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I
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I
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J
E
lec
&
C
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m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
1
2
9
-
138
130
ap
p
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[
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11
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12
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1
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tif
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A
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[
15
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I
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i
m
u
latio
n
s
ar
e
g
i
v
e
n
to
s
h
o
w
t
h
e
v
alid
it
y
o
f
o
b
tain
ed
r
esu
lts
.
T
h
e
r
est
o
f
th
e
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
T
h
e
s
ec
o
n
d
p
ar
t
g
iv
es
an
o
v
er
v
ie
w
o
n
t
h
e
p
r
i
n
cip
l
e
o
f
t
h
e
g
e
n
etic
al
g
o
r
ith
m
.
T
h
e
th
ir
d
p
ar
t
d
ea
ls
w
it
h
th
e
ap
p
licatio
n
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
to
t
h
e
o
p
tim
a
l
d
esi
g
n
o
f
a
th
r
ee
s
ta
g
es
b
ip
o
lar
tr
an
s
i
s
to
r
a
m
p
li
f
i
er
.
T
h
e
f
o
u
r
t
h
p
ar
t
s
h
o
w
s
t
h
e
r
es
u
lt
s
o
f
t
h
e
o
p
ti
m
al
s
izi
n
g
.
Fi
n
all
y
,
t
h
e
f
i
f
t
h
s
ec
tio
n
,
f
o
llo
w
ed
b
y
a
co
n
cl
u
s
io
n
,
p
r
esen
t
s
h
o
w
to
s
et
SP
I
C
E
p
ar
am
e
ter
s
an
d
s
h
o
w
s
th
e
s
i
m
u
lat
io
n
r
es
u
lts
.
2.
G
E
NE
T
I
C
A
L
G
O
RI
T
H
M
T
h
e
GA
f
i
n
d
th
eir
o
r
ig
in
s
i
n
th
e
b
io
lo
g
ical
p
r
o
ce
s
s
es
o
f
s
u
r
v
i
v
al
an
d
ad
ap
tatio
n
.
I
ts
p
r
in
cip
le
co
n
s
is
ts
o
f
s
a
m
p
lin
g
a
p
o
p
u
latio
n
o
f
p
o
ten
tial
s
o
lu
tio
n
s
.
A
p
o
p
u
latio
n
o
f
in
d
iv
id
u
als
i
s
,
in
itiall
y
,
r
an
d
o
m
l
y
g
en
er
ated
.
T
h
e
G
A
p
er
f
o
r
m
s
t
h
en
o
p
er
atio
n
s
o
f
s
e
lecti
o
n
,
cr
o
s
s
o
v
er
a
n
d
m
u
tatio
n
o
n
t
h
e
i
n
d
iv
id
u
als,
co
r
r
esp
o
n
d
in
g
r
esp
ec
tiv
el
y
to
th
e
p
r
in
cip
al
o
f
s
u
r
v
iv
a
l
o
f
th
e
f
it
test
,
r
ec
o
m
b
in
at
io
n
o
f
g
en
etic
m
a
ter
ial
an
d
r
an
d
o
m
m
u
tatio
n
o
b
s
er
v
ed
in
n
atu
r
e
[
1
8
]
.
T
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
is
ca
r
r
ied
o
u
t
th
r
o
u
g
h
t
h
e
g
e
n
er
atio
n
o
f
s
u
cc
e
s
s
i
v
e
p
o
p
u
latio
n
s
u
n
ti
l
a
s
to
p
cr
iter
io
n
is
m
et.
T
h
e
f
lo
w
c
h
ar
t
i
n
Fi
g
u
r
e
1
p
r
o
v
id
es
an
o
v
er
v
ie
w
o
f
a
G
A
p
r
o
ce
d
u
r
e
[
1
8
]
.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
a
G
A
T
h
er
e
ar
e
th
er
ef
o
r
e
6
elem
en
ts
n
ec
ess
ar
y
f
o
r
th
e
r
u
n
n
i
n
g
o
f
t
h
e
G
A
[
1
8
]
:
1.
W
e
b
eg
in
th
e
p
r
o
ce
s
s
o
f
f
itti
n
g
t
h
e
p
r
o
b
le
m
to
a
G
A
b
y
d
ef
i
n
in
g
a
c
h
r
o
m
o
s
o
m
e
a
s
an
ar
r
ay
o
f
v
ar
iab
le
v
alu
e
s
to
b
e
o
p
tim
ized
.
2.
T
h
e
u
s
er
m
u
s
t
f
i
x
a
p
r
io
r
i
t
h
e
s
izi
n
g
p
ar
a
m
eter
s
o
f
t
h
e
al
g
o
r
ith
m
,
i
n
p
ar
ticu
lar
t
h
e
s
ize
o
f
th
e
p
o
p
u
latio
n
an
d
th
e
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
(
w
h
ich
i
s
v
er
y
o
f
te
n
u
s
ed
as a
co
n
d
itio
n
f
o
r
s
to
p
p
in
g
th
e
al
g
o
r
ith
m
)
.
