r
t
he
CM
O
S
T
wo
-
s
t
a
g
e
Co
m
pa
ra
t
o
r
T
h
e
b
asic
id
ea
o
f
a
co
m
p
ar
ato
r
cir
cu
it
is
to
co
m
p
ar
e
th
e
t
w
o
in
p
u
t
s
i
g
n
a
ls
(
in
ter
m
s
o
f
cu
r
r
en
t
o
r
v
o
ltag
e)
an
d
o
u
tp
u
t
s
h
o
w
s
w
h
ich
s
i
g
n
al
is
h
ig
h
.
T
h
e
in
p
u
t
v
ar
iab
les
f
o
r
th
e
d
esig
n
o
f
co
m
p
ar
ato
r
cir
cu
it
ar
e
g
iv
e
n
as
f
o
llo
w
s
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
R
E
S
I
SS
N:
2089
-
4864
A
n
Op
tima
l D
esig
n
o
f CM
OS
Tw
o
S
ta
g
e
C
o
mp
a
r
a
to
r
C
ir
cu
it
Us
in
g
S
w
a
r
m…
(
S
a
s
iku
ma
r
)
133
DD
V
an
d
SS
V
ar
e
th
e
p
o
s
itiv
e
an
d
n
eg
ativ
e
p
o
w
er
s
u
p
p
l
y
r
esp
ec
ti
v
el
y
;
tn
V
an
d
tp
V
ar
e
th
e
NM
OS
an
d
P
MO
S
th
r
esh
o
ld
v
o
ltag
e
r
e
s
p
ec
tiv
el
y
;
'
.
n
n
o
x
KC
=
an
d
'
.
p
p
o
x
KC
=
ar
e
th
e
tr
an
s
co
n
d
u
c
tan
ce
p
ar
am
eter
o
f
NM
O
S
a
n
d
P
MO
S
tr
an
s
is
to
r
s
.
W
h
er
e
n
an
d
p
i
n
d
icate
s
t
h
e
elec
tr
o
n
a
n
d
h
o
le
m
o
b
ilit
y
;
ox
C
is
th
e
g
ate
o
x
id
e
ca
p
ac
itan
ce
p
er
u
n
it a
r
ea
.
T
h
e
d
esig
n
s
tep
s
in
v
o
lv
ed
i
n
t
h
e
co
m
p
ar
ato
r
cir
cu
it a
r
e
as f
o
llo
w
s
[
1
8
]
:
Fin
d
t
h
e
r
an
g
e
o
f
7
D
I
to
s
atis
f
y
s
le
w
r
ate
(
SR
)
7
D
L
I
SR
C
=
(
1
)
66
2
66
2.
[]
DS
p
D
S
WI
L
K
V
=
(
2
)
77
2
77
2.
[]
DS
p
D
S
WI
L
K
V
=
(
3
)
6
2
67
m
v
ds
ds
g
A
gg
=−
+
(
4
)
Fin
d
o
u
t t
h
e
f
ir
s
t sta
g
e
v
o
lta
g
e
g
ain
f
r
o
m
o
v
er
all
g
ai
n
12
10000
v
v
v
A
A
A
=
(
5
)
Fin
d
o
u
t t
h
e
cu
r
r
en
t v
al
u
es
f
o
l
lo
w
i
n
g
th
r
o
u
g
h
M1
,
M2
,
M3
a
n
d
M4
1
2
3
4
5
/
2
D
S
D
S
D
S
D
S
D
S
I
I
I
I
I
=
=
=
=
(
6
)
C
alcu
late
th
e
c
u
r
r
en
t
4
DS
I
,
w
h
er
e
4
44
6
(
/
)
(
/
)
D
S
D
S
WL
II
WL
=
(
7
)
54
2
D
S
D
S
II
=
(8
)
C
alcu
late
th
e
c
u
r
r
en
t
7
DS
I
,
w
h
er
e
5
57
6
(
/
)
(
/
)
DS
DS
WL
II
WL
=
(
9
)
4
5
3
/2
D
S
S
D
D
S
I
I
I
==
(
1
0
)
2
1
5
/2
S
D
S
D
S
D
I
I
I
==
(
1
1
)
Fin
d
t
h
e
v
al
u
e
o
f
5
5
W
L
in
o
r
d
er
to
s
atis
f
y
t
h
e
p
o
s
iti
v
e
I
C
MR.
Evaluation Warning : The document was created with Spire.PDF for Python.
134
I
SS
N
:
2
0
8
9
-
4
864
I
J
R
E
S
Vo
l.
7
,
No
.
