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10
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No
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Feb
r
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ar
y
2
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2
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79
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G
A
)
.
I
n
th
is
p
ap
er
,
th
e
o
p
tim
al
t
r
an
s
ie
n
t
b
eh
av
io
r
o
f
C
MO
S
in
v
er
ter
is
in
v
est
ig
ated
u
s
i
n
g
a
n
e
w
o
p
tim
izatio
n
m
et
h
o
d
,
th
e
s
y
m
b
io
tic
o
r
g
an
i
s
m
s
s
ea
r
ch
(
SOS)
[
9
]
.
Sp
ec
if
icall
y
,
SO
S
is
u
s
ed
to
f
i
n
d
th
e
o
p
ti
m
u
m
v
al
u
es
o
f
th
e
w
id
th
s
a
n
d
len
g
t
h
s
o
f
t
h
e
c
h
an
n
el
s
o
f
th
e
N
MO
S
a
n
d
P
MO
S
tr
an
s
i
s
to
r
s
alo
n
g
w
it
h
th
e
lo
ad
ca
p
ac
ito
r
th
at
g
iv
e
s
y
m
m
etr
ic
s
w
i
tch
i
n
g
c
h
ar
ac
ter
is
tics
o
f
t
h
e
i
n
v
er
ter
.
Sin
ce
i
ts
i
n
v
e
n
tio
n
,
SOS
h
a
s
b
ee
n
u
s
ed
in
s
e
v
er
al
o
p
ti
m
iz
atio
n
p
r
o
b
le
m
s
i
n
e
n
g
in
ee
r
i
n
g
.
I
n
[
1
0
,
1
1
]
,
SOS
h
as
b
ee
n
ap
p
lied
to
s
o
lv
e
th
e
o
p
ti
m
al
p
o
w
er
f
lo
w
a
n
d
th
e
ec
o
n
o
m
ic
e
m
is
s
io
n
lo
ad
d
is
p
atch
p
r
o
b
le
m
s
.
Mo
r
eo
v
er
,
SOS
h
as
b
ee
n
s
u
cc
e
s
s
f
u
ll
y
ap
p
lied
in
th
e
o
p
ti
m
al
d
esi
g
n
o
f
l
in
ea
r
a
n
d
cir
cu
lar
a
n
ten
n
a
ar
r
a
y
s
[
1
2
,
1
3
]
,
in
s
o
l
v
i
n
g
t
h
e
lo
ad
f
r
eq
u
e
n
c
y
co
n
tr
o
l
p
r
o
b
le
m
[
1
4
]
,
i
n
s
tr
u
ct
u
r
e
o
p
ti
m
izat
io
n
p
r
o
b
lem
s
[
1
5
]
,
in
s
o
lv
i
n
g
u
n
c
o
n
s
tr
ain
ed
f
u
n
c
tio
n
o
p
tim
izatio
n
[
1
6
]
,
in
t
h
e
d
esi
g
n
o
f
an
i
m
p
r
o
v
ed
3
D
T
u
r
b
o
C
o
d
e
[
1
7
]
,
in
t
h
e
o
p
ti
m
al
d
es
i
g
n
o
f
an
a
lo
g
ac
tiv
e
f
ilter
s
[
1
8
]
,
in
th
e
s
y
n
t
h
esi
s
o
f
ellip
tical
an
ten
n
a
ar
r
a
y
s
[
1
9
]
,
an
d
o
th
er
en
g
in
ee
r
i
n
g
ar
ea
s
[
2
0
-
24]
.
T
h
e
r
esu
lts
in
d
icate
d
th
at
SO
S
g
i
v
es
v
er
y
g
o
o
d
r
esu
lts
an
d
is
v
er
y
co
m
p
etitiv
e
w
it
h
t
h
e
s
tate
o
f
t
h
e
ar
t
f
o
r
th
e
s
o
lu
tio
n
o
f
th
ese
p
r
o
b
le
m
s
,
w
h
ic
h
m
o
ti
v
ated
th
is
w
o
r
k
.
T
h
e
SOS
r
e
s
u
lts
w
ill
b
e
co
m
p
ar
ed
w
it
h
t
h
o
s
e
o
b
tain
ed
u
s
i
n
g
d
if
f
er
e
n
t
o
p
ti
m
izatio
n
m
e
th
o
d
s
av
ai
lab
le
in
th
e
liter
at
u
r
e
[
3
-
7
]
.
Mo
r
eo
v
er
,
SOS
r
esu
lt
s
w
il
l
b
e
co
m
p
ar
ed
w
it
h
th
o
s
e
o
b
tain
ed
b
y
t
h
e
P
SP
I
C
E
s
i
m
u
lato
r
.
T
h
e
p
ap
er
is
d
iv
id
ed
as
f
o
llo
w
s
:
SO
S
alg
o
r
it
h
m
is
b
r
ief
l
y
d
escr
ib
ed
in
Sectio
n
2
.
T
h
e
in
ter
ested
r
ea
d
er
m
a
y
co
n
s
u
lt
th
e
ab
o
v
e
r
e
f
er
en
ce
s
f
o
r
m
o
r
e
d
etails
o
n
th
e
SOS
a
lg
o
r
it
h
m
.
I
n
Sectio
n
3
,
th
e
f
o
r
m
u
lat
io
n
o
f
th
e
p
r
o
b
lem
i
s
p
r
esen
ted
,
w
h
ile
n
u
m
er
ical
r
es
u
lts
a
n
d
d
is
cu
s
s
io
n
ar
e
p
r
esen
ted
in
S
ec
tio
n
4
.
Sectio
n
5
co
n
clu
d
es t
h
e
p
ap
er
.
2.
SO
S AL
G
O
RI
T
H
M
S
y
m
b
io
tic
Or
g
an
is
m
s
Sear
c
h
(
SOS)
h
as
b
ee
n
r
ec
en
tl
y
p
r
o
p
o
s
ed
in
[
9
]
as
an
ef
f
ec
ti
v
e
ev
o
lu
tio
n
ar
y
g
lo
b
al
o
p
ti
m
izatio
n
m
e
th
o
d
.
It
is
b
ased
o
n
t
h
e
s
y
m
b
io
tic
in
te
r
ac
tio
n
s
tr
ate
g
ies
b
et
w
ee
n
d
if
f
er
en
t
o
r
g
an
i
s
m
s
i
n
an
ec
o
s
y
s
te
m
.
S
y
m
b
io
s
is
d
esc
r
ib
es th
e
i
n
ter
ac
tio
n
b
et
w
ee
n
d
if
f
er
e
n
t sp
ec
ie
s
li
v
in
g
to
g
eth
er
.
I
n
SOS,
t
he
t
h
r
ee
m
ai
n
t
y
p
e
s
o
f
s
y
m
b
io
tic
r
elati
o
n
s
h
ip
s
(
m
u
t
u
ali
s
m
,
co
m
m
e
n
s
alis
m
,
an
d
p
ar
asi
tis
m
)
ar
e
m
o
d
eled
u
s
i
n
g
s
i
m
p
le
m
at
h
e
m
a
tical
e
x
p
r
ess
io
n
s
.
An
ex
a
m
p
le
o
f
m
u
t
u
ali
s
m
,
in
w
h
ich
b
o
t
h
o
r
g
an
i
s
m
s
b
en
ef
it
f
r
o
m
s
u
c
h
a
r
elatio
n
s
h
ip
,
is
th
e
in
ter
ac
tio
n
b
et
w
ee
n
b
ee
s
an
d
f
lo
w
er
s
.
An
ex
a
m
p
le
o
f
co
m
m
e
n
s
ali
s
m
,
w
h
er
e
o
n
e
s
p
ec
ie
s
b
en
ef
it
s
f
r
o
m
t
h
e
i
n
ter
ac
tio
n
,
w
h
i
le
t
h
e
o
t
h
er
d
o
es
n
o
t
g
a
in
o
r
lo
s
e
a
n
y
t
h
i
n
g
,
is
t
h
e
r
e
latio
n
s
h
ip
b
et
w
ee
n
r
e
m
o
r
a
f
i
s
h
an
d
s
h
ar
k
s
.
