Today
we
're
going
to
talk
about
how
to
use
JMP
to
do
thin
film
development
using
plasma
-enhanced
chemical
vapor
deposition
.
This
is
a
bit
different
,
I
think
,
than
some
of
the
typical
work
that
's
done
in
industry
,
where
it
's
a
continuously
stirred
reactor
and
you
can
always
mix
things
.
Regardless
of
what
happens
,
you
can
always
measure
outputs
.
But in
plasma-enhanced
chemical
vapor
deposition
,
it
's
really
discrete
pockets
of
stability
that
you
have
to
work
with
.
Even
though
we
can
set
up
a large
parameter
space
,
there
can
be
spots
within
that
parameter
space
where
you
may
not
be
able
to
strike
the
plasma
or
it
could
arc
because
the
power
density
is
too
high
.
Since
we
have
a
large
number
of
deposition
parameters
,
we
need
to
use
a
design
of
experiments
to
effectively
explore
that
parameter
space
.
Even
if
we
're
able
to
strike
the
plasma
,
there
are
still
issues
with
thin
film
uniformity
.
We
're
depositing
nanometer
films
with
nearly
perfect
uniformity
across
a
12
-inch
wafer
.
Once
we
get
that
,
we
still
have
to
hit
the
targeted
film
properties
.
We
're
going
to
talk
about
how
to
use
PECVD
to
develop
new
thin
films
from
new
precursors
.
The
first
thing
we
're
going
to
do
is
talk
about
Precursor
1
.
From
what
I
was
able
to
read
from
the
JMP
tutorials
,
the
Definitive
Screening
Design
is
a
very
effective
way
to
screen
a
large
number
of
main
effects
in
the
fewest
number
of
experiments
.
That
's
key
to
the
work
that
we
do.
We
want
to
get
the
right
answer
in
the
shortest
amount
of
time
with
a
data
-driven
approach
.
We
used
a
Definitive
Screening
DOE
to
explore
seven
factors
in
26
runs
.
What
we
'll
do
is
just
open
up
that
initial
DOE
.
This
is
the
setup
we
came
for
the
Definitive
Screening
DOE
.
Here
are
the
seven
different
factors
we're
varying
for
the
deposition,
and
our
output
is
going
to
be
this
parameter
Y,
and
we
're
trying
to
maximize
it
.
If
we
look
at
the
range
of
parameters
for
this
type
of
PECVD
processing
,
this
is
a
very
wide
range
of
initial
parameters
.
Again
,
we
're
trying
to
screen
for
main
effects
,
and
our
outputs
are
ranging
from,
say
,
9-34
,
and
our
baseline
was
21
.
We
do
see
an
improvement
there
.
One
of
the
things
that
I
always
like
to
do
when
we
do
a
DOE
is
include
a
center
point
replicate
or
a
repeat
run
to
see
how
reproducible
the
instrument
is
,
as
well
as
to
make
sure
that
the
statistics
we
generate
within
the
design
are
valid
.
These
are
the
two
center
-point
runs
,
and
you
can
see
we
get
excellent
reproducibility
.
The
other
thing
that
's
really
nice
for
us
to
do
before
we
get
into
fitting
the
model
is
just
to
look
at
the
output
variables
and
try
to
identify
any
trends
that
we
can
see
.
Is
there
anything
we
can
identify
quickly
that
we
can
attribute
the
main
factors
to
?
Here,
there
's
four
points
with
a
Y
value
of
greater
than
30
within
the
DOE
.
If
we
select
those
points
,
it
's
nice
to
see
if
we
can
see
any
trends
associated
with
these
data
.
One
of
the
fastest
ways
I
found
to
do
that
is
to
quickly
do
a
multivariate
analysis
,
and
we
can
do
this
graphically
.
What
we
're
going
to
do
is
take
all
our
factors
and
then
our
output
variable
,
and
we
're
going
to
generate
a
multivariate
analysis
.
Here
in
this
graph
,
this
is
our
Y
value
.
You
can
see
as
we
go
from
10 to
30
,
the
four
values
that
are
the
highest
are
highlighted
,
but
the
rows
are
the
various
factors
.
Here
we
can
see
for
helium
,
we
have
the
highest
values
at
the
high
and
low
splits
.
For
precursor
,
we
have
the
high
and
low
splits
.