3
,
N
o
v
e
m
b
er
2
0
1
8
:
1
3
1
–
13
7
2
11
1
1
[
(
)
]
(
/
)
2
V
SD
N
P
P
D
S
AI
WL
KI
+
=
(
1
2
)
(
)
1
(
m
a
x
)
1
1
1
2
/
(
/
)
S
D
S
A
T
D
D
G
D
S
P
T
V
V
V
I
K
W
L
V
=
−
−
−
(
1
3
)
5
5
2
5
(
)
2
(
/
)
[]
DS
P
D
S
S
A
T
I
WL
KV
=
(
1
4
)
Fin
d
t
h
e
v
al
u
e
o
f
3
3
W
L
in
o
r
d
er
to
s
atis
f
y
t
h
e
n
e
g
ati
v
e
I
C
M
R
.
3
3
2
1
(
m
i
n
)
3
1
2
(
/
)
()
DS
P
G
S
S
T
T
I
WL
K
V
V
V
V
=
−
−
+
(
1
5
)
Fin
d
t
h
e
v
al
u
e
o
f
b
iasi
n
g
r
esis
to
r
(
R
b
)
,
w
h
er
e
8
8
0
SD
b
DS
V
R
I
−
=
(
1
6
)
T
h
e
co
s
t f
u
n
ctio
n
o
f
FP
A
i
s
th
e
g
iv
e
n
b
y
(
i.e
.
T
h
e
to
tal
ch
ip
ar
ea
o
f
an
o
p
er
atio
n
al
a
m
p
li
f
ie
r
)
1
()
N
ii
i
C
F
W
L
=
=
(
1
7
)
W
h
er
e,
N
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
tr
an
s
is
to
r
s
,
W
i a
n
d
L
i
ar
e
th
e
w
id
th
a
n
d
len
g
t
h
o
f
tr
an
s
i
s
to
r
s
.
3.
P
RO
P
O
SE
D
F
L
O
W
E
R
P
O
L
L
I
NAT
I
O
N
AL
G
O
RI
T
H
M
(
F
P
A)
F
O
R
C
M
O
S
T
WO
ST
A
G
E
CO
M
P
ARATO
R
CIRCUI
T
O
P
T
I
M
I
Z
AT
I
O
N
Flo
w
er
p
o
lli
n
atio
n
al
g
o
r
ith
m
is
a
p
o
p
u
latio
n
b
ased
m
eta
-
h
eu
r
is
tic
s
o
p
ti
m
izatio
n
alg
o
r
it
h
m
,
w
h
ic
h
m
i
m
ics
t
h
e
f
lo
w
er
p
o
llin
a
tio
n
p
r
o
ce
s
s
o
f
f
lo
w
er
in
g
p
lan
t
s
an
d
th
e
b
asic
s
tr
u
ct
u
r
e
o
f
FP
A
i
s
p
r
esen
ted
in
[
1
9
]
.
T
h
is
alg
o
r
ith
m
i
s
s
i
m
p
le
i
n
n
at
u
r
e
an
d
it
h
as o
n
l
y
t
w
o
s
ea
r
ch
o
p
er
ato
r
s
n
a
m
el
y
,
t
h
e
g
lo
b
al
s
ea
r
ch
o
p
er
ato
r
an
d
th
e
lo
ca
l
s
ea
r
c
h
o
p
er
ato
r
.
T
h
ese
t
w
o
o
p
er
ato
r
s
n
o
r
m
all
y
u
s
ed
to
iter
ativ
el
y
u
p
d
ate
th
e
ca
n
d
id
ate
s
o
lu
t
io
n
.
T
h
e
m
at
h
e
m
atica
l r
ep
r
esen
tati
o
n
o
f
g
lo
b
al
s
ea
r
ch
o
p
er
ato
r
is
ex
p
r
ess
ed
as,
1*
(
)
(
)
t
t
t
i
i
i
P
P
L
P
g
+
=
+
−
(
1
8
)
W
h
er
e
t
i
P
in
d
icate
s
t
h
e
s
o
l
u
tio
n
i
P
at
t
-
t
h
iter
atio
n
,
*
g
in
d
icate
s
th
e
b
est
s
o
lu
tio
n
i
n
th
e
c
u
r
r
en
t
p
o
p
u
latio
n
,
r
ep
r
esen
ts
th
e
s
ca
le
f
ac
to
r
(
0
.
0
1
=
).
()
L
in
d
icate
s
th
e
le
v
y
f
lig
h
t
s
tep
s
ize.
T
h
e
g
en
er
al
eq
u
atio
n
o
f
lev
y
d
i
s
tr
ib
u
tio
n
w
h
e
n
L
>
0
is
f
o
llo
w
s
a
s
,
0
1
(
)
s
in
(
/
2
)
1
.