An
ex
a
m
p
le
o
f
p
ar
asit
is
m
,
w
h
er
e
th
e
r
elatio
n
s
h
ip
b
en
ef
it
s
o
n
e
s
p
e
cies
w
h
ile
t
h
e
o
th
er
s
p
ec
ies
i
s
h
ar
m
ed
,
i
s
t
h
e
in
t
er
ac
tio
n
b
et
w
ee
n
t
h
e
p
las
m
o
d
iu
m
p
ar
asit
es
an
d
h
u
m
an
s
.
On
e
o
f
th
e
m
a
in
ad
v
an
ta
g
es
in
u
s
i
n
g
SO
S
is
t
h
e
f
ac
t
t
h
at
it
i
s
f
r
ee
f
r
o
m
a
n
y
t
u
n
in
g
p
ar
a
m
eter
s
.
I
n
it
s
i
m
p
le
m
e
n
tat
io
n
,
S
OS
r
eq
u
ir
es
o
n
l
y
co
m
m
o
n
co
n
tr
o
llin
g
p
ar
a
m
eter
s
li
k
e
p
o
p
u
la
tio
n
s
ize
(
n
u
m
b
er
o
f
o
r
g
an
is
m
s
)
a
n
d
m
a
x
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
Si
m
i
lar
to
m
o
s
t
e
v
o
lu
tio
n
ar
y
o
p
tim
izatio
n
m
et
h
o
d
s
,
th
e
f
ir
s
t
s
tep
i
n
SO
S
is
to
r
an
d
o
m
l
y
g
e
n
er
ate
an
i
n
itial
p
o
p
u
latio
n
o
f
o
r
g
a
n
i
s
m
s
(
w
h
ich
is
ca
lled
t
h
e
ec
o
s
y
s
te
m
)
.
T
h
e
n
u
m
b
er
o
f
g
e
n
er
ated
o
r
g
an
is
m
s
N
is
ca
lled
th
e
p
o
p
u
latio
n
s
ize.
T
h
u
s
,
t
h
e
ec
o
s
y
s
te
m
i
s
a
n
N
x
D
m
atr
i
x
,
w
h
er
e
D
is
t
h
e
d
i
m
e
n
s
io
n
o
f
th
e
p
r
o
b
lem
(
i.e
.
,
th
e
n
u
m
b
er
o
f
d
esi
g
n
v
ar
iab
les).
An
o
r
g
a
n
i
s
m
(
in
d
iv
id
u
al
)
X
i
(
i=1
,
…,
N
)
w
it
h
in
t
h
e
ec
o
s
y
s
te
m
(
p
o
p
u
latio
n
)
is
a
r
ea
l
-
v
alu
ed
v
ec
to
r
w
it
h
D
ele
m
en
t
s
w
h
ich
r
ep
r
esen
ts
a
s
i
n
g
le
p
o
s
s
ib
le
s
o
lu
tio
n
to
a
s
p
ec
if
ic
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
s
e
co
n
d
s
tep
is
to
as
s
o
ciate
e
ac
h
o
r
g
an
i
s
m
X
i
o
f
t
h
e
ec
o
s
y
s
te
m
(
i.e
.
,
ea
ch
r
o
w
in
th
e
ec
o
s
y
s
te
m
m
atr
i
x
)
w
ith
a
ce
r
tain
v
al
u
e
o
f
th
e
f
it
n
es
s
f
u
n
ctio
n
w
h
ic
h
i
m
i
tates
t
h
e
d
eg
r
ee
o
f
ad
ap
tati
o
n
t
o
th
e
d
esire
d
o
b
j
ec
tiv
e.
Af
ter
w
ar
d
s
,
n
e
w
s
o
l
u
tio
n
s
ar
e
cr
ea
ted
b
y
s
i
m
u
lati
n
g
th
e
ab
o
v
e
m
en
t
io
n
ed
t
h
r
ee
s
y
m
b
io
tic
i
n
ter
ac
tio
n
s
b
et
w
ee
n
t
h
e
o
r
g
an
is
m
s
i
n
t
h
e
ec
o
s
y
s
te
m
.
E
ac
h
o
r
g
an
i
s
m
i
n
th
e
ec
o
s
y
s
te
m
r
a
n
d
o
m
l
y
in
ter
ac
ts
w
i
th
o
t
h
er
o
r
g
an
is
m
s
th
r
o
u
g
h
all
t
h
ese
t
h
r
ee
p
h
a
s
es
a
n
d
n
e
w
o
r
g
a
n
is
m
s
ar
e
cr
ea
ted
w
h
ich
h
a
v
e
b
etter
f
itn
e
s
s
f
u
n
ctio
n
v
al
u
e.
T
h
is
p
r
o
ce
s
s
is
r
ep
ea
ted
u
n
til
a
s
p
ec
if
ic
ter
m
i
n
atio
n
cr
iter
i
o
n
(
e.
g
.
,
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
)
is
f
u
l
f
ill
ed
.
I
n
ea
ch
iter
atio
n
,
ev
er
y
o
r
g
an
i
s
m
X
i
(
i=1
,
…,
N
)
g
o
es
th
r
o
u
g
h
t
h
e
f
o
llo
w
in
g
th
r
ee
p
h
ase
s
:
2
.
1
.
M
utua
lis
m
ph
a
s
e
Firstl
y
,
an
o
th
er
o
r
g
a
n
is
m
X
j
i
s
r
an
d
o
m
l
y
s
elec
ted
to
“m
u
t
u
a
ll
y
”
i
n
ter
ac
t
w
it
h
X
i
.
T
h
en
,
X
i
an
d
X
j
ar
e
u
p
d
ated
ac
co
r
d
in
g
to
th
e
f
o
llo
w
i
n
g
ex
p
r
ess
io
n
s
[
9
]:
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Op
tima
l d
esig
n
o
f symme
tr
ic
s
w
itch
in
g
C
MOS
in
ve
r
ter u
s
in
g
s
ymb
io
tic
o
r
g
a
n
is
ms s
ea
r
ch
…
(
N
ih
a
d
Dib
)
173
(
2
)
w
h
er
e
th
e
(
Mu
tu
a
l
V
ec
to
r
)
is
th
e
a
v
er
ag
e
b
etw
ee
n
th
e
t
w
o
in
ter
ac
ti
n
g
o
r
g
an
i
s
m
s
,
i.e
.
,
(
Mu
tu
a
l
V
ec
to
r
)
=
(
X
i
+X
j
)
/
2
.
I
n
t
h
e
ab
o
v
e
eq
u
atio
n
s
,
X
best
is
th
e
b
est
o
r
g
a
n
i
s
m
(
th
a
t
is
t
h
e
o
n
e
t
h
at
r
es
u
lted
i
n
th
e
m
in
i
m
u
m
v
alu
e
o
f
t
h
e
f
it
n
es
s
f
u
n
ct
io
n
)
,
a
n
d
r
a
n
d
(
0
,
1
)
is
a
r
a
n
d
o
m
n
u
m
b
er
in
t
h
e
r
an
g
e
[
0
,
1
]
.
T
h
e
p
ar
am
eter
s
BF
1
an
d
BF
2
ar
e
ca
lled
b
en
ef
it
f
ac
to
r
s
w
h
i
ch
r
ep
r
esen
t
t
h
e
le
v
el
o
f
b
en
e
f
it
b
et
w
ee
n
t
h
e
o
r
g
a
n
is
m
s
as
th
e
o
r
g
a
n
is
m
s
m
a
y
f
u
ll
y
o
r
p
ar
tiall
y
b
e
n
ef
it
f
r
o
m
s
u
c
h
a
r
elatio
n
s
h
ip
.
T
h
e
y
ar
e
r
an
d
o
m
l
y
d
eter
m
i
n
ed
as
eith
e
r
1
o
r
2
.
A
t
th
e
e
n
d
o
f
th
e
m
u
t
u
ali
s
m
p
h
ase,
th
e
n
e
w
o
r
g
an
i
s
m
s
ar
e
ac
ce
p
ted
,
an
d
tak
e
n
to
th
e
n
e
x
t
p
h
ase,
if
th
e
y
g
iv
e
b
etter
(
th
at
is
le
s
s
)
f
i
tn
e
s
s
f
u
n
ctio
n
v
alu
es t
h
a
n
th
eir
p
r
e
-
i
n
ter
ac
tio
n
f
it
n
ess
v
al
u
es.
2
.
2
.