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
A
g
en
etic
a
lg
o
r
ith
m
fo
r
th
e
o
p
tima
l d
esig
n
o
f a
mu
ltis
ta
g
e
a
mp
lifi
er
(
E
l B
eq
a
l A
s
ma
e
)
131
3.
T
h
en
th
e
Ge
n
er
atio
n
o
f
th
e
i
n
itial
p
o
p
u
latio
n
(
s
et
o
f
p
o
s
s
i
b
le
s
o
lu
tio
n
s
)
ca
n
b
e
r
an
d
o
m
o
r
f
r
o
m
k
n
o
wn
ap
p
r
o
x
im
a
te
s
o
lu
t
io
n
(
s
)
.
4.
E
ac
h
ch
r
o
m
o
s
o
m
e
h
as
a
co
s
t
f
o
u
n
d
b
y
e
v
al
u
atin
g
t
h
e
co
s
t
f
u
n
ctio
n
f
at
t
h
e
v
ar
iab
les.
T
h
e
h
ig
h
er
t
h
i
s
co
s
t,
th
e
g
r
ea
ter
is
t
h
e
c
h
an
ce
o
f
a
n
in
d
iv
id
u
al
(
s
o
lu
tio
n
)
b
ein
g
s
el
ec
ted
f
o
r
r
ep
r
o
d
u
ctio
n
.
5.
No
w
is
t
h
e
ti
m
e
to
d
ec
id
e
w
h
ich
c
h
r
o
m
o
s
o
m
es
i
n
t
h
e
in
it
ial
p
o
p
u
latio
n
ar
e
f
it
e
n
o
u
g
h
to
s
u
r
v
i
v
e
an
d
p
o
s
s
ib
l
y
r
ep
r
o
d
u
ce
o
f
f
s
p
r
i
n
g
in
t
h
e
n
e
x
t
g
e
n
er
atio
n
,
th
e
c
o
s
ts
a
n
d
as
s
o
ciate
d
ch
r
o
m
o
s
o
m
es
ar
e
r
an
k
ed
f
r
o
m
lo
w
est co
s
t to
h
ig
h
e
s
t c
o
s
t .
T
h
e
r
est d
ie
o
f
f
.
6.
T
h
en
r
ec
o
m
b
i
n
atio
n
/r
ep
r
o
d
u
ctio
n
is
ac
h
ie
v
ed
th
r
o
u
g
h
t
wo
g
en
etic
o
p
er
ato
r
s
,
n
a
m
el
y
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
.
C
r
o
s
s
o
v
e
r
th
at
c
o
m
b
in
es
(
m
ates
)
tw
o
ch
r
o
m
o
s
o
m
es
(
p
ar
en
ts
)
t
o
p
r
o
d
u
ce
a
n
ew
ch
r
o
m
o
s
o
m
e
(
o
f
f
s
p
r
in
g
)
.
T
h
e
i
d
e
a
b
eh
in
d
cr
o
s
s
o
v
er
is
th
at
th
e
n
ew
ch
r
o
m
o
s
o
m
e
m
ay
b
e
b
et
te
r
th
a
n
b
o
th
o
f
th
e
p
a
r
en
ts
if
it
tak
es
th
e
b
est
c
h
ar
a
cte
r
is
tics
f
r
o
m
ea
ch
o
f
th
e
p
a
r
en
ts
.
Mu
tati
o
n
is
u
s
u
ally
co
n
s
i
d
e
r
e
d
as
an
au
x
il
ia
r
y
o
p
e
r
a
to
r
t
o
e
x
ten
d
th
e
s
ea
r
ch
s
p
ac
e
an
d
ca
u
s
es
r
el
ea
s
e
f
r
o
m
a
lo
ca
l
o
p
tim
u
m
w
h
en
u
s
ed
c
au
ti
o
u
s
ly
w
ith
th
e
s
e
le
cti
o
n
an
d
c
r
o
s
s
o
v
er
s
y
s
tem
s
.
Op
er
atio
n
s
o
f
s
ele
c
tio
n
,
cr
o
s
s
o
v
er
,
a
n
d
m
u
tatio
n
ar
e
r
ep
ea
ted
u
n
ti
l
a
f
a
v
o
r
ab
le
n
u
m
b
er
o
f
in
d
iv
id
u
als
f
o
r
th
e
n
e
w
g
e
n
er
atio
n
is
cr
ea
ted
,
an
d
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
is
ca
lc
u
late
d
ag
ain
f
o
r
all
o
f
th
e
in
d
i
v
id
u
a
ls
i
n
th
e
n
e
w
g
e
n
er
atio
n
.