,
(
0
)
L
s
s
s
+
(
1
9
)
W
h
er
e
()
in
d
icate
s
t
h
e
s
ta
n
d
ar
d
g
a
m
m
a
f
u
n
ct
io
n
,
s
r
ep
r
esen
t
s
t
h
e
s
tep
s
ize.
T
h
e
m
at
h
e
m
atica
l r
ep
r
esen
tati
o
n
o
f
lo
ca
l sear
ch
o
p
er
ato
r
i
s
ex
p
r
ess
ed
as,
1
()
t
t
t
t
i
i
j
k
P
P
P
P
+
=
+
−
(
2
0
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
R
E
S
I
SS
N:
2089
-
4864
A
n
Op
tima
l D
esig
n
o
f CM
OS
Tw
o
S
ta
g
e
C
o
mp
a
r
a
to
r
C
ir
cu
it
Us
in
g
S
w
a
r
m…
(
S
a
s
iku
ma
r
)
135
W
h
er
e
t
j
P
an
d
t
k
P
in
d
icate
t
w
o
r
an
d
o
m
l
y
s
elec
ted
s
o
lu
t
io
n
s
,
a
n
d
r
ep
r
esen
ts
a
r
an
d
o
m
n
u
m
b
er
in
[
0
,
1
]
.
T
h
e
FP
A
m
o
r
e
lik
el
y
u
s
e
s
t
h
e
lo
ca
l
s
ea
r
ch
o
p
er
ato
r
in
o
r
d
er
to
s
o
lv
e
o
p
ti
m
izatio
n
p
r
o
b
lem
.
T
h
is
ca
n
b
e
ac
h
iev
ed
b
y
t
h
e
p
r
o
b
ab
ilit
y
co
ef
f
icie
n
t
(
p
)
w
h
ic
h
i
s
0
.
8
f
o
r
th
e
lo
ca
l
s
ea
r
ch
o
p
er
ato
r
an
d
0
.
2
f
o
r
th
e
g
lo
b
al
s
ea
r
ch
o
p
er
ato
r
.
T
h
e
Neld
er
-
Me
ad
is
a
lo
ca
l
o
p
ti
m
izati
o
n
tech
n
iq
u
e
u
s
ed
to
i
m
p
r
o
v
e
t
h
e
lo
ca
l
s
ea
r
ch
ex
p
lo
itatio
n
o
f
FP
A
.
T
h
e
s
tep
s
o
f
p
r
o
p
o
s
ed
NM
FP
A
ar
e
as
f
o
llo
w
s
:
Step
1
:
C
o
n
tr
o
l
p
ar
am
eter
s
e
ttin
g
:
t
h
e
p
o
p
u
latio
n
s
ize
N,
th
e
s
w
itc
h
p
r
o
b
ab
ilit
y
p
,
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
an
d
t
h
e
p
ar
a
m
eter
s
f
o
r
th
e
s
i
m
p
le
x
m
et
h
o
d
.
Step
2
: E
v
alu
ate
t
h
e
N
ca
n
d
id
ate
s
o
lu
tio
n
s
an
d
f
i
n
d
th
e
b
est
s
o
lu
tio
n
f
r
o
m
t
h
at.
Step
3
:
B
ased
o
n
s
w
i
tch
p
r
o
b
ab
ilit
y
,
g
e
n
er
ate
a
n
e
w
s
o
lu
ti
o
n
u
s
i
n
g
t
h
e
lo
ca
l
s
ea
r
ch
o
p
e
r
ato
r
o
r
th
e
g
lo
b
al
s
ea
r
ch
o
p
er
ato
r
.
T
h
e
n
e
w
s
o
l
u
tio
n
s
ar
e
b
etter
th
a
n
cu
r
r
en
t
s
o
lu
tio
n
t
h
e
n
u
p
d
ate
th
e
b
es
t so
l
u
tio
n
.
Step
4
:
Select
th
e
n
+1
b
est
s
o
l
u
tio
n
a
n
d
f
o
r
m
a
n
in
itial
s
i
m
p
lex
u
s
i
n
g
Neld
er
-
Me
ad
m
et
h
o
d
.
T
h
en
ex
ec
u
te
m
ti
m
e
s
an
d
r
ep
lace
th
e
p
r
ev
io
u
s
s
elec
ted
n
+1
s
o
lu
tio
n
.
No
w
u
p
d
ate
th
e
cu
r
r
en
t b
est s
o
l
u
tio
n
.
Step
5
: Co
n
ti
n
u
e
iter
atio
n
s
f
o
r
m
s
tep
3
u
n
til t
h
e
en
d
co
n
d
itio
n
s
ati
s
f
ied
.