Co
mm
ens
a
li
s
m
p
ha
s
e
Si
m
i
lar
to
th
e
f
ir
s
t
p
h
a
s
e,
a
n
o
r
g
an
is
m
X
j
is
r
a
n
d
o
m
l
y
s
e
lec
ted
to
in
ter
ac
t
w
i
th
X
i
.
Ho
w
ev
er
,
in
t
h
is
p
h
ase,
X
i
tr
ies
to
b
en
ef
it
f
r
o
m
th
e
in
ter
ac
tio
n
,
w
h
ile
X
j
its
e
l
f
is
n
o
t
a
f
f
ec
ted
at
all
.
T
h
e
n,
X
i
is
u
p
d
ated
u
s
in
g
th
e
f
o
llo
w
i
n
g
ex
p
r
es
s
io
n
[
9
]:
(
3
)
T
h
e
ter
m
(
X
bes
t
–
X
j
)
r
ef
lect
s
th
e
b
en
e
f
i
t
p
r
o
v
id
ed
b
y
X
j
t
o
X
i
to
in
cr
ea
s
e
it
s
d
eg
r
ee
o
f
ad
ap
tatio
n
(
to
th
e
h
ig
h
est
d
eg
r
ee
r
ep
r
esen
ted
b
y
X
best
)
s
u
c
h
th
at
it
ca
n
b
etter
s
u
r
v
iv
e
i
n
th
e
ec
o
s
y
s
te
m
.
T
h
e
n
e
w
o
r
g
an
is
m
X
i
(n
e
w
)
is
ad
o
p
ted
,
a
n
d
tak
e
n
to
th
e
th
ir
d
p
h
ase,
if
it
r
esu
lt
s
in
a
b
etter
f
it
n
es
s
f
u
n
ctio
n
v
al
u
e.
I
f
n
o
t,
th
en
X
i
r
e
m
ain
s
t
h
e
s
a
m
e,
w
h
i
ch
m
ea
n
s
t
h
at
it d
id
n
’
t
g
ai
n
an
y
th
in
g
f
r
o
m
s
u
ch
a
n
i
n
ter
ac
t
io
n
w
it
h
X
j
.
2
.
3
.
P
a
ra
s
it
is
m
ph
a
s
e
I
n
th
i
s
last
t
h
ir
d
p
h
ase,
a
n
e
w
v
ec
to
r
ca
lled
“
P
a
r
a
s
ite
V
ec
to
r
”
is
cr
ea
ted
b
y
m
u
ta
tin
g
X
i
in
a
r
an
d
o
m
d
i
m
en
s
io
n
u
s
i
n
g
a
r
an
d
o
m
n
u
m
b
er
.
T
h
en
,
an
o
r
g
an
i
s
m
X
j
is
r
an
d
o
m
l
y
s
elec
ted
to
s
er
v
e
as
a
h
o
s
t
to
th
e
p
ar
asit
e
v
ec
to
r
,
an
d
t
h
e
f
it
n
es
s
f
u
n
ct
io
n
f
o
r
b
o
th
o
r
g
an
i
s
m
s
i
s
ev
al
u
ated
.
I
f
th
e
p
ar
asit
e
v
ec
to
r
h
as
a
b
etter
f
it
n
es
s
,
t
h
en
X
j
i
s
r
e
m
o
v
ed
a
n
d
its
p
o
s
it
io
n
is
tak
e
n
o
v
er
b
y
th
e
p
ar
asit
e
v
ec
to
r
.
O
n
t
h
e
o
th
er
h
an
d
,
i
f
X
j
h
a
s
a
b
etter
f
it
n
es
s
v
al
u
e,
t
h
en
i
t
w
ill
b
e
i
m
m
u
n
e
f
r
o
m
t
h
e
p
ar
asit
e
v
ec
to
r
,
an
d
w
ill
n
o
t
b
e
af
f
ec
ted
b
y
its
ex
is
te
n
ce
.
I
n
t
h
i
s
ca
s
e,
t
h
e
p
ar
asit
e
v
ec
to
r
is
s
i
m
p
l
y
r
e
m
o
v
ed
f
r
o
m
t
h
e
ec
o
s
y
s
te
m
.
A
p
s
e
u
d
o
-
co
d
e
an
d
a
d
etailed
d
escr
ip
tio
n
o
f
th
e
S
OS a
lg
o
r
it
h
m
ca
n
b
e
f
o
u
n
d
in
[
9
]
.
3.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
On
e
o
f
t
h
e
m
o
s
t
i
m
p
o
r
ta
n
t
is
s
u
es
i
n
t
h
e
C
MO
S
in
v
er
te
r
s
is
th
e
s
y
m
m
etr
ic
s
w
itch
i
n
g
s
p
ee
d
.
I
n
C
MO
S
i
n
v
er
ter
s
cir
cu
it
s
,
th
e
s
w
itc
h
i
n
g
s
p
ee
d
is
af
f
ec
t
ed
b
y
th
e
o
u
tp
u
t
r
esi
s
tan
ce
o
f
th
e
h
i
g
h
s
tate
o
f
th
e
P
-
MO
S
FET
,
th
e
ca
p
ac
itiv
e
lo
ad
,
th
e
len
g
th
a
n
d
w
id
t
h
o
f
th
e
P
MO
S
an
d
th
e
NM
O
S
ch
an
n
el
s
an
d
o
n
b
o
th
th
e
in
p
u
t
v
o
lta
g
e
V
in
a
n
d
th
e
p
o
w
er
s
u
p
p
l
y
.
T
h
e
s
w
itc
h
i
n
g
s
p
ee
d
ca
n
b
e
i
m
p
r
o
v
ed
b
y
d
e
cr
ea
s
in
g
th
e
o
u
tp
u
t
r
esis
ta
n
ce
an
d
t
h
is
ca
n
b
e
d
o
n
e
b
y
i
n
cr
ea
s
i
n
g
th
e
p
o
w
er
s
u
p
p
ly
.
Ho
w
e
v
er
,
th
e
lar
g
er
p
o
w
e
r
s
u
p
p
l
y
w
il
l
r
esu
l
t
in
i
n
cr
ea
s
ed
p
o
w
er
d
is
s
ip
atio
n
.
I
n
th
is
p
ap
er
,
th
e
C
M
OS
in
v
er
ter
is
o
p
ti
m
ized
to
o
p
er
ate
at
2
.
5
V,
an
d
th
e
s
w
i
tch
i
n
g
o
p
er
atio
n
is
an
al
y
ze
d
to
d
eter
m
i
n
e
t
h
e
r
is
e
an
d
f
all
ti
m
es
(
t
r
an
d
t
f
)
an
d
p
r
o
p
a
g
atio
n
d
ela
y
s
(
t
pH
L
an
d
t
pLH
).
T
h
e
P
SP
I
C
E
s
ch
em
atic
d
iag
r
a
m
o
f
t
h
e
C
MO
S
i
n
v
er
ter
is
s
h
o
w
n
in
F
ig
u
r
e
1
,
w
h
ile
t
h
e
in
p
u
t
an
d
th
e
o
u
tp
u
t
w
av
e
f
o
r
m
s
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
As
w
el
l
k
n
o
w
n
,
t
h
e
f
all
ti
m
e
(
t
f
)
,
is
t
h
e
t
i
m
e
r
eq
u
ir
ed
f
o
r
t
h
e
o
u
tp
u
t
v
o
lta
g
e
to
d
r
o
p
f
r
o
m
V
90%
o
f
th
e
o
u
tp
u
t
v
o
lta
g
e
lev
e
l
d
o
w
n
to
V
10%,
an
d
th
e
r
is
e
ti
m
e
(
t
r
)
is
d
ef
in
ed
as
th
e
t
i
m
e
r
eq
u
ir
ed
f
o
r
th
e
o
u
tp
u
t
v
o
ltag
e
to
r
is
e
f
r
o
m
V
10%
to
V
9
0%.