T
h
e
b
est
in
d
i
v
id
u
al
in
th
e
n
e
w
g
en
er
ati
o
n
ac
co
r
d
in
g
to
it
s
f
i
tn
e
s
s
is
k
ep
t
to
co
n
tin
u
e
to
th
e
n
ex
t
g
en
er
atio
n
.
T
h
u
s
,
th
e
f
it
n
es
s
o
f
th
e
e
n
tire
p
o
p
u
latio
n
w
ill
b
e
d
ec
r
ea
s
ed
w
it
h
th
e
r
ep
r
o
d
u
ctio
n
o
f
th
e
g
e
n
er
atio
n
.
I
n
th
e
liter
at
u
r
e,
th
e
n
u
m
b
er
o
f
ap
p
licatio
n
s
t
u
d
ies
o
f
t
h
e
G
A
tech
n
iq
u
e
is
u
n
co
u
n
tab
le
a
n
d
th
e
f
ield
s
o
f
ap
p
licatio
n
ar
e
v
er
y
d
iv
er
s
e.
T
h
ese
in
cl
u
d
e
f
o
r
ex
a
m
p
le:
P
o
w
er
S
u
p
p
l
y
S
y
s
te
m
[
1
9
]
,
E
lectr
ic
Veh
icle
s
[
2
0
]
,
T
r
af
f
ic
L
ig
h
t
Sig
n
al
P
ar
a
m
eter
s
Op
ti
m
iza
ti
o
n
[
2
1
]
,
Dy
n
a
m
ic
Op
ti
m
izati
o
n
P
r
o
b
lem
s
[
2
2
]
,
R
eso
l
u
tio
n
u
n
iv
er
s
it
y
co
u
r
s
e
s
ch
ed
u
l
es
[
2
3
]
,
P
o
w
er
f
ac
to
r
i
m
p
r
o
v
e
m
e
n
t
i
n
t
h
e
in
d
u
s
tr
y
[
2
4
]
,
etc.
I
n
th
e
f
o
llo
w
in
g
,
w
e
p
r
esen
t a
n
ap
p
licatio
n
o
f
t
h
e
G
A
to
th
e
o
p
tim
a
l d
esig
n
o
f
a
t
h
r
ee
-
s
ta
g
e
a
m
p
lifie
r
.
3.
AP
P
L
I
CA
T
I
O
N:
T
H
RE
E
-
S
T
A
G
E
B
I
P
O
L
AR
T
R
ANSI
S
T
O
R
AM
P
L
I
F
I
E
R
C
I
RCU
I
T
W
e
p
r
o
p
o
s
e
in
t
h
is
s
ec
tio
n
,
th
e
o
p
ti
m
al
s
izin
g
o
f
t
h
r
ee
s
tag
e
b
ip
o
lar
tr
an
s
i
s
to
r
a
m
p
li
f
ier
.
T
h
e
s
ch
e
m
at
ic
o
f
t
h
is
a
m
p
lifie
r
is
g
i
v
en
i
n
Fi
g
u
r
e
2.
Fig
u
r
e
2
.
T
h
e
th
r
ee
-
s
ta
g
e
a
m
p
lif
ier
A
cc
o
r
d
in
g
to
th
e
s
t
u
d
y
o
f
t
h
e
eq
u
i
v
alen
t
cir
c
u
it
o
f
th
i
s
a
m
p
lif
ier
i
n
s
m
all
s
i
g
n
a
ls
in
t
h
e
m
id
b
an
d
w
h
er
e
all
th
e
ca
p
ac
ita
n
ce
s
ar
e
n
eg
lec
ted
,
w
e
h
av
e
o
b
tain
ed
t
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
s
f
o
r
A
V
,
Z
IN
an
d
Z
OUT
:
T
h
e
v
o
lt
ag
e
g
ain
:
1
β
R
h
R
1
β
R
h
h
1
β
β
R
R
1
β
R
A
1
1
th
11
2
th
3
"
11
'
11
1
2
2
th
1
th
3
V
(
1
)
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
:
1
2
9
-
138
132
W
ith
:
4
3
B2
R
R
R
(
2
)
6
5
B3
R
R
R
(
3
)
1
E
I1
R
ρ
R
(
4
)
2
C
'
I2
R
ρ
R
(
5
)
L
3
E
R
R
"
ρ
R
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6
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2
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4
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ab
le
4
.
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er
f
o
r
m
a
n
ce
s
as
s
o
cia
ted
to
th
e
o
p
tim
a
l v
al
u
es
A
V
(
d
B
)
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IN
(
K
Ω
)
Z
O
U
T
(
Ω
)
F
L
(
H
z
)
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H
(
M
H
z
)
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(
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)
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i
n
e
a
r
v
a
l
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e
s
4
4
.