T
h
e
m
ain
ai
m
o
f
th
is
p
ap
er
is
to
f
i
n
d
t
h
e
le
n
g
t
h
a
n
d
w
id
t
h
o
f
th
e
tr
an
s
is
to
r
b
y
u
tili
zi
n
g
t
h
e
FP
A
.
T
h
e
in
p
u
t
s
p
ec
if
icatio
n
an
d
it
s
r
an
g
e
ar
e
s
h
o
w
n
in
T
ab
le
1
.
T
ab
le
1
.
Desig
n
P
ar
a
m
eter
s
,
T
ec
h
n
o
lo
g
y
a
n
d
C
o
n
s
tan
t
v
al
u
e
s
o
f
T
w
o
-
S
tag
e
Op
er
atio
n
al
Am
p
li
f
ier
I
n
p
u
t
s
,
T
e
c
h
n
o
l
o
g
y
V
a
l
u
e
s
V
DD
1
.
8
V
V
SS
-
1
.
8
V
V
tp
-
0
.
4
3
V
V
tn
0
.
4
8
V
K
n
3
4
5
(
µ
A
/
V
2
)
K
p
5
5
(
µ
A
/
V
2
)
T
e
c
h
n
o
l
o
g
y
0
.
1
8
µ
m
4.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
T
h
is
s
ec
tio
n
d
escr
ib
e
th
e
s
im
u
latio
n
r
es
u
lt
o
f
FP
A
b
as
ed
C
MO
S
co
m
p
ar
ato
r
cir
cu
it
d
esig
n
.
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
o
p
ti
m
i
za
tio
n
alg
o
r
it
h
m
i
s
co
n
s
tr
u
ct
ed
u
s
in
g
M
A
T
L
A
B
f
o
r
th
e
d
esig
n
o
f
C
MO
S
co
m
p
ar
ato
r
.
T
h
e
d
esig
n
p
ar
a
m
eter
s
a
n
d
d
esi
g
n
co
n
s
tr
ai
n
ts
ar
e
co
n
s
id
er
ed
as
th
e
i
n
p
u
t
v
ar
iab
le
f
o
r
o
p
tim
izatio
n
al
g
o
r
ith
m
.
T
h
e
c
o
n
s
ta
n
t
cir
cu
it
d
esi
g
n
v
ar
iab
les
ar
e
tak
en
f
r
o
m
m
o
d
el
p
ar
a
m
eter
ca
lled
GP
DK
1
8
0
n
m
tec
h
n
o
lo
g
y
.
T
h
e
m
ai
n
o
b
j
ec
tiv
e
is
ai
m
ed
to
m
in
i
m
iz
e
th
e
to
tal
c
h
ip
s
ize
o
f
C
MO
S
co
m
p
ar
ato
r
cir
cu
i
t.
T
h
e
r
esu
lt
o
b
tain
ed
f
r
o
m
th
e
f
lo
w
er
p
o
llin
a
tio
n
al
g
o
r
ith
m
b
a
s
ed
co
m
p
ar
ato
r
d
e
s
ig
n
is
co
m
p
ar
ed
w
ith
ex
i
s
ti
n
g
m
et
h
o
d
s
lik
e
Dif
f
er
en
t
ial
E
v
o
lu
tio
n
(
DE
)
an
d
Har
m
o
n
y
Sea
r
ch
(
HS)
alg
o
r
ith
m
[
2
0
]
.
T
h
e
in
p
u
t
v
ar
iab
les
an
d
th
eir
v
alu
e
s
ar
e
g
iv
e
n
i
n
T
ab
le
1
,
in
o
r
d
er
to
d
ef
in
e
th
e
i
n
p
u
t
r
a
n
g
e
o
f
a
n
o
p
ti
m
izatio
n
p
r
o
b
lem
.
T
h
e
co
m
p
ar
ato
r
c
o
s
t
f
u
n
c
tio
n
is
aim
ed
to
m
i
n
i
m
ize
th
e
ch
i
p
ar
ea
less
th
an
3
0
0
µm
2
.
T
h
e
s
i
m
u
latio
n
r
esu
lt
s
s
h
o
w
t
h
at
t
h
e
lea
s
t
c
h
ip
ar
ea
o
f
3
6
.
7
7
µm
2
.
An
o
p
ti
m
al
tr
a
n
s
is
to
r
d
i
m
e
n
s
io
n
v
al
u
e
s
o
f
t
h
e
C
MO
S
co
m
p
ar
ato
r
ar
e
g
iv
e
n
i
n
T
ab
le
2
.