T
h
e
y
ca
n
b
e
co
m
p
u
ted
as f
o
llo
w
s
[
3
-
8
]
:
*
(
)
+
(
4
)
[
(
(
)
)
]
(
5
)
On
t
h
e
o
t
h
er
h
a
n
d
,
t
h
e
h
i
g
h
-
to
-
lo
w
p
r
o
p
ag
atio
n
d
ela
y
t
i
m
e
(
t
pHL
)
is
d
ef
i
n
ed
as
th
e
ti
m
e
d
ela
y
b
et
w
ee
n
t
h
e
V
50%
tr
a
n
s
i
tio
n
o
f
t
h
e
r
i
s
i
n
g
in
p
u
t
v
o
lta
g
e
a
n
d
th
e
V
50%
tr
a
n
s
it
io
n
o
f
t
h
e
f
all
i
n
g
o
u
tp
u
t
v
o
ltag
e,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
171
-
1
79
174
an
d
c
an
b
e
co
m
p
u
ted
as
[3
-
8]
:
*
(
)
+
(
6
)
L
ast
l
y
,
t
h
e
lo
w
-
to
-
h
i
g
h
p
r
o
p
a
g
atio
n
d
ela
y
ti
m
e
(
t
pLH
)
is
d
ef
in
ed
as
th
e
ti
m
e
d
ela
y
b
et
w
ee
n
th
e
V
50%
tr
an
s
itio
n
o
f
th
e
f
alli
n
g
i
n
p
u
t
v
o
ltag
e
an
d
th
e
V
50
%
tr
an
s
itio
n
o
f
th
e
r
is
i
n
g
o
u
tp
u
t
v
o
ltag
e
,
an
d
c
an
b
e
co
m
p
u
ted
as
[3
-
8
]
:
[
(
(
|
|
)
)
]
(
7
)
Fig
u
r
e
1
.
C
MO
S i
n
v
er
ter
cir
c
u
it (
as d
r
a
w
n
i
n
P
SIP
I
C
E
)
Fig
u
r
e
2
.
I
n
p
u
t a
n
d
o
u
tp
u
t v
o
lt
ag
e
w
av
e
f
o
r
m
s
o
f
t
h
e
C
MO
S i
n
v
er
ter
T
h
e
co
s
t (
f
itn
es
s
)
f
u
n
ctio
n
to
b
e
m
i
n
i
m
ized
ca
n
b
e
w
r
itte
n
a
s
[3
-
8
]
:
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Op
tima
l d
esig
n
o
f symme
tr
ic
s
w
itch
in
g
C
MOS
in
ve
r
ter u
s
in
g
s
ymb
io
tic
o
r
g
a
n
is
ms s
ea
r
ch
…
(
N
ih
a
d
Dib
)
175
w
h
er
e
|
(
(
(
)
)
)
|
|
(
)
|
(
9
)
Su
b
j
ec
t
to
th
e
f
o
llo
w
i
n
g
co
n
s
t
r
ain
ts
:
(
)
(
)
T
h
u
s
,
t
h
e
o
b
j
ec
tiv
e
o
f
t
h
e
o
p
ti
m
izatio
n
is
to
g
e
t,
as
m
u
c
h
as
p
o
s
s
ib
le,
a
s
y
m
m
etr
ic
r
esp
o
n
s
e
o
f
th
e
in
v
er
ter
,
s
u
c
h
th
at
t
h
e
f
all
an
d
r
is
e
ti
m
es
ar
e
eq
u
al,
an
d
th
e
p
r
o
p
ag
atio
n
d
elay
s
ar
e
eq
u
al.
T
h
er
e
ar
e
th
r
ee
v
ar
iab
les
i
n
t
h
e
o
p
ti
m
izatio
n
p
r
o
b
lem
,
n
a
m
el
y
,
th
e
lo
ad
ca
p
ac
itan
ce
C
L
,
a
n
d
th
e
r
atio
b
et
wee
n
t
h
e
w
id
t
h
s
a
n
d
th
e
len
g
t
h
s
o
f
t
h
e
ch
a
n
n
els,
an
d
.
T
h
u
s
,
th
e
d
i
m
e
n
s
io
n
o
f
th
e
o
p
ti
m
izatio
n
p
r
o
b
le
m
is
3
.
T
h
e
m
i
n
i
m
u
m
a
n
d
m
a
x
i
m
u
m
v
al
u
es
o
f
th
e
ab
o
v
e
co
n
s
tr
ai
n
ts
ar
e
s
h
o
w
n
i
n
T
ab
le
1
,
w
h
er
e
s
i
m
ilar
to
[
3
-
8
]
,
8
d
if
f
er
en
t d
es
ig
n
s
et
s
ar
e
co
n
s
id
er
ed
in
th
i
s
p
ap
er
.
Mo
r
e
o
v
er
,
s
i
m
ilar
to
[
1
-
8
]
,
T
SMC
0
.
2
5
m
icr
o
n
f
ab
r
icatio
n
tech
n
o
lo
g
y
i
s
co
n
s
id
er
ed
,
w
h
er
e:
V
DD
=
2
.
5
V,
V
tn
=
0
.
3
6
5
5
V,
V
tp
=
0
.
5
4
6
6
V,
=
2
4
3
.
6
A
/V
2
,
a
n
d
=
5
1
.
6
A/V
2
.
T
ab
le
1
.
C
o
n
s
tr
ain
t
s
v
al
u
es
f
o
r
th
e
8
d
esig
n
s
ets [
3
-
8]
D
e
si
g
n
se
t
n
o
.
C
L
(
p
F
)
(
W/
L
)
n
(
W/
L
)
p
t
f
(
n
s)
t
r
(
n
s)
t
pH
L
(
n
s)
t
pL
H
(
n
s)
1
0
.
2
–
4
1
.
1
–
6
.
1
2
.
8
–
1
9
.
3
1
.
1
–
13
1
.
1
–
13
0
.
5
–
10
0
.
5
–
10
2
0
.
1
–
5
.
1
1
.
6
–
7
.
1
1
.
8
–
18
1
.
1
–
15
1
.
1
–
15
0
.
5
–
8
0
.
5
–
8
3
0
.
4
7
–
2
1
.
4
–
6
.
7
3
.
2
–
38
0
.
5
–
12
0
.
5
–
12
0
.
2
–
9
0
.
2
–
9
4
0
.
1
-
1
.
1
1
.
2
–
7
1
.
5
-
1
7
.
5
0
.
5
–
5
0
.
5
–
5
0
.
2
–
4
0
.
2
–
4
5
0
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2
–
14
1
.
9
–
5
.
0
2
.
7
–
17
0
.
7
–
6
0
.
7
–
6
0
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4
–
5
0
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4
–
5
6
0
.
3
–
3
.
6
1
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3
–
3
.
5
3
.
5
–
1
6
.
2
0
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2
5
–
7
0
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2
5
–
7
0
.
3
–
4
.
5
0
.
3
–
4
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5
7
0
.
2
–
4
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9
1
.
1
–
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8
2
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2
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2
5
.
3
0
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5
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6
0
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5
–
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6
0
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2
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0
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7
8
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.
3
–
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6
0
.
1
–
4
.
4
0
.
1
–
4
.
4
4.
NUM
E
RICAL
R
E
SU
L
T
S
Fo
r
ea
ch
d
esig
n
s
et
i
n
T
ab
l
e
1
,
SOS
co
d
e
w
ith
p
o
p
u
lat
io
n
s
ize=
1
0
an
d
m
a
x
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
=
500
is
r
u
n
f
o
r
5
0
i
n
d
ep
en
d
en
t
ti
m
es.
E
ac
h
r
u
n
t
o
o
k
ar
o
u
n
d
8
s
ec
o
n
d
s
o
n
an
HP
Pro
B
o
o
k
6
5
5
0
b
L
ap
to
p
w
it
h
“
I
n
tel
C
o
r
e
i7
C
P
U
M
6
2
0
@
2
.
6
7
GHz
”
an
d
“
4
GB
R
A
M”
.
Ta
b
le
2
s
h
o
w
s
t
h
e
b
est
r
es
u
lt
s
o
b
tain
ed
u
s
i
n
g
SO
S
f
o
r
ea
ch
d
esig
n
s
et,
w
h
er
e
it
ca
n
b
e
s
ee
n
th
at
a
s
y
m
m
etr
ic
o
u
tp
u
t
wav
ef
o
r
m
i
s
o
b
tain
ed
w
it
h
eq
u
a
l f
all
a
n
d
r
is
e
ti
m
es a
n
d
al
m
o
s
t e
q
u
al
p
r
o
p
ag
atio
n
d
ela
y
s
.
I
n
T
ab
le
3
,
SOS r
esu
lt
s
ar
e
co
m
p
ar
ed
w
i
t
h
th
e
b
est
-
k
n
o
w
n
r
es
u
lt
s
f
o
u
n
d
in
t
h
e
li
ter
atu
r
e.