8
5
47
43
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4
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2
4
5
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3
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4
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8
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2
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8
.
8
7
68
68
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1
6
8
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1
6
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5
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5
0
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6
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82
8
6
.
6
8
6
.
6
8
5
.
6
5.
CO
M
P
UT
I
NG
SPI
CE
P
AR
AM
E
T
E
RS A
N
D
SI
M
UL
AT
I
O
N
5
.
1
.
Co
m
pu
t
ing
SPI
CE
pa
ra
m
et
er
s
T
h
e
f
o
llo
w
i
n
g
s
tep
-
by
-
s
tep
p
r
o
ce
d
u
r
e
lead
s
to
th
e
r
e
q
u
ir
ed
s
p
ice
p
ar
am
eter
s
,
i
n
d
icate
d
b
y
b
o
ld
f
ac
e
ch
ar
ac
ter
s
in
t
h
e
eq
u
atio
n
s
[
25
].
a.
C
o
m
p
u
te
th
e
“
tr
a
n
s
p
o
r
t satu
r
a
tio
n
cu
r
r
en
t”
u
s
in
g
:
T
BE
C
V
V
e
x
p
I
IS
(
3
4
)
W
h
er
e
:
q
KT
V
T
b.
T
h
e
id
ea
l “
m
a
x
i
m
u
m
f
o
r
w
ar
d
b
eta”
w
it
h
o
u
t c
o
r
r
ec
tio
n
f
o
r
E
ar
l
y
ef
f
ec
t is
g
i
v
en
b
y
:
β
BF
(
3
5
)
c.
C
o
m
p
u
te
h
11
f
r
o
m
:
C
T
11
I
V
β
h
(
3
6
)
d.
C
o
m
p
u
te
th
e
“f
o
r
w
ar
d
E
ar
l
y
v
o
ltag
e”
u
s
in
g
:
C
I
ρ
VAF
(
3
7
)
W
h
er
e
I
C
,
is
th
e
b
ias cu
r
r
en
t a
t
w
h
ic
h
th
e
h
-
p
ar
a
m
eter
s
w
er
e
m
ea
s
u
r
ed
.
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
:
1
2
9
-
138
136
e.
C
o
m
p
u
te
th
e
v
al
u
e
o
f
th
e
“
ze
r
o
-
b
ias b
ase
r
esis
ta
n
ce
”
u
s
i
n
g
:
x
r
RB
(
3
8
)
f.
Dete
r
m
i
n
i
n
g
C
J
C
:
Fo
r
C
µ,
SP
I
C
E
d
eter
m
i
n
es c
o
l
lecto
r
-
b
ase
ca
p
ac
itan
ce
f
r
o
m
:
M
J
C
V
J
C
C
J
C
CB
V
1
C
μ
(
3
9
)
V
CB
is
t
h
e
Q
-
p
o
in
t
co
llecto
r
-
b
ase
v
o
lta
g
e
t
h
at
SP
I
C
E
w
il
l
d
eter
m
i
n
e
d
u
r
i
n
g
th
e
d
c
an
al
y
s
is
.
W
e
n
ee
d
to
s
p
ec
if
y
MJ
C
,
VJ
C
,
an
d
C
J
C
s
o
t
h
at
w
h
e
n
SP
I
C
E
r
u
n
s
a
s
i
m
u
latio
n
,
t
h
e
r
es
u
lti
n
g
C
µ
w
ill
m
atc
h
th
e
d
esire
d
v
al
u
e.
R
ea
s
o
n
ab
le
v
alu
e
s
f
o
r
MJ
C
an
d
VJ
C
ar
e
MJ
C
=
0
.
5
,
V
J
C
=0
.
7
V.
T
o
f
in
d
C
J
C
th
e
“
b
ase
-
co
llect
o
r
ze
r
o
-
b
ias
d
ep
letio
n
ca
p
ac
itan
ce
”,
th
e
v
al
u
e
o
f
C
µ,
w
ill
b
e
g
iv
e
n
as
w
ell
a
s
th
e
v
o
lta
g
e,
V
CB
,
at
w
h
ich
th
e
m
ea
s
u
r
e
m
en
t
w
as
m
ad
e.
g.
Dete
r
m
i
n
i
n
g
C
J
E
:
Fo
r
C
π
,
SP
I
C
E
d
eter
m
i
n
es
t
h
e
b
ase
-
e
m
itter
j
u
n
ct
io
n
ca
p
ac
it
an
ce
C
je
a
n
d
th
e
d
i
f
f
u
s
io
n
ca
p
ac
itan
ce
C
b
an
d
ad
d
th
ese:
b
je
π
C
C
C
(
4
0
)
TF
C
J
E
11
π
h
β
2
C
(
4
1
)
Her
e
T
F
is
t
h
e
f
o
r
w
ar
d
tr
an
s
it
ti
m
e.