T
o
ev
alu
ate
th
e
e
f
f
icie
n
c
y
o
f
t
h
e
p
r
o
p
o
s
ed
o
p
ti
m
izatio
n
tech
n
iq
u
e
is
co
m
p
ar
ed
w
it
h
t
h
er
tech
n
iq
u
es
ca
lled
d
if
f
er
en
tial
ev
o
lu
tio
n
a
n
d
h
ar
m
o
n
y
s
ea
r
c
h
s
h
o
w
n
i
n
T
ab
le
3
Fig
u
r
e
2
s
h
o
w
t
h
e
e
f
f
icien
c
y
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
in
ter
m
s
o
f
p
o
w
er
d
is
s
ip
atio
n
.
T
h
e
s
i
m
u
l
atio
n
r
esu
lts
s
h
o
w
t
h
at
t
h
e
p
r
o
p
o
s
ed
o
p
tim
iza
tio
n
tech
n
iq
u
e
is
m
o
s
t
s
u
i
tab
le
f
o
r
s
i
m
p
le
a
n
alo
g
cir
c
u
it d
esi
g
n
.
T
ab
le
2
.
Op
tim
al
tr
an
s
is
to
r
d
im
en
s
io
n
f
o
r
C
MO
S t
w
o
s
tag
e
co
m
p
ar
ato
r
D
e
si
g
n
p
a
r
a
me
t
e
r
s
V
a
l
u
e
s
U
n
i
t
s
W
1
/L
1
2
3
.
2
4
/
0
.
1
8
(
µ
m/
µ
m)
W
2
/L
2
2
3
.
2
4
/
0
.
1
8
(
µ
m/
µ
m)
W
3
/L
3
2
.
5
/
0
.
1
8
(
µ
m/
µ
m)
W
4
/L
4
2
.
5
/
0
.
1
8
(
µ
m/
µ
m)
W
5
/L
5
5
.
8
/
0
.
1
8
(
µ
m/
µ
m)
W
6
/L
6
4
7
/
0
.
1
8
(
µ
m/
µ
m)
W
7
/L
7
8
7
.
5
/
.
1
8
(
µ
m/
µ
m)
W
8
/L
8
1
2
.
5
/
.
1
8
(
µ
m/
µ
m)
R
b
88
k
o
h
m
Evaluation Warning : The document was created with Spire.PDF for Python.
136
I
SS
N
:
2
0
8
9
-
4
864
I
J
R
E
S
Vo
l.
7
,
No
.
3
,
N
o
v
e
m
b
er
2
0
1
8
:
1
3
1
–
13
7
T
ab
le
3
.
Desig
n
Sp
ec
i
f
icatio
n
s
R
es
u
lt
o
f
t
h
e
C
MO
S
T
w
o
Sta
g
e
C
o
m
p
ar
ato
r
D
e
si
g
n
c
r
i
t
e
r
i
a
S
p
e
c
i
f
i
c
a
t
i
o
n
s
DE
HS
P
r
o
p
o
se
d
me
t
h
o
d
L
o
a
d
c
a
p
a
c
i
t
a
n
c
e
(
p
F
)
≥
1
0
10
10
12
U
n
i
t
y
g
a
i
n
b
a
n
d
w
i
d
t
h
(
M
H
z
)
≥
1
0
1
6
.
0
5
5
1
7
.
2
5
5
1
8
.
4
3
G
a
i
n
(
d
B
)
>
8
0
8
2
.
4
2
4
8
2
.
9
3
2
8
5
.
8
S
l
e
w
R
a
t
e
(
V
/
µ
s)
≥
1
0
1
6
0
1
6
0
1
2
0
V
i
c
m
i
n
(V)
≥
-
1
.
6
5
-
1
.
6
0
4
2
-
1
.
6
1
4
6
-
1
.
2
1
6
0
V
i
c
m
a
x
(V)
≤
1
.
6
5
1
.
6
4
5
8
1
.
5
9
3
8
1
.
1
9
3
4
C
M
R
R
(
d
B
)
>
8
5
8
7
.
4
7
1
5
8
7
.
8
2
2
3
8
9
.
4
2
A
r
e
a
(
µ
m
2
)
<
3
0
0
8
2
0
0
2
6
5
3
6
.
7
7
P
o
w
e
r
d
i
ssi
p
a
t
i
o
n
(
µ
W
)
≤
1
0
0
0
5
1
1
5
0
8
3
0
9
Fig
u
r
e
2
.
P
o
w
er
d
is
s
ip
atio
n
o
f
t
w
o
s
ta
g
e
co
m
p
ar
ato
r
cir
cu
it
5.
C
O
N
C
L
U
SIO
N
A
n
e
w
s
w
ar
m
in
te
lli
g
en
t
tech
n
iq
u
e
f
o
r
d
eter
m
i
n
i
n
g
t
h
e
tr
a
n
s
is
to
r
s
ize
s
,
in
p
u
t
b
ias
c
u
r
r
en
t
an
d
o
th
er
p
ar
am
eter
s
o
f
C
MO
S
co
m
p
ar
ato
r
is
p
r
esen
ted
.