But for
temperature
and
pressure
,
we
have
the
highest
values
at
the
lowest
splits
.
It
's
really
,
I
think
,
a
good
indication
initially
before
we
've
done
any
model
fitting
,
that
temperature
and
pressure
could
be
important
variables
for
us
to
look
at
.
If
we
go
back
to
the
table,
the
other
nice
thing
about
JMP
,
it
's
very
powerful
because...
Again
,
before
we
do
the
definitive
screening
,
we
can
use
a
predictor
screening
to
identify
what
are
the
most
important
factors
.
Again
,
we
use
the
standard
analysis
,
input
our
factors
,
our
response
is
Y
,
and
you
can
see
what
the
predictor
screening
is
telling
us
is
,
yes
,
pressure
and
temperature
are
very
important
.
But one
thing
that
we
didn
't
catch
in
that
multivariate
analysis
is
the
precursor
flow
.
These
three
factors
,
pressure
,
precursor
flow
,
and
temperature
appear
to
be
dominant
in
giving
us
the
highest
values
of
Y
.
Now
I
wanted
to
fit
the
model
because
I
think
the
real
power
of
the
DOE
is
not
the
runs
in
the
table
,
but
it
's
the
response
surface
model
that
you
can
use
to
get
predictions
for
improvement
as
well
as
directions
to
further
explore
.
But when
I
went
to
analyze
it
,
it
wouldn
't
work
.
It
turns
out
we
were
only
able
to
complete
25
of
the
26
runs
,
and
I
was
not
aware
that
the
Definitive
Screening
DOE
,
the
default
analysis
would
not
work
if
you
did
not
complete
all
of
the
runs
.
At
this
point
,
I
contacted
Jed
at
JMP
to
help
me
understand
how
I
could
get
some
models
out
of
this
data
that
we
carefully
collected
over
a
period
of
time
.
I
'll
turn
it
over
to
Jed
.
When
Bill
called
,
like
he
said
,
when
he
hit
that
script
that
saved
to
the
data
table
of
Fit
Definitive
Screening
,
nothing
happens
.
If
you
look
over
here
,
the
log
is
saying
,
there
are
runs
that
are
not
fold
over
center
point
run
,
and
it
's
run
17
,
which
is
obviously
the
run
that
was
missing
and
couldn
't
be
completed
in
the
experiment
.
What
Bill
wanted
was
a
way
to
still
fit
that
Definitive
Screening
model
.
We
came
up
with
two
different
approaches
and
had
three
models
that
came
about
from
those
two
different
approaches
.
The
first
one
is
related
to
the
Definitive
Screening
.
The
designs
of
these
types
of
models,
of
these
experiments,
are
always
fold
-over
pairs
,
where
there
's
a
pair
of
opposites
.
If
we
can
find
that
fold
-over
pair
,
or
the
twin
,
I
guess
,
of
this
row
17
,
we
should
be
able
to
exclude
both
rows
and
then
fit
the
definitive
screening
design
.
We
just
needed
a
simple
way
to
do
that
.
What
we
came
up
with
was
basically
to
use
a
couple
of
shortcuts
.
I
'm
going
to
first
standardize
the
attributes
of
these
columns
and
change
the
modeling
type
to
ordinal
.
As
I
do
this
,
you
'll
notice
that
my
ability
to
select
has
changed
.
That
helps
when
I
look
at
a
data
filter
.
Now
I
have
boxes
rather
than
histograms
,
so
it
just
makes
it
faster
to
select
.
What
we
need
to
do
is
find
the
opposite
row
of
this
.
I
have
this
row
17
selected
,
and
you
see
that
it
's
high
,
high
,
high
,
low
,
low
,
low
,
and
then
I
'm
out
of
memory
space
.
I
'm
remembering
high
,
high
,
high
,
low
,
low
,
low
.
I
need
to
find
the
opposite
of
that
,
which
is
going
to
be
low
,
low
,
low
,
high
,
high
,
high
.
If
I
just
come
over
here
and
start
working
my
way
down
,
low
,
low
,
low
,
high
,
by
the
time
I
get
to
just
four
of
the
runs
so
just
more
than
half
of
the
factors
selected
,
now
I
'm
down
to
just
one
matching
row
.
It
just
so
happens
that
the
very
next
run
was
the
fold-over
pair
in
this
experiment
.