W
e
n
ee
d
to
s
p
ec
i
f
y
C
J
E
an
d
T
F,
s
o
t
h
at
w
h
e
n
SP
I
C
E
r
u
n
s
a
s
i
m
u
latio
n
,
th
e
r
es
u
lti
n
g
C
π
w
ill
m
atc
h
t
h
e
d
esire
d
v
alu
e.
T
o
f
in
d
C
J
E
,
w
e
s
et
TF
=
0
s
,
an
d
m
o
d
eli
n
g
C
π
b
y
t
h
e
j
u
n
ctio
n
ca
p
ac
itan
ce
alo
n
e.
C
J
E
2
C
π
(
4
2
)
5
.
2
.
Si
m
ula
t
io
n
Fo
r
o
u
r
s
i
m
u
latio
n
w
e
u
s
e
th
e
2
N2
2
2
2
A
NP
N
B
J
T
,
th
e
d
ata
s
h
ee
t
o
f
th
e
tr
an
s
is
t
o
r
co
n
tai
n
th
e
i
n
f
o
r
m
atio
n
n
ee
d
ed
to
f
i
n
d
IS
,
b
elo
w
is
a
p
lo
t
o
f
V
BE
vs
.
I
C
f
o
r
th
e
u
s
ed
tr
an
s
i
s
to
r
[
26
].
Fig
u
r
e
4
s
h
o
w
s
th
e
B
ase
−
e
m
it
ter
v
o
ltag
e
.
Fig
u
r
e
4
.
B
ase
−
em
i
tter
v
o
lta
g
e
Fro
m
th
e
p
lo
t
ab
o
v
e
,
f
o
r
I
C
=
0
.
5
m
A
w
e
h
a
v
e
V
BE
=
0
.
6
2
V
at
2
5
°C
,
an
d
V
T
=
2
6
m
V
at
th
e
s
a
m
e
te
m
p
er
at
u
r
e.
Fro
m
(
3
4
)
,
IS
=
2
2
×
1
0
-
15
A.
T
h
e
f
o
ll
o
w
i
n
g
T
ab
le
5
p
r
esen
ts
V
AF
ca
lcu
lated
f
r
o
m
(
3
7
)
co
r
r
esp
o
n
d
s
to
ea
ch
tr
an
s
is
to
r
.
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
A
g
en
etic
a
lg
o
r
ith
m
fo
r
th
e
o
p
tima
l d
esig
n
o
f a
mu
ltis
ta
g
e
a
mp
lifi
er
(
E
l B
eq
a
l A
s
ma
e
)
137
T
ab
le
5
.
Valu
es o
f
V
A
F
T
r
a
n
si
st
o
r
1
T
r
a
n
si
st
o
r
2
T
r
a
n
si
st
o
r
3
VAF
(
V
)
0
.
5
2
0
.
5
4
0
.
8
1
A
D
C
an
al
y
s
i
s
r
ev
ea
l
s
th
at
V
CB
f
o
r
th
e
cir
cu
it
i
s
1
.
5
6
V,
f
r
o
m
(
3
9
)
an
d
(
4
2
)
,
w
e
f
i
n
d
C
J
C
an
d
C
J
E
co
r
r
esp
o
n
d
to
th
e
th
r
ee
tr
an
s
is
to
r
s
,
as
s
h
o
w
n
i
n
T
ab
le
6
.
Af
ter
s
ett
in
g
SP
I
C
E
p
ar
a
m
eter
s
,
w
e
s
i
m
u
late
th
e
th
r
ee
-
s
ta
g
e
a
m
p
li
f
ier
an
d
w
e
h
a
v
e
th
e
f
r
eq
u
en
c
y
r
esp
o
n
s
e
cu
r
v
e
o
f
th
e
v
o
lta
g
e
g
ai
n
f
o
r
E
1
2
as
s
h
o
w
n
i
n
Fig
u
r
e
5
,
w
e
n
o
tice
t
h
a
t
t
h
e
m
id
-
b
an
d
g
ai
n
is
1
9
.
1
2
d
B
,
th
e
u
p
p
er
cu
to
f
f
f
r
eq
u
en
c
y
is
1
4
.
1
1
MH
z
an
d
th
e
lo
w
er
cu
to
f
f
f
r
eq
u
e
n
c
y
i
s
3
3
.
5
6
Hz,
th
at
w
e
g
i
v
e
a
m
id
-
b
an
d
eq
u
al
to
1
4
.
1
0
MH
z.
T
ab
le
6
.
Valu
es o
f
C
J
C
a
n
d
C
J
E
f
o
r
E
1
2
T
r
a
n
si
st
o
r
1
T
r
a
n
si
st
o
r
2
T
r
a
n
si
st
o
r
3
C
JC
(
p
F
)
6
.
3
3
5
.