Flo
w
er
p
o
llin
atio
n
al
g
o
r
it
h
m
(
FP
A
)
h
a
s
s
h
o
w
n
its
e
x
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
ca
p
ab
ilit
y
i
n
f
i
n
d
i
n
g
t
h
e
o
p
ti
m
a
l
d
esig
n
p
ar
a
m
eter
s
i
n
m
u
l
tid
i
m
e
n
s
io
n
al
s
ea
r
ch
s
p
ac
e.
A
t
th
e
s
a
m
e
ti
m
e
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
r
ed
u
ce
s
th
e
ch
ip
a
r
ea
,
p
o
w
er
d
is
s
ip
atio
n
a
n
d
i
n
c
r
ea
s
es
t
h
e
DC
g
ai
n
o
f
C
MO
S
co
m
p
ar
ato
r
.
Si
m
u
latio
n
r
es
u
lt
d
e
m
o
n
s
tr
ates
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
u
cc
ess
f
u
ll
y
m
et
th
e
cir
c
u
it
d
es
ig
n
s
p
ec
if
icat
io
n
.
T
h
e
s
i
m
u
latio
n
r
es
u
lts
s
h
o
w
th
at
t
h
e
FP
A
o
p
ti
m
izatio
n
m
e
t
h
o
d
is
e
f
f
icie
n
t
m
et
h
o
d
f
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th
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esig
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o
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m
p
le
an
alo
g
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c
u
its
.
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R
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NC
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S
[1
]
G
.
G
.
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G
iele
n
a
n
d
R.
A
.
Ru
t
e
n
b
a
r,
“
Co
m
p
u
ter
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a
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d
d
e
sig
n
o
f
a
n
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lo
g
a
n
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m
ix
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d
-
sig
n
a
l
in
teg
ra
ted
c
ircu
it
s,”
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c
e
e
d
in
g
s
o
f
t
h
e
IEE
E
,
2
0
0
0
,
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l.
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8
,
n
o
.
1
2
,
p
p
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8
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5
–
1
8
5
4
.
[2
]
D.
H
a
ld
a
r,
S
.
P
a
n
w
a
r,
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.
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m
a
r,
A
.
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o
s
w
a
m
i,
a
n
d
S
.
Dh
a
w
a
n
,
“
Circu
it
s
f
o
r
o
p
ti
c
a
l
b
a
se
d
li
n
e
o
f
sig
h
t
v
o
ic
e
c
o
m
m
u
n
ica
ti
o
n
,
”
Bu
l
letin
o
f
E
lec
trica
l
En
g
i
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e
e
rin
g
a
n
d
In
fo
rm
a
t
ics
,
2
0
1
7
,
v
o
l.
6
,
n
o
.
1
.
,
p
p
.
7
6
–
80.
[3
]
M
e
d
e
iro
,
F
.
,
R
o
d
r
ig
u
e
z
,
R.
,
F
e
r
n
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n
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e
z
,
F
.
V
.
,
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g
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e
z
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,
H
u
e
rtas
,
J.
L
.
,
&
R
o
d
r
ig
u
e
z
A
.
”
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lo
b
a
l
d
e
sig
n
o
f
a
n
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lo
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ll
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u
si
n
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sta
ti
stica
l
o
p
t
i
m
iz
a
ti
o
n
tec
h
n
iq
u
e
s,”
A
n
a
l
o
g
i
n
t
e
g
ra
ted
c
irc
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it
s
a
n
d
si
g
n
a
l
p
ro
c
e
ss
in
g
,
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9
9
4
,
v
o
l.
6
,
n
o
.
3
,
p
p
.
1
7
9
–
1
9
5
.
[4
]
S
il
v
e
i
ra
,
F
.
,
F
lan
d
re
,
D.,
&
Je
sp
e
rs,
P
.
G
.
A
.
“
A
g
m
/Id
b
a
se
d
m
e
t
h
o
d
o
l
o
g
y
f
o
r
th
e
d
e
sig
n
o
f
CM
O
S
a
n
a
lo
g
c
ircu
it
s
a
n
d
it
s
a
p
p
li
c
a
ti
o
n
to
t
h
e
sy
n
th
e
sis
o
f
a
S
OI
m
icro
p
o
w
e
r
OTA
,
”
I
EE
E
J
o
u
rn
a
l
o
f
so
li
d
sta
te
c
irc
u
it
s,
1
9
9
6
,
v
o
l.
3
1
,
n
o
.