Na
m
el
y
,
SO
S
r
esu
lts
ar
e
co
m
p
ar
ed
w
ith
t
h
o
s
e
o
b
tain
ed
u
s
in
g
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
w
i
th
co
n
s
tr
ictio
n
f
ac
to
r
an
d
in
er
tia
w
ei
g
h
t
a
p
p
r
o
ac
h
(
P
SOC
FIW
A
)
[
3
]
,
d
if
f
er
e
n
tial
ev
o
l
u
tio
n
(
DE
)
[
4
]
,
cr
az
in
ess
b
ased
p
ar
ticl
e
s
war
m
o
p
ti
m
izatio
n
(
C
R
P
SO)
[
5
]
,
h
y
b
r
id
h
ar
m
o
n
y
s
ea
r
ch
w
i
th
d
i
f
f
er
e
n
tial
ev
o
l
u
tio
n
(
HS
-
DE
)
[
6
]
,
an
d
p
ar
t
icle
s
w
ar
m
o
p
ti
m
izatio
n
w
i
t
h
a
g
i
n
g
lead
er
an
d
ch
alle
n
g
er
(
AL
C
-
P
SO)
[
7
]
.
I
t sh
o
u
ld
b
e
m
en
t
io
n
ed
th
at
w
e
t
o
o
k
th
e
v
al
u
e
s
o
f
t
h
e
in
p
u
t
p
ar
a
m
eter
s
(
C
L
,
,
)
f
r
o
m
t
h
e
tab
les
in
[
3
-
7
]
an
d
co
m
p
u
ted
th
e
co
r
r
esp
o
n
d
in
g
C
F
v
al
u
es
o
u
r
s
el
v
es,
as
it
is
b
eliev
ed
th
at
th
e
r
ep
o
r
ted
C
F
v
al
u
es
in
[
3
-
7
]
ar
e
n
o
t
r
ig
h
t.
E
v
e
n
t
h
o
u
g
h
S
OS
d
o
es
n
o
t
g
et
t
h
e
least
er
r
o
r
in
all
d
esi
g
n
s
et
s
,
it
d
o
es
co
m
p
ete
v
er
y
w
ell
w
it
h
o
t
h
er
w
ell
-
d
ev
elo
p
ed
m
e
th
o
d
s
.
I
t
s
h
o
u
ld
b
e
al
s
o
m
en
t
io
n
e
d
th
at,
u
n
lik
e
o
th
er
m
et
h
o
d
s
,
SOS
is
f
r
ee
o
f
tu
n
i
n
g
p
ar
am
e
ter
s
;
o
n
e
h
as
to
s
et
th
e
v
alu
es
o
f
t
h
e
p
o
p
u
latio
n
s
ize
an
d
th
e
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
o
n
l
y
.
Mo
r
eo
v
er
,
th
e
b
asic
SOS
alg
o
r
ith
m
is
u
s
ed
h
er
e
w
it
h
o
u
t
an
y
m
o
d
i
f
icatio
n
s
,
in
co
n
tr
ar
y
to
[
3
,
5
,
7
]
w
h
er
e
P
SO
w
a
s
m
o
d
i
f
ied
to
o
v
er
co
m
e
s
o
m
e
o
f
i
ts
i
n
h
er
en
t
d
r
a
w
b
ac
k
s
.
T
ab
le
4
s
h
o
ws
s
o
m
e
s
tatis
t
ical
m
ea
s
u
r
es
o
f
t
h
e
SO
S
r
esu
lts
.
I
t
ca
n
b
e
cle
ar
l
y
s
ee
n
t
h
at
t
h
e
SOS
is
a
r
o
b
u
s
t
o
p
ti
m
izat
io
n
m
et
h
o
d
as
it
o
b
tain
s
a
v
er
y
s
m
al
l
v
al
u
e
f
o
r
th
e
s
tan
d
ar
d
d
ev
iatio
n
(
co
m
p
ar
ed
,
f
o
r
ex
am
p
le,
w
i
th
t
h
e
o
n
e
r
ep
o
r
ted
in
[
5
]
u
s
in
g
C
R
P
SO)
.
Fig
u
r
e
3
s
h
o
w
s
t
h
e
co
n
v
er
g
en
ce
c
u
r
v
e
f
o
r
th
e
d
e
s
ig
n
s
et
n
u
m
b
er
8
w
h
ic
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
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0
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1
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176
o
b
tain
s
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e
f
o
r
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Ver
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g
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co
n
v
er
g
e
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ce
is
r
e
ac
h
ed
w
it
h
i
n
o
n
l
y
1
0
0
iter
atio
n
s
.
Fig
u
r
e
4
s
h
o
w
s
box
-
an
d
-
w
h
is
k
er
p
lo
t
f
o
r
th
e
s
a
m
e
d
esig
n
s
et.
I
n
t
h
is
f
i
g
u
r
e,
m
ed
ian
(
m
id
d
le
o
f
d
ataset)
is
r
ep
r
esen
ted
b
y
th
e
r
ed
li
n
e,
i.e
.
,
5
0
%
o
f
th
e
d
ata
ar
e
lar
g
er
th
a
n
th
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s
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al
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e
.
Up
p
er
an
d
lo
w
er
en
d
s
o
f
th
e
b
o
x
r
ep
r
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t
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e
7
5
th
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d
2
5
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h
p
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n
ti
les,
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.
,
2
5
%
o
f
th
e
d
ata
ar
e
lar
g
er
th
a
n
t
h
e
u
p
p
er
en
d
,
w
h
ile
2
5
%
o
f
t
h
e
d
ata
ar
e
less
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a
n
th
e
lo
w
er
en
d
.
T
h
e
d
if
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er
en
ce
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et
w
ee
n
th
e
u
p
p
er
an
d
lo
w
er
e
n
d
s
o
f
t
h
e
b
o
x
(
i.e
.
,
th
e
le
n
g
th
o
f
th
e
b
o
x
)
is
k
n
o
w
n
as
th
e
“
i
n
ter
q
u
ar
tile
r
an
g
e
(
I
Q
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”.
T
h
e
w
h
i
s
k
er
s
ar
e
l
in
e
s
e
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n
d
in
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d
s
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o
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e
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ata.
T
h
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en
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o
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th
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p
p
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w
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i
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n
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icate
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t
h
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lar
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e
s
t
v
al
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e
i
n
th
e
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ata
s
et
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e
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d
i
n
g
u
p
p
er
o
u
tlier
s
)
,
w
h
ile
th
e
e
n
d
o
f
th
e
lo
w
er
w
h
is
k
er
i
n
d
icate
s
th
e
s
m
alles
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al
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e
i
n
th
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d
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g
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er
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u
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.
Ou
tlier
s
ar
e
d
ata
w
it
h
v
alu
e
s
b
e
y
o
n
d
th
e
e
n
d
s
o
f
th
e
w
h
is
k
er
s
,
an
d
ar
e
i
n
d
icate
d
w
it
h
r
ed
“
+”
s
i
g
n
.
An
o
u
tl
ier
is
an
y
v
al
u
e
t
h
at
lie
s
m
o
r
e
t
h
an
o
n
e
a
n
d
a
h
alf
t
i
m
e
s
t
h
e
len
g
t
h
o
f
th
e
b
o
x
f
r
o
m
eit
h
er
en
d
o
f
t
h
e
b
o
x
.
T
ab
le
2
.
SOS r
esu
lts
D
e
si
g
n
se
t
n
o
.
C
L
(
p
F
)
(
W/
L
)
n
(
W/
L
)
p
t
f
(
n
s)
t
r
(
n
s)
t
pH
L
(
n
s)
t
pL
H
(
n
s)
CF
(
p
s)
1
0
.
7
4
2
8
3
.
4
9
7
8
3
1
8
.
6
7
5
5
6
1
.
1
7
8
5
1
.
1
7
8
5
0
.
5
0
.
5
1
8
3
5
1
8
.
3
5
2
0
.
7
1
5
9
3
3
.
3
7
1
3
18
1
.
1
7
8
5
1
.
1
7
8
5
0
.
5
0
.
5
1
8
3
5
1
8
.
3
5
3
0
.
6
0
3
6
6
6
.
7
3
5
.
7
7
2
5
5
0
.
5
0
.
5
0
.
2
1
2
1
3
0
.
2
1
9
9
2
7
.