1
8
1
0
.
7
5
C
JE
(
p
F
)
4
.
1
0
3
.
4
0
7
.
5
0
Fig
u
r
e
5
.
Fre
q
u
en
c
y
r
e
s
p
o
n
s
e
cu
r
v
e
o
f
t
h
e
v
o
lta
g
e
g
ain
f
o
r
th
e
th
r
ee
-
s
ta
g
e
a
m
p
lifie
r
6.
CO
NCLU
SI
O
N
I
n
th
is
p
ap
er
,
w
e
h
a
v
e
p
r
esen
ted
an
ap
p
licatio
n
o
f
th
e
Gen
etic
A
l
g
o
r
ith
m
f
o
r
th
e
o
p
ti
m
a
l
d
esig
n
o
f
th
r
ee
-
s
ta
g
e
b
ip
o
lar
tr
an
s
i
s
to
r
a
m
p
li
f
ier
.
W
e
s
elec
ted
t
h
e
o
p
ti
m
al
v
al
u
es
o
f
d
i
s
cr
ete
co
m
p
o
n
en
t
s
f
r
o
m
d
if
f
er
en
t
m
an
u
f
ac
t
u
r
ed
s
er
ies
an
d
w
e
g
av
e
t
h
e
o
p
ti
m
al
v
al
u
es
f
o
r
t
h
e
h
y
b
r
id
p
ar
a
m
eter
s
o
f
t
h
e
tr
a
n
s
is
to
r
s
.
T
h
e
d
esi
g
n
o
f
t
h
e
a
m
p
li
f
ier
w
it
h
t
h
e
tar
g
e
ted
p
er
f
o
r
m
an
ce
s
i
s
s
u
cc
es
s
f
u
ll
y
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lized
b
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u
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o
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v
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it
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p
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tech
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iq
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as p
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o
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v
ia
SP
I
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E
s
i
m
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latio
n
.
RE
F
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R
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NC
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S
[1
]
P
a
u
l
R.
G
ra
y
,
P
a
u
l
J.
Hu
rst,
S
t
e
p
h
e
n
H.
L
e
w
is,
Ro
b
e
rt
G
.
M
e
y
e
r
,
An
a
lys
is
An
d
De
sig
n
Of
A
n
a
lo
g
In
teg
r
a
ted
Circ
u
it
s
,
Jo
h
n
W
il
e
y
&
S
o
n
s,
In
c
.
F
o
u
rth
Ed
it
io
n
,
2
0
0
1
.
[2
]
O.
J.
Us
h
ie,
M
.
A
b
b
o
d
,
a
n
d
E.
C
.
A
sh
ig
w
u
ik
e
,
“
Na
tu
ra
ll
y
Ba
se
d
Op
ti
m
isa
ti
o
n
A
lg
o
rit
h
m
f
o
r
A
n
a
l
o
g
u
e
El
e
c
tro
n
ic
Circu
it
s:
GA
,
P
S
O
,
A
BC,
BF
O,
a
n
d
F
iref
ly
a
C
a
se
S
t
udy
,”
J
o
u
rn
a
l
o
f
Au
t
o
ma
t
io
n
&
S
y
ste
ms
En
g
i
n
e
e
rin
g
,
v
o
l
.
9
,
n
o
.
3
,
p
p
.
1
7
3
-
1
8
4
,
2
0
1
5
.
[3
]
O.
J.
Us
h
ie
,
“
In
telli
g
e
n
t
o
p
ti
m
isa
ti
o
n
o
f
a
n
a
lo
g
u
e
c
irc
u
it
s
u
sin
g
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
is
a
ti
o
n
,
g
e
n
e
ti
c
p
ro
g
ra
m
m
in
g
a
n
d
g
e
n
e
ti
c
f
o
ld
in
g
,”
T
h
e
sis,
Bru
n
e
l
Un
iv
e
rsity
L
o
n
d
o
n
,
2
0
1
6
.
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
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p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
1
2
9
-
138
138
[4
]
I.
H.
Os
m
a
n
,
J.
P
.
Ke
ll
y
(Ed
s.),
M
e
ta
-
h
e
u
ristics
:
th
e
o
ry
a
n
d
a
p
p
li
c
a
ti
o
n
s
,
Kl
u
w
e
rs
A
c
a
d
e
m
ic
P
u
b
li
sh
e
rs
,
Bo
sto
n
,
1
9
9
6
.
[5
]
L
a
x
m
i
A
.
Be
w
o
o
r,
V.
Ch
a
n
d
ra
P
ra
k
a
sh
,
S
a
g
a
r
U.