9
,
p
p
.
1
3
1
4
–
1
3
1
9
.
[5
]
Oc
o
n
n
o
r,
I
.
,
&
Ka
ise
r,
A
.
“
A
u
to
m
a
ted
s
y
n
th
e
sis
o
f
c
u
rre
n
t
m
e
m
o
ry
c
e
ll
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
c
o
mp
u
ter
a
id
e
d
d
e
sig
n
o
f
i
n
teg
r
a
ted
c
irc
u
it
s
a
n
d
s
y
ste
ms
,
2
0
0
0
,
v
o
l.
1
9
,
n
o
.
4
,
p
p
.
4
1
3
–
4
2
4
.
[6
]
L
o
u
lo
u
,
M
.
,
A
it
A
li
,
S
.
,
F
a
k
h
f
a
k
h
,
M
.
,
&
M
a
sm
o
u
d
i,
N
.
“
A
n
o
p
ti
m
iz
e
d
m
e
th
o
d
o
lo
g
y
to
d
e
sig
n
C
M
OS
o
p
e
ra
ti
o
n
a
l
a
m
p
li
f
ier
,
”
IEE
E
In
ter
n
a
ti
o
n
a
l
c
o
n
fer
e
n
c
e
o
n
mic
ro
e
lec
tro
n
ic,
IC
M
’2
0
0
2
,
De
c
e
m
b
e
r
1
4
–
1
6
,
2
0
0
2
.
Be
iru
t,
L
e
b
a
n
o
n
.
[7
]
T
a
lb
i,
E.
G
.
“
A
ta
x
o
n
o
m
y
o
f
h
y
b
rid
m
e
ta
-
h
e
u
risti
c
s,”
J
o
u
rn
a
l
o
f
He
u
ristics
,
2
0
0
2
,
v
o
l
.
8
,
p
p
.
5
4
1
–
5
6
4
.
[8
]
A
a
rts,
E.
,
&
L
e
n
stra
,
K.
“
L
o
c
a
l
se
a
rc
h
in
c
o
mb
i
n
a
to
ria
l
o
p
ti
miz
a
ti
o
n
,
”
P
ri
n
c
e
to
n
:
P
ri
n
c
e
to
n
U
n
iv
e
rsity
P
re
ss
,
2
0
0
3
.
[9
]
G
lo
v
e
r,
F
.
“
T
a
b
u
se
a
rc
h
-
p
a
rt
I,
”
ORS
A
J
o
u
rn
a
l
o
n
c
o
m
p
u
ti
n
g
,
1
9
8
9
,
v
o
l.
1
n
o
.
3
,
p
p
.
1
9
0
–
2
0
6
.
0
10
0
20
0
30
0
40
0
50
0
60
0
DE
HS
P
r
o
po
s
ed
Me
tho
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
R
E
S
I
SS
N:
2089
-
4864
A
n
Op
tima
l D
esig
n
o
f CM
OS
Tw
o
S
ta
g
e
C
o
mp
a
r
a
to
r
C
ir
cu
it
Us
in
g
S
w
a
r
m…
(
S
a
s
iku
ma
r
)
137
[1
0
]
Dre
o
,
J.,
P
e
tr
o
w
sk
i,
A
.
,
S
iarry
,
P
.
,
&
T
a
il
lard
,
E.
“
M
e
ta
-
h
e
u
r
isti
c
s
fo
r
h
a
rd
o
p
ti
miza
t
io
n
:
M
e
th
o
d
s
a
n
d
c
a
se
stu
d
ies
,
”
Ne
w
Yo
rk
:
S
p
ri
n
g
e
r,
2
0
0
6
.
[1
1
]
R.
A
.
V
u
ra
l
a
n
d
T
.
Yild
ir
im
,
“
A
n
a
lo
g
c
ircu
it
siz
in
g
v
ia
s
w
a
r
m
in
telli
g
e
n
c
e
,
”
AE
U
-
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
C
o
mm
u
n
ica
ti
o
n
s,
2
0
1
2
,
v
o
l.
6
6
,
n
o
.
9
.
[1
2
]
D.
K.
S
a
m
b
a
ri
y
a
a
n
d
S
.
S
h
ra
n
g
i,
“
Op
ti
m
a
l
d
e
si
g
n
o
f
P
ID co
n
tr
o
ll
e
r
f
o
r
lo
a
d
f
re
q
u
e
n
c
y
c
o
n
tro
l
u
sin
g
h
a
rm
o
n
y
se
a
rc
h
a
lg
o
rit
h
m
,
”
In
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
E
n
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
,
2
0
1
7
,
v
o
l.
5
,
n
o
.
1
,
p
p
.
1
9
–
32.