7
9
4
0
.
1
0
8
1
2
1
.
2
6
.
4
0
7
0
2
0
.
5
0
.
5
0
.
2
1
2
1
3
0
.
2
1
9
9
3
7
.
7
9
5
0
.
3
2
2
7
9
1
.
9
1
0
.
1
4
4
4
5
0
.
9
4
2
8
0
.
9
4
2
8
0
.
4
0
.
4
1
4
6
9
1
4
.
6
8
6
0
.
3
8
6
6
3
.
0
3
4
1
7
1
6
.
2
0
.
7
0
7
1
0
.
7
0
7
1
0
.
3
0
.
3
1
1
0
1
1
1
.
0
1
7
0
.
4
2
6
8
7
4
.
7
3
7
8
2
2
5
.
2
9
6
1
1
0
.
5
0
.
5
0
.
2
1
2
1
3
0
.
2
1
9
9
2
7
.
7
9
8
0
.
3
9
4
8
7
7
.
3
0
4
4
8
39
0
.
3
0
.
3
0
.
1
2
7
2
8
0
.
1
3
1
9
5
4
.
6
7
T
ab
le
3
.
C
o
m
p
ar
is
o
n
o
f
SOS
-
o
b
tain
ed
CF
v
al
u
es (
in
p
s
)
w
it
h
o
th
er
m
e
th
o
d
s
i
n
th
e
li
ter
atu
r
e.
T
h
e
least
CF
v
al
u
e
is
i
n
b
o
ld
f
ac
e
D
e
si
g
n
se
t
n
o
.
S
O
S
C
R
P
S
O
[
5
]
R
G
A
[
5
]
HS
-
DE
[
6
]
PSO
-
C
F
I
W
A
[
3
]
D
E
[
4
]
A
L
C
-
PSO
[
7
]
1
1
8
.
3
5
3
1
.
8
4
6
5
.
7
7
1
7
.
7
4
1
8
.
2
6
3
7
.
2
6
1
7
.
8
3
2
1
8
.
3
5
3
5
.
0
6
7
5
.
0
9
1
7
.
9
2
1
8
.
1
7
3
9
.
5
1
7
.
8
5
3
7
.
7
9
1
5
.
3
7
7
7
.
5
4
7
.
8
1
7
.
7
9
1
6
.
0
1
7
.
8
4
7
.
7
9
1
5
.
1
2
3
6
.
8
3
7
.
7
9
7
.
7
9
1
2
.
8
1
1
3
.
6
8
5
1
4
.
6
8
3
0
.
0
4
6
9
.
2
8
1
4
.
1
9
1
4
.
4
6
4
2
.
1
3
1
4
.
1
9
6
1
1
.
0
1
2
0
.
5
9
7
0
.
9
2
2
2
.
9
5
1
1
.
5
3
2
3
.
5
8
1
0
.
9
6
7
7
.
7
9
8
.
8
4
6
9
.
2
5
7
.
8
1
7
.
9
1
5
.
9
7
7
.
7
9
8
4
.
6
7
7
.
9
1
6
8
.
7
8
4
.
6
7
4
.
6
7
9
.
3
5
4
.
6
7
T
ab
le
4
.
Statis
tics
o
f
SO
S C
F v
alu
e
s
o
u
t o
f
5
0
r
u
n
s
f
o
r
ea
ch
s
et
Set
1
Set
2
Set
3
Set
4
Set
5
Set
6
Set
7
Set
8
Set
8
(CRPSO
)
[
5
]
Mi
n
i
m
u
m
(p
s
)
1
8
.
3
5
3
8
1
8
.
3
5
3
8
7
.
7
8
6
9
5
7
.
7
8
6
9
5
1
4
.
6
8
3
1
1
.
0
1
2
2
7
7
.
7
8
6
9
5
4
.
6
7
2
1
7
5
.
2
5
9
4
Max
i
m
u
m
(
p
s
)
1
8
.
5
3
7
1
8
.
8
6
2
8
7
.
8
4
7
7
.
8
8
8
3
3
1
4
.
8
5
4
8
1
1
.
0
4
9
8
3
7
.
9
7
5
8
6
4
.
7
6
0
5
6
1
5
.
3
4
3
2
Mea
n
(
p
s
)
1
8
.
3
8
5
3
1
8
.
3
8
1
5
7
.
7
9
4
3
7
.
7
9
8
3
1
4
.
6
9
5
8
1
1
.
0
1
9
0
3
7
.
8
1
2
0
8
4
.
6
8
1
9
6
1
0
.
0
5
7
2
St
a
n
d
ar
d
d
ev
i
at
i
o
n
(
x
1
0
-
14
)
(
s
)
4
.
1
5
4
8
7
.
9
9
7
7
1
.
3
1
7
4
1
.
9
5
9
5
2
.
6
6
4
1
3
0
.
8
9
9
5
2
3
.
6
8
4
1
.
5
3
9
4
6
1
0
8
.
1
5
T
h
e
0
.
2
5
m
icr
o
n
C
M
OS
i
n
v
e
r
ter
cir
cu
it
i
s
s
i
m
u
lated
u
s
i
n
g
th
e
n
e
w
v
er
s
io
n
o
f
P
SP
I
C
E
(
V
1
7
.
7
)
.
T
h
e
r
esu
lts
o
f
eig
h
t
d
if
f
er
e
n
t
d
esig
n
s
et
ar
e
o
b
tain
ed
an
d
s
h
o
w
n
i
n
T
ab
le
5
,
co
n
s
id
er
i
n
g
t
h
e
s
a
m
e
SO
S
o
p
tim
ized
v
alu
e
s
as
s
h
o
w
n
i
n
T
ab
le
2
o
f
th
e
i
m
p
o
r
tan
t
p
ar
am
eter
s
,
o
u
tp
u
t
lo
ad
ca
p
ac
ito
r
an
d
th
e
g
eo
m
etr
y
o
f
th
e
tr
an
s
is
to
r
,
as
in
p
u
ts
f
o
r
P
SP
I
C
E
s
i
m
u
latio
n
.
Fi
g
u
r
es
5
an
d
6
s
h
o
w
s
th
e
t
i
m
in
g
d
iag
r
a
m
s
f
o
r
th
e
r
i
s
e
an
d
f
all
ti
m
es
a
n
d
th
e
p
r
o
p
ag
atio
n
d
elay
ti
m
es,
r
esp
ec
ti
v
el
y
,
f
o
r
d
esig
n
s
et
n
u
m
b
er
8
u
s
i
n
g
P
SP
I
C
E
s
i
m
u
latio
n
.
A
r
ea
s
o
n
ab
le
s
i
m
ilar
it
y
b
et
w
e
en
t
h
e
SO
S r
esu
lts
i
n
T
ab
le
2
an
d
P
SP
I
C
E
r
esu
lts
in
T
ab
le
5
f
o
r
th
e
r
i
s
e
an
d
f
all
ti
m
e
s
ca
n
b
e
o
b
s
er
v
ed
.
Ho
w
e
v
er
,
lar
g
er
g
r
o
u
p
d
ela
y
s
ar
e
p
r
ed
icted
u
s
in
g
P
SP
I
C
E
.
T
h
is
is
d
u
e
to
th
e
f
ac
t
t
h
at
m
o
r
e
co
m
p
lex
e
x
p
r
ess
io
n
s
ar
e
u
s
ed
i
n
P
SP
I
C
E
to
ev
alu
a
te
t
h
e
r
is
e
a
n
d
f
al
l
ti
m
es
a
n
d
th
e
p
r
o
p
ag
atio
n
d
ela
y
s
,
w
h
ile
s
i
m
p
le
ap
p
r
o
x
i
m
a
te
e
x
p
r
ess
io
n
s
ar
e
u
s
ed
h
er
e
as
s
u
m
i
n
g
lo
n
g
c
h
an
n
el
tr
an
s
is
to
r
s
an
d
an
id
ea
l
s
tep
i
n
p
u
t
v
o
ltag
e.
Si
m
i
lar
o
b
s
er
v
atio
n
h
as
b
ee
n
n
o
ted
in
[
3
-
7
]
.