We
can
select
both
of
those
runs
,
exclude
them
,
and
then
go
back
in
into
the
column
properties
and
change
that
modeling
type
back
to
continuous
.
Now
when
we
hit
that
Definitive
Screening
button
,
it
works
.
We
can
run
that
model
and
see
that
it
's
predicting
fairly
well
.
We
can
see
the
profiler
,
but
we
also
were
really
aware
that
one
of
the
runs
out
of
26
,
that
's
almost
4
%.
We
're
throwing
away
roughly
4
%
of
the
information
by
excluding
this
.
What
we
'd
really
like
to
do
is
not
throw
that
information
away
,
find
a
way
to
use
that
.
We
used
the
Model Screening
platform
in
JMP
Pro
to
run
a
bunch
of
models
and
then
select
the
best
.
The
two
that
came
out
the
best
were
a
Neural
and
Stepwise
model
,
and
I
can
walk
through
those
really
quickly
.
The
Neural
model
was
with
our
response
and
our
factors
.
Since
this
was
a
DOE
,
we
're
going
to
do
the
minimum
holdback
,
and
I
'm
just
going
to
choose
a
random
seed
so
this
is
repeatable
.
The
Model Screening
platform
generally
suggests
about
20
boosts
.
If
I
hit
Go
here
,
I
get
a
pretty
good
Rsquare
across
this.
Maybe
I
might
try
to
tune
this
model
by
adding
some
more
parameters
,
but
when
I
do
,
I
can
see
that
R
square
is
not
really
changing
.
I
don
't
think
I
want
to
add
more
parameters
and
risk
overfitting
the
model
.
That
was
one
extra
way
to
do
it
.
Then
the
second
model
that
showed
up
easily
for
us
using
the
Model Screening
platform
was
to
do
Stepwise
.
The
way
we
did
that
was
we
put
our
output
here
and
then
use
the
shortcut
to
do
a
response
surface
.
That
includes
all
main
factors
,
all
squared
terms
,
and
all
two
-way
interactions
.
Then
if
we
change
this
to
Stepwise
here
,
we
can
hit
the
Run
button
and
Go
.
Now
JMP
is
going
to
enter
and
exit
everything
until
it
finds
the
model
that
fits
the
best
.
We
can
go
ahead
and
run
that
.
Now
we
have
three
models
that
we
want
to
compare
.
What
I
'm
going
to
do
is
I
'm
going
to
take
this
first
model
and
save
.
I
'm
going
to
publish
that
prediction
formula
to
the
Formula
Depot
.
I
'm
going
to
give
it
a
really
quick
name
,
and
we
will
call
this
DSD
.
Whatever
,
I
can
't
type
.
We
'll
call
it
DSD
,
and
then
close
it
.
We
'll
do
this
with
the
Neural
as
well
.
We
will
publish
that
prediction
formula
,
give
it
a
name
,
and
do
the
same
with
this
final
model
where
we
will
publish
that
prediction
formula
.
This
last
one
was
,
we
called
it
Stepwise
.
Now
I
have
these
three
models
and
I
can
compare
them
.
We
can
run
the
Model
Comparison
platform
for
all
three
of
them
from
within
the
Formula
Depot.
We
can
get
a
rank
of
the
Rsquares
of
those
models
.
We
can
look
at
the
actual
versus
predicted
,
and
we
can
see
that
they
're
all
predicting
about
the
same
.
We
can
also
look
at
the
predicted
by
row
,
and
we
can
see
this
one
point
from
the
Definitive
Screening
Design
is
the
one
that
was
left
out
when
we
fit
the
original
Definitive
Screening
Design
.
It
seems
to
be
important
,
and
probably
most
importantly
,
we
can
look
at
a
profiler
for
all
of
these
against
each
other
.
If
I
turn
off
the
desirability
,
we
can
see
how
these
models
compare
to
each
other
.
For
example
,
we
can
look
at
that
Definitive
Screening
Design
and
see
that
it
's
showing
some
curvature
where
the
other
two
models
are
not
.
Maybe
we
can
look
and
see
over
here
that
the
curvature
is
different
for
each
of
those
.
Then
the
question
becomes
,
which
model
is
best
and
how
do
I
know
?
Then
that
brings
us
back
to
Bill
.