S
a
p
k
a
l,
“
Co
m
p
a
ra
ti
v
e
A
n
a
l
y
sis
o
f
M
e
tah
e
u
risti
c
A
p
p
ro
a
c
h
e
s
f
o
r
M
a
k
e
sp
a
n
M
in
im
iza
ti
o
n
f
o
r
No
W
a
it
F
lo
w
S
h
o
p
S
c
h
e
d
u
li
n
g
P
r
o
b
lem
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
ter
E
n
g
in
e
e
rin
g
(
IJ
ECE
)
, v
ol
.
7
,
n
o
.
1
,
p
p
.
4
1
7
-
4
2
3
,
F
e
b
2
0
1
7
.
[6
]
M
.
Do
rig
o
a
n
d
S
.
Krz
y
s
z
to
f
,
An
In
tro
d
u
c
ti
o
n
t
o
A
n
t
Co
lo
n
y
Op
ti
miza
ti
o
n
,
a
c
h
a
p
ter
i
n
A
p
p
ro
x
im
a
ti
o
n
A
lg
o
rit
h
m
s
a
n
d
M
e
tah
e
u
risti
c
s
,
a
b
o
o
k
e
d
i
ted
b
y
T
.
F
.
G
o
n
z
a
lez
.
2
0
0
6
.
[7
]
M
.
Krish
n
a
v
e
n
i,
P
.
S
u
b
a
sh
i
n
i,
T
.
T
.
Dh
iv
y
a
p
ra
b
h
a
,
“
Im
p
ro
v
e
d
Ca
n
n
y
Ed
g
e
s
Us
in
g
Ce
ll
u
lar
Ba
s
e
d
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
T
e
c
h
n
iq
u
e
f
o
r
Ta
m
il
S
ig
n
Di
g
it
a
l
I
m
a
g
e
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
6
,
n
o
.
5
,
p
p
.
2
1
5
8
-
2
1
6
6
,
Oc
t
2
0
1
6
.
[8
]
J.
Dre
o
,
A
.
P
e
´
tro
w
sk
i,
P
.
S
iarr
y
,
E.
T
a
il
lard
,
M
e
ta
h
e
u
ristics
fo
r
h
a
rd
o
p
ti
miza
ti
o
n
:
M
e
th
o
d
s
a
n
d
c
a
se
st
u
d
ies
,
Ne
w
Yo
rk
:
S
p
rin
g
e
r,
2
0
0
6
.
[9
]
B.
Be
n
h
a
la
a
n
d
O.
Bo
u
a
tt
a
n
e
,
“
G
A
a
n
d
A
CO
t
e
c
h
n
iq
u
e
s
f
o
r
th
e
a
n
a
lo
g
c
ircu
it
s
d
e
sig
n
o
p
ti
m
iza
ti
o
n
,”
J
o
u
rn
a
l
o
f
T
h
e
o
re
ti
c
a
l
a
n
d
A
p
p
li
e
d
I
n
fo
rm
a
t
io
n
T
e
c
h
n
o
lo
g
y
(
J
AT
IT
)
,
v
o
l.
6
4
,
n
o
.
2
,
p
p
.
4
1
3
–
4
1
9
,
2
0
1
4
.
[1
0
]
F
.
G
lo
v
e
r,
“
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
,
v
o
l.
1
,
n
o
.
3
,
p
p
.
1
9
0
–
2
0
6
,
1
9
8
9
.
[1
1
]
F
.
T
.
S
.
Ch
a
n
,
M
.
K.
T
iw
a
ri,
S
wa
rm
In
telli
g
e
n
c
e
:
fo
c
u
s
o
n
a
n
t
a
n
d
p
a
rt
icle
swa
rm
o
p
ti
miza
ti
o
n
,
I
-
T
e
c
h
Ed
u
c
a
ti
o
n
a
n
d
P
u
b
l
ish
i
n
g
,
2
0
0
7
.
[1
2
]
B.
Be
n
h
a
la,
“
A
n
im
p
ro
v
e
d
a
c
o
a
lg
o
rit
h
m
f
o
r
th
e
a
n
a
lo
g
c
ircu
it
s
d
e
sig
n
o
p
ti
m
iza
ti
o
n
,”
I
n
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
Circ
u
it
s,
S
y
ste
ms
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
1
0
,
pp.
1
2
8
-
1
3
3
,
2
0
1
6
.
[1
3
]
L
.
Kritele
,
B.
Be
n
h
a
la,
a
n
d
I.
Z
o
rk
a
n
i
,
“
A
n
t
Co
l
o
n
y
Op
ti
m
iza
ti
o
n
f
o
r
O
p
ti
m
a
l
L
o
w
-
P
a
ss
S
tate
V
a
riab
le
F
il
ter
S
izin
g
,
”
In
ter
n
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
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
8
,
n
o
.
1
,
p
p
.
2
2
7
-
2
3
5
,
F
e
b
2
0
1
8
.
[1
4
]
L
.
Kritele
,
B.
Be
n
h
a
la,
I.