C
lea
r
l
y
,
th
ese
ap
p
r
o
x
i
m
ate
ex
p
r
es
s
io
n
s
u
n
d
er
est
i
m
a
te
th
e
p
r
o
p
ag
atio
n
d
ela
y
s
,
a
n
d
th
u
s
,
w
e
ar
e
n
o
w
e
x
p
lo
r
in
g
t
h
e
u
s
e
o
f
m
o
r
e
co
m
p
lex
ex
p
r
ess
io
n
s
,
li
k
e
th
e
i
m
p
r
o
v
ed
o
n
e
s
v
er
y
r
ec
e
n
t
l
y
p
r
o
p
o
s
ed
in
[
2
5
].
I
t is
w
o
r
t
h
m
e
n
tio
n
in
g
t
h
at
P
SP
I
C
E
r
esu
lts
ar
e
o
b
tain
ed
u
s
i
n
g
V1
7
.
7
w
it
h
th
e
f
o
llo
w
in
g
s
e
t
tin
g
s
:
a.
Si
m
u
latio
n
p
r
o
f
ile:
An
al
y
s
i
s
t
y
p
e
=
T
r
an
s
ien
t
T
S
T
OP
:
8
s
ec
o
n
d
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Op
tima
l d
esig
n
o
f symme
tr
ic
s
w
itch
in
g
C
MOS
in
ve
r
ter u
s
in
g
s
ymb
io
tic
o
r
g
a
n
is
ms s
ea
r
ch
…
(
N
ih
a
d
Dib
)
177
Star
t sav
i
n
g
d
ata
af
ter
: 0
s
ec
o
n
d
s
Ma
x
i
m
u
m
s
tep
s
ize:
1
m
s
SKI
P
B
P
: t
r
u
e
A
ll
o
t
h
er
s
i
m
u
latio
n
p
ar
a
m
ete
r
s
ar
e
lef
t
to
t
h
eir
d
ef
a
u
lt
v
alu
es
(
R
E
L
T
OL
,
VNT
OL
,
A
B
ST
OL
,
C
HGT
OL
,
GM
I
N,
I
T
L
1
,
I
T
L
2
,
I
T
L
4
,
T
N
OM
,
T
HR
E
A
DS,
Sp
e
ed
lev
el,
SKI
P
B
P
)
.
b
.
C
MO
S
tec
h
n
o
lo
g
y
u
s
ed
:
Mb
r
ea
k
P
an
d
Mb
r
ea
k
N
to
s
i
m
u
late
t
h
e
0
.
2
5
μ
m
T
SMC
f
a
b
r
icatio
n
tech
n
o
lo
g
y
w
it
h
t
h
e
p
ar
a
m
eter
s
m
e
n
tio
n
e
d
in
th
e
p
ap
er
(
μ
n
C
ox
, V
tn
, V
tp
,
(
W
/L
)
n
,
(
W
/L
)
p
).
c.
I
n
p
u
t so
u
r
ce
: (
VP
UL
SE)
s
o
u
r
ce
w
ith
t
h
e
f
o
llo
w
in
g
ch
ar
ac
ter
is
tics
:
V1
=
0
; V
2
=
2
.
5
=
VDD;
T
D
=
0
;
T
R
=
1
n
; T
F =
1
n
; P
W
=
1
; P
E
R
=
2
Fig
u
r
e
3
.
C
o
n
v
er
g
en
ce
c
u
r
v
e
f
o
r
th
e
SOS r
esu
lts
f
o
r
th
e
d
esi
g
n
s
et
n
u
m
b
er
8
Fig
u
r
e
4
.
B
o
x
-
an
d
-
w
h
is
k
er
p
lo
t f
o
r
th
e
SOS r
es
u
lt
s
f
o
r
d
esig
n
s
et
n
u
m
b
er
8
T
ab
le
5
.
P
SP
I
C
E
r
esu
lts
u
s
in
g
Vin
“
Hi
g
h
”
=
V
DD
D
e
si
g
n
se
t
n
o
.
C
L
(
p
F
)
(
W/
L
)
n
(
W/
L
)
p
t
f
(
n
s)
t
r
(
n
s)
t
pH
L
(
n
s)
t
pL
H
(
n
s)
CF
(
n
s)
1
0
.
7
4
2
8
3
.
4
9
7
8
3
1
8
.
6
7
5
5
6
1
.
3
5
5
2
4
0
.
9
6
9
1
6
7
8
6
1
.
1
9
6
6
1
.
1
1
6
6
0
.
4
6
6
0
7
2
0
.
7
1
5
9
3
3
.
3
7
1
3
18
1
.
3
5
5
2
4
0
.
9
6
9
1
6
7
4
1
1
.
0
9
7
1
1
.
1
6
2
7
0
.
4
5
1
6
7
3
0
.
6
0
3
6
6
6
.
7
3
5
.
7
7
2
5
5
0
.
7
6
7
0
3
5
6
9
0
.
6
4
5
7
5
3
8
9
0
.
8
8
7
3
4
8
0
.
6
0
2
4
6
7
0
.
4
0
6
1
6
4
0
.
1
0
8
1
2
1
.
2
6
.
4
0
7
0
2
0
.
7
6
7
0
4
2
2
7
0
.
6
4
5
7
5
7
6
7
0
.
9
1
4
8
3
0
0
.
6
2
7
8
3
2
0
.
4
0
8
2
8
5
0
.
3
2
2
7
9
1
.
9
1
0
.
1
4
4
4
5
1
.
1
5
7
9
0
0
.
8
3
5
1
1
8
2
0
1
.
0
6
4
5
0
.
7
1
5
3
1
6
0
.
6
7
1
9
7
6
0
.
3
8
6
6
3
.
0
3
4
1
7
1
6
.
2
1
.
0
0
2
5
6
0
.
7
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5.
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[1
]
R.
Vu
ra
l,
O.
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.
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ir
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m
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S
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p
.
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.
[2
]
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u
ra
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O.
De
r,
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d
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.
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rim
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“
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., v
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.
[3
]
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,
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r,
D.
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n
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S
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h
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l,
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De
sig
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te
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.
[4
]
B.
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n
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h
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ti
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iff
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n
ti
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o
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El
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[5
]
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.
[6
]
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[
7
]
B.
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M
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in
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,”
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ter
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l
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Cy
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p
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–
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,
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g
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.
[8
]
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r,
D.
M
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In
v
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o
r
S
y
m
m
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tri
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S
w
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Ch
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ra
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teristics
Us
in
g
F
iref
l
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l
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m
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e
let
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u
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,”
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ter
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ti
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o
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rn
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o
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r
m
In
telli
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Res
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(
IJ
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IR)
,
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p
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[9
]
M
.
C
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n
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a
n
d
D.
P
ra
y
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o
,
“
S
y
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o
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n
ism
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:
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e
tah
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risti
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iza
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a
lg
o
rit
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m
,”
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ter
s
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n
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S
tr
u
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tu
re
s
,
v
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1
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p
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0
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.
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,
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u
ra
l
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o
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ti
n
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d
A
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ti
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l.
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1
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p
.
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5
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–
3
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8
5
,
No
v
.
2017.
Fig
u
r
e
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.
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P
SP
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f
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esig
n
s
et
8
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
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g
I
SS
N:
2
0
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8
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Op
tima
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…
(
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d
Dib
)
179
[1
1
]
M
.
Do
so
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lu
,
U.
G
u
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,
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.
Du
m
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n
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Co
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Ap
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v
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3
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pp
.
721
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.
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.
[1
2
]
N.
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,
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De
sig
n
o
f
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Pro
g
.
El
e
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tro
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g
n
.
Res
.
B
, v
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8
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p
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1
6
.
[1
3
]
N.
Dib
,
“
De
sig
n
o
f
P
lan
a
r
Co
n
c
e
n
tri
c
Circu
lar
A
n
ten
n
a
A
rra
y
s
Wi
th
Re
d
u
c
e
d
S
id
e
L
o
b
e
s
L
e
v
e
l
U
sin
g
S
y
m
b
io
ti
c
Org
a
n
is
m
s S
e
a
rc
h
,
”
Ne
u
ra
l
Co
m
p
u
ti
n
g
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
3
0
,
n
o
.
1
2
,
p
p
.
3
8
5
9
-
3
8
6
8
,
De
c
.
2
0
1
8
.
[1
4
]
D.
G
u
h
a
,
P
.
Ro
y
,
a
n
d
S
.