Thanks
,
Jed
.
Let
me
share
my
screen
.
Can
you
see
my
screen
?
Yes
.
I
'm
going
to
just
...
Jed
saved
all
that
to
the
Formula
Depot,
so I
'm
just
going
to
execute
the
script
that
will
take
us
to
the
Formula
Depot
that
he
's
already
saved
.
Again
,
we
'll
then
just
go
right
to
the
profiler
.
We
're
going
to
fit
all
three
of
these
.
Then
we
have
the
profiler
.
This
is
what
the
real
power
,
I
think
,
of
the
DOE
is,
because
the
Prediction Profiler
,
we
can
optimize
and
maximize
the
desirability
.
The
response
surface
models
will
tell
us
what
combination
of
factors
we
need
to
get
the
highest
elastic
or
highest
value
of
the
output
parameter
.
What
was
really
eye
-opening
for
me
is
if
you
look
at
the
values
that
we
get
when
we
do
this
optimization
,
two
of
the
predictions
,
the
Neural
net
and
the
Definitive
Screening
DOE,
are
giving
us
values
of
the
Y
parameter
that
are
greater
than
anything
we
saw
in
our
initial
data
table
.
We
had
a
maximum
value
of
34
.
Let
's
see.
I
'm
sorry
,
I
just
got
to
get
that
screen
back
.
Typically
,
it
's
very
unusual
for
me
to
see
this
with
the
DOE
.
Typically
,
the
model
,
if
you
maximize
it
,
is
generally
close
to
what
you
see
as
in
the
table
.
But in
this
case
,
it
looks
like
we
really
have
some
low
-hanging
fruit
.
We
needed
to
test
this
combination
of
parameters
and
really
see
if
that
prediction
was
valid
or
not
.
If
we
go
back
to
our
JMP
journal...
I
just
want
to
show
you
what
happened
.
We
took
,
I
think
this
is
the
prediction
from
one
of
the
Neural
network
fits
.
Again
,
the
highest
value
in
the
Definitive
Screening
DOE
was
34
.
The
model
prediction
was
42
,
but
when
we
actually
ran
it
,
we
saw
some
artifacts
in
the
film
that
were
not
acceptable
.
The
plasma
itself
was
stable
.
There
was
no
way
to
see
this
until
the
wafer
came
out
of
the
reactor
.
But you
can
see
there
's
a
bullseye
pattern
,
which
is
due
to
film
non
-uniformity
.
In
this
case
,
it
's
very
thick
in
the
middle
and
thin
at
the
edge
,
which
gives
us
this
bullseye
pattern
.
Then
if
you
look
carefully
,
you
can
see
all
these
small
dots
over
the
wafer
,
which
are
actually
the
holes
under
the
shower head
.
The
shower head
has
thousands
of
small
holes
where
the
gas
comes
out
.
In
this
case
,
we
have
a
shower
head
pattern
and
a
bullseye
.
I
think
the
model
is
telling
us
what
direction
to
go
.
But again
,
plasmas
are
challenging
to
use
.
Even
though
the
model
was
telling
us
this
film
should
have
the
highest
value
of
Y
,
the
film
itself
was
unacceptable
.
Then
we
have
to
rely
on
our
process
and
theoretical
knowledge
of
the
process
.
We
know
that
argon
has
a
lower
ionization energy,
and
if
we
substitute
argon
for
helium
and
the
plasma
,
we
can
get
a
higher
plasma
density
,
which
may
help
us
overcome
these
challenges
.
What
we
did
is
switch
to
argon
,
and
you
can
see
,
although
the
film
is
not
perfect
,
it
's
much
more
uniform
and
certainly
good
enough
for
us
to
get
the
physical
properties
of
the
film
that
we
can
use
.
In
this
case
,
we
were
able
to
hit
a
Y
value
of
46
,
which
again
,
is
much
greater
than
34
.
We
're
certainly
trending
in
the
right
direction
.
What
we
really
wanted
to
do
is
,
are
there
any
opportunities
for
us
to
further
improve
the
film
?
Again
,
that
's
where
the
Prediction Profiler
or
the
response
surface
models
are
very
powerful
.
If
we
just
look
at
the
trends
that
we
see
here...
I
'm
just
going
to
blow
these
up
so
we
can
see
them
a
little
bit
better
for
each
of
these
cases
.