Z
o
rk
a
n
i,
“
Op
ti
m
a
l
Dig
it
a
l
IIR
F
il
ter
De
sig
n
Us
in
g
A
n
t
Co
lo
n
y
Op
ti
m
iza
ti
o
n
,”
IEE
E
4
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
O
p
ti
miza
ti
o
n
a
n
d
Ap
p
li
c
a
t
io
n
s (
ICOA'1
8
)
,
M
o
h
a
m
m
e
d
ia,
M
o
ro
c
c
o
,
p
p
.
1
-
5
,
A
p
r
20
18
.
[1
5
]
H.
Bo
u
y
g
h
f
,
B.
Be
n
h
a
la
a
n
d
A
.
Ra
ih
a
n
i,
“
Op
ti
m
iza
ti
o
n
o
f
6
0
-
G
HZ
d
o
w
n
-
c
o
n
v
e
rti
n
g
CM
OS
d
u
a
l
-
g
a
te
m
i
x
e
r
u
sin
g
a
rti
f
icia
l
b
e
e
c
o
lo
n
y
a
lg
o
rit
h
m
,”
J
o
u
rn
a
l
o
f
T
h
e
o
re
ti
c
a
l
a
n
d
Ap
p
li
e
d
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
(
J
AT
IT
)
,
v
o
l.
9
5
,
n
o
4
,
p
p
.
8
9
0
–
9
0
2
,
2
0
1
7
.
[1
6
]
H.
Bo
u
y
g
h
f
,
B.
Be
n
h
a
la
a
n
d
A
.
Ra
ih
a
n
i,
Op
t
ima
l
d
e
sig
n
o
f
RF
C
M
OS
c
irc
u
it
s
b
y
me
a
n
s
o
f
a
n
a
rt
i
fi
c
ia
l
b
e
e
c
o
l
o
n
y
tec
h
n
iq
u
e
,
Ch
a
p
ter
1
1
,
B
o
o
k
:
F
o
c
u
s
o
n
sw
a
r
m
in
telli
g
e
n
c
e
re
s
e
a
r
c
h
a
n
d
a
p
p
li
c
a
ti
o
n
s,
Ed
s.
,
B.
Be
n
h
a
la,
P
.
P
e
re
ira
a
n
d
A
.
S
a
ll
e
m
,
NO
V
A
S
c
ien
c
e
P
u
b
li
s
h
e
rs,
p
p
.
2
2
1
–
2
4
6
,
2
0
1
7
.
[1
7
]
H.
Bo
u
y
g
h
f
,
B.
Be
n
h
a
la,
A
.
Ra
ih
a
n
i
,
“
A
n
a
l
y
sis
o
f
th
e
i
m
p
a
c
t
o
f
m
e
tal
th
ick
n
e
ss
a
n
d
g
e
o
m
e
tri
c
p
a
ra
m
e
ters
o
n
th
e
q
u
a
li
ty
f
a
c
to
r
-
Q
in
i
n
teg
ra
ted
sp
ir
a
l
in
d
u
c
to
rs b
y
m
e
a
n
s
o
f
a
rti
f
icia
l
b
e
e
c
o
lo
n
y
tec
h
n
iq
u
e
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
9
,
n
o
.
4
,
p
p
.
2
9
1
8
-
2
9
3
1
,
A
u
g
2
0
1
9
.
[1
8
]
R.
L
.
Ha
u
p
t
a
n
d
S
.
E.
Ha
u
p
t,
Pr
a
c
t
ica
l
Ge
n
e
ti
c
Al
g
o
rit
h
ms
,
(b
o
o
k
)
Jo
h
n
W
il
e
y
&
S
o
n
s,
2
0
0
4
.
[1
9
]
V
.
Z
.
M
a
n
u
so
v
,
P
.
V
.
M
a
tren
i
n
,
E.
S
.
T
re
ti
a
k
o
v
a
,
“
I
m
p
le
m
e
n
tatio
n
o
f
P
o
p
u
lati
o
n
A
lg
o
rit
h
m
s
to
M
in
im
ize
P
o
w
e
r
L
o
ss
e
s
a
n
d
Ca
b
le
Cro
ss
-
S
e
c
ti
o
n
in
P
o
w
e
r
S
u
p
p
ly
S
y
ste
m
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
6
,
n
o
.
6
,
p
p
.
2
9
5
5
-
2
9
6
1
,
De
c
2
0
1
6
.
[2
0
]
M
.
M
o
n
taz
e
ri
-
G
h
,
A
.
P
o
u
rsa
m
a
d
a
n
d
B.
G
h
a
li
c
h
i,
“
A
p
p
li
c
a
ti
o
n
o
f
g
e
n
e
ti
c
a
l
g
o
rit
h
m
f
o
r
o
p
ti
m
iza
t
io
n
o
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tt
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:
//
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.
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