B
a
n
e
rj
e
e
,
“
S
y
m
b
io
ti
c
o
r
g
a
n
is
m
s
e
a
rc
h
a
lg
o
rit
h
m
a
p
p
li
e
d
to
lo
a
d
f
re
q
u
e
n
c
y
c
o
n
tro
l
o
f
m
u
lt
i
-
a
re
a
p
o
w
e
r
s
y
ste
m
,”
En
e
rg
y
S
y
st
,
v
o
l.
9
,
n
o.
2
,
p
p
.
4
3
9
–
4
6
8
,
M
a
y
2
0
1
8
.
[
1
5
]
G.
T
e
jan
i,
V
.
S
a
v
sa
n
i,
a
n
d
K.
V
i
v
e
k
,
“
A
d
a
p
ti
v
e
s
y
m
b
io
ti
c
o
rg
a
n
ism
s
se
a
rc
h
(S
OS)
a
lg
o
rit
h
m
f
o
r
st
ru
c
tu
ra
l
d
e
sig
n
o
p
ti
m
iza
ti
o
n
,”
J
o
u
rn
a
l
o
f
Co
m
p
u
t
a
ti
o
n
a
l
De
sig
n
a
n
d
E
n
g
in
e
e
rin
g
,
v
o
l.
3
,
p
p
.
2
2
6
–
2
4
9
,
2
0
1
6
.
[1
6
]
S.
Na
m
a
,
A
.
S
a
h
a
,
a
n
d
S
.
G
h
o
sh
,
“
Im
p
ro
v
e
d
s
y
m
b
io
ti
c
o
rg
a
n
ism
s
se
a
r
c
h
a
lg
o
rit
h
m
f
o
r
so
lv
in
g
u
n
c
o
n
stra
in
e
d
f
u
n
c
ti
o
n
o
p
t
im
iza
ti
o
n
,”
De
c
isio
n
S
c
ien
c
e
L
e
tt
e
rs
, v
o
l.
5
,
p
p
.
3
6
1
–
3
8
0
,
2
0
1
6
.
[1
7
]
S.
Ba
n
e
rjee
,
a
n
d
S
.
C
h
a
tt
o
p
a
d
h
y
a
y
,
“
P
o
w
e
r
Op
ti
m
iza
ti
o
n
o
f
Th
re
e
Dim
e
n
sio
n
a
l
T
u
rb
o
C
o
d
e
Us
in
g
a
No
v
e
l
M
o
d
if
ied
S
y
m
b
io
ti
c
Org
a
n
is
m
S
e
a
rc
h
(M
S
OS)
A
lg
o
rit
h
m
,”
W
ire
le
ss
Per
s Co
mm
u
n
,
v
o
l
.
9
2
,
p
p
.
9
4
1
–
9
6
8
,
2
0
1
7
.
[1
8
]
N.
Dib
a
n
d
B.
El
-
As
ir,
“
Op
ti
m
a
l
De
sig
n
o
f
A
n
a
lo
g
A
c
ti
v
e
F
il
ters
Us
in
g
S
y
m
b
io
ti
c
Org
a
n
is
m
s
S
e
a
rc
h
,
”
In
tern
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Nu
me
ric
a
l
M
o
d
e
ll
i
n
g
:
El
e
c
tro
n
ic Ne
two
rk
s,
De
v
ice
s a
n
d
Fi
e
ld
s
,
v
o
l
.
3
1
,
n
o
.
5
,
p
p
.
1
-
1
5
,
S
e
p
.
2
0
1
8
.
[1
9
]
N.
Dib
,
A
.
Am
a
ireh
,
a
n
d
A
.
Al
-
Zo
u
b
i,
“
On
th
e
Op
ti
m
a
l
S
y
n
th
e
sis
o
f
El
li
p
ti
c
a
l
A
n
ten
n
a
A
rra
y
s,”
In
t.
J
.
o
f
El
e
c
tro
n
ics
,
v
o
l.
1
0
6
,
n
o
.
1
,
p
p
.
1
2
1
-
1
3
3
,
Ja
n
.
2
0
1
9
.
[2
0
]
G
.
T
e
jan
i,
N.
P
h
o
ld
e
e
,
S
.
Bu
re
e
ra
t,
D.
P
ra
y
o
g
o
,
a
n
d
A
.
Ga
n
d
o
m
i
,
“
S
tru
c
tu
ra
l
o
p
ti
m
iza
ti
o
n
u
sin
g
m
u
lt
i
-
o
b
jec
ti
v
e
m
o
d
if
ied
a
d
a
p
ti
v
e
s
y
m
b
io
ti
c
o
rg
a
n
ism
s se
a
rc
h
,
”
Exp
e
rt S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
, v
o
l.
1
2
5
,
p
p
.
4
2
5
-
4
4
1
,
2
0
1
9
.
[2
1
]
D.
P
ra
y
o
g
o
,
a
n
d
C.
Ku
su
m
a
,
“
Op
ti
m
i
z
a
ti
o
n
o
f
re
so
u
rc
e
l
e
v
e
li
n
g
p
ro
b
lem
u
n
d
e
r
m
u
lt
ip
le
o
b
jec
ti
v
e
c
rit
e
ria
u
sin
g
a
s
y
m
b
io
ti
c
o
rg
a
n
ism
s se
a
rc
h
,
”
Civil
En
g
in
e
e
rin
g
Dime
n
si
on
,
v
o
l.
2
1
,
p
p
.
4
3
-
4
9
,
2
0
1
9
.
[2
2
]
G
.
T
e
jan
i,
N.
P
h
o
ld
e
e
,
S
.
B
u
re
e
ra
t,
a
n
d
D.
P
ra
y
o
g
o
,
“
M
u
lt
i
o
b
jec
ti
v
e
a
d
a
p
ti
v
e
s
y
m
b
io
ti
c
o
rg
a
n
ism
s
se
a
rc
h
f
o
r
tru
ss
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s,”
Kn
o
wled
g
e
-
b
a
se
d
sy
ste
ms
,
v
o
l.
1
6
1
,
p
p
.
3
9
8
-
4
1
4
,
2
0
1
8
.
[2
3
]
D.
P
ra
y
o
g
o
,
M
.
Ch
e
n
g
,
F
.
W
o
n
g
,
D.
T
jan
d
ra
,
a
n
d
Du
c
-
Ho
c
T
ra
n
,
“
Op
ti
m
iza
ti
o
n
m
o
d
e
l
f
o
r
c
o
n
stru
c
ti
o
n
p
ro
jec
t
re
so
u
rc
e
lev
e
li
n
g
u
sin
g
a
n
o
v
e
l
m
o
d
if
ied
s
y
m
b
io
ti
c
o
rg
a
n
ism
s se
a
r
c
h
,
”
Asia
n
J
o
u
rn
a
l
o
f
Civil
En
g
in
e
e
rin
g
,
v
o
l.
1
9
,
pp.
6
2
5
-
6
3
8
,
2
0
1
8
.
[2
4
]
D.
P
ra
y
o
g
o
,
M
in
-
Yu
a
n
C
h
e
n
g
,
a
n
d
H.
P
ra
y
o
g
o
,
“
A
n
o
v
e
l
im
p
le
m
e
n
tatio
n
o
f
n
a
tu
re
-
i
n
sp
ired
o
p
ti
m
iza
ti
o
n
f
o
r
c
iv
il
e
n
g
in
e
e
rin
g
:
a
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
s
y
m
b
io
ti
c
o
rg
a
n
is
m
s
se
a
r
c
h
,
”
Civil
En
g
in
e
e
rin
g
Dime
n
sio
n
,
v
o
l.
1
9
,
n
o
.
1
,
p
p
.
3
6
-
4
1
,
2
0
1
7
.
[
25
]
E.
Zaf
a
rk
h
a
h
,
M
.
M
a
y
m
a
n
d
i
-
Ne
jad
,
a
n
d
M
.
Zare
,
“
Im
p
ro
v
e
d
a
c
c
u
ra
c
y
e
q
u
a
ti
o
n
f
o
r
p
ro
p
a
g
a
ti
o
n
d
e
l
a
y
o
f
a
CM
OS
in
v
e
rter
in
a
sin
g
le
e
n
d
e
d
rin
g
o
sc
il
lato
r,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
Co
mm
u
n
i
c
a
ti
o
n
s
(
AE
Ü)
,
v
o
l.
7
1
,
p
p
.
1
1
0
–
1
1
7
,
Ja
n
.
2
0
1
7
.
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