[1
3
]
Do
rig
o
,
M
.
,
DiCa
ro
,
G
.
,
&
G
a
m
b
a
rd
e
ll
a
,
L
.
M
.
“
A
n
t
a
lg
o
rit
h
m
s
f
o
r
d
isc
re
te
o
p
ti
m
iza
ti
o
n
,
”
Arti
fi
c
ia
l
L
i
fe
J
o
u
rn
a
l,
1
9
9
9
,
v
o
l.
5
,
p
p
.
1
3
7
–
1
7
2
.
[1
4
]
K.
L
e
n
in
,
“
Em
b
e
ll
ish
e
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
f
o
r
so
lv
in
g
re
a
c
ti
v
e
p
o
w
e
r
p
ro
b
l
e
m
,
”
In
d
o
n
e
sia
n
Jo
u
rn
a
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
c
s,
2
0
1
7
,
v
o
l
.
5
,
n
o
.
3
,
p
p
.
1
9
2
-
1
9
8
.
[1
5
]
P
ra
sa
d
B
,
M
a
n
d
a
l
D,
G
h
o
sh
a
l
S
P
.
“
P
S
O
w
it
h
a
g
in
g
lea
d
e
r
a
n
d
c
h
a
ll
e
n
g
e
rs
f
o
r
o
p
ti
m
a
l
d
e
sig
n
o
f
h
ig
h
sp
e
e
d
s
y
m
m
e
tri
c
sw
it
c
h
in
g
CM
OS
i
n
v
e
rter”
.
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
M
a
c
h
in
e
L
e
a
rn
in
g
a
n
d
Cy
b
e
rn
e
ti
c
s.
2
0
1
6
;
8
(4
)
:
1
4
0
3
-
1
4
2
2
.
[1
6
]
A
s
a
it
h
a
m
b
i
S
a
n
d
Ra
jap
p
a
M
.
“
S
w
a
r
m
in
telli
g
e
n
c
e
-
b
a
se
d
a
p
p
r
o
a
c
h
f
o
r
o
p
t
im
a
l
d
e
sig
n
o
f
C
M
OS
d
if
f
e
r
e
n
ti
a
l
a
m
p
li
f
ier
a
n
d
c
o
m
p
a
ra
to
r
c
ircu
i
t
u
si
n
g
a
h
y
b
rid
sa
lp
sw
a
r
m
a
l
g
o
rit
h
m
”
.
Rev
iew
o
f
S
c
ien
t
if
ic
I
n
stru
me
n
,
2
0
1
8
v
o
l:
8
9
(5
).
[1
7
]
V
.
Bh
a
ti
a
,
N.
P
a
n
d
e
y
,
a
n
d
A
.
Bh
a
tt
a
c
h
a
r
y
y
a
,
“
Hig
h
sp
e
e
d
p
o
w
e
r
e
ff
ici
e
n
t
CM
OS
in
v
e
rter
b
a
se
d
c
u
rre
n
t
c
o
m
p
a
ra
to
r
in
UMC
9
0
n
m
tec
h
n
o
l
o
g
y
,
”
In
te
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
,
2
0
1
6
,
v
o
l.
6
,
n
o
.
1
.
[1
8
]
P
.
E.
A
ll
e
n
a
n
d
D.
R
.
Ho
l
b
e
rg
,
“
CM
OS
a
n
a
lo
g
c
irc
u
it
d
e
s
ig
n
,
”
O
x
f
o
rd
Un
iv
e
rsit
y
P
re
ss
,
USA
,
2
0
1
2
.
[1
9
]
X
.
Ya
n
g
,
M
.
Ka
ra
m
a
n
o
g
lu
,
a
n
d
X
.
He
,
“
F
lo
w
e
r
p
o
ll
i
n
a
ti
o
n
a
l
g
o
rit
h
m
:
A
n
o
v
e
l
a
p
p
ro
a
c
h
f
o
r
m
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
,
”
E
n
g
i
n
e
e
rin
g
Op
ti
miza
ti
o
n
,
2
0
1
4
,
v
o
l.
4
6
,
n
o
.
9
,
p
p
.
1
2
2
2
–
1
2
3
7
.
[2
0
]
V
u
ra
l
RA
,
Bo
z
k
u
rt
U,
Yild
ir
im
T,
“
M
e
ta
-
h
e
u
risti
c
s
b
a
se
d
CM
OS
t
w
o
-
sta
g
e
c
o
m
p
a
ra
to
r
o
p
ti
m
iza
ti
o
n
”
,
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
W
o
rld
C
o
n
g
re
ss
o
n
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
2
0
1
3
,
v
o
l.
2
,
p
p
.
6
4
5
–
6
5
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.