The
data
is
really
telling
us,
in
certain
cases
,
there
's
things
that
we
really
want
to
investigate
.
Lower
temperatures
look
like
it
's
definitely
favoring
the
highest
value
of
Y
,
pressure
appears
to
be
a
key
parameter
,
and
low
-frequency
power
for
this
initial
DOE
looks
like
in
two
cases
,
you
want
to
go
to
higher
low
-frequency
power
.
The
Stepwise
is
giving
us
the
opposite
.
But you
can
see
that
this
is
really
a
blueprint
for
us
to
do
an
additional
design
to
see
how
far
we
can
push
it
.
Can
we
go
to
lower
flow
rates,
can
we
go
to
lower
temperatures,
can
we
go
to
lower
pressures
and
further
improve
the
film
properties
?
It
's
really
sequential
learning
,
and
that
for
me
,
is
the
real
power
of
the
DOE
.
We
don
't
really
have
time
to
go
through
all
of
that
,
but
what
I
did
is
put
together
a
new
JMP
table
with
the
results
from
our
sequential
learning
for
this
set
of
experiments
.
Here
is
the
same
data
we
saw
in
the
Definitive
Screening
DOE
.
Here
are
our
Y
values
,
and
we
're
ranging
from
9 to
34
.
The
different
colors
are
the
different
DOE
.
Here
's
the
next
DOE
that
we
did
.
What
you
can
see
is
based
on
the
trends
in
the
Prediction
Profiler
and
the
response
surface
models
,
we
fixed
the
low
-frequency
power
at
the
highest
setting
we
could
.
It
turns
out
physical
limitations
for
this
plasma
chemistry
prevented
us
from
adding
any
more
than
20
%
low
-frequency
power
.
We
also
fixed
the
temperature
.
We
can
't
operate
for
this
chemistry
below
200
.
We
know
that
lower
temperature
gave
us
the
highest
value
,
so
we
fixed
these
two
and
then
did
a
five
-factor
DOE
focusing
on
lower
precursor
flows
,
various
spacings
,
the
higher
powers
,
and
certainly
the
lower
pressures
,
which
was
indicated
as
one
of
the
most
important
parameters
.
If
you
look
at
the
Y
values
here
,
you
can
see
we
're
definitely
trending
in
the
right
direction
.
Now
we
're
going
from
mid
20s
up
to
56
.
We
're
certainly
above
the
46
we
saw
there
.
Then
we
did
the
same
learning
.
Again
,
the
Prediction Profiler
indicated
what
parameters
we
should
explore
.
We
did
another
DOE
.
In
this
case
,
we
fixed
different
parameters
,
but
you
can
see
that
the
trend
is
the
same
.
Now
we
're
hitting
up
to
66
in
terms
of
our
Y
value
,
and
we
did
one
final
experiment
,
and
in
this
case
you
can
see
basically
the
sum
of
all
the
knowledge
that
we
gained
.
It
turned
out
when
we
switched
to
argon
,
you
could
add
more
low
-frequency
power
.
You
could
go
from
zero
to
40
%.
In
our
final
analysis
,
this
DOE
showed
that
unlike
the
first
DOE
,
after
we
finetuned
everything
and
switched
gasses
,
the
low
-frequency
power
had no
statistical
impact
on
the
Y
value
.
We
set
that
to
zero
.
We
found
out
the
lowest
spacing
was
the
most
important
.
Our
sweet
spot
for
pressure
was
2
.3
torr
,
and
we
did
want
to
operate
at
the
lowest
temperature
.
Really
we
had
a
three
-factor
DOE
between
total
power
,
precursor
flow
,
and
argon
dilution
to
really
dial
in
the
films
,
and
we
could
hit
a
maximum
value
of
84
.
I
summarized
all
that
in
a
box
plot
here
,
which
I
think
really
shows
the
ability
of
the
DOE
and
sequential
learning
,
where
we
started
out
with
a
seven
-factor
Definitive
Screening
DOE
with
26
runs
and
ended
up
with
a
three
-factor
I
-optimal
design
with
16
runs
,
but
you
can
see
our
continued
improvement
.
This
was
our
reference
target
,
so
we
still
have
more
work
to
do
.
But this
is
a
very
powerful
way
for
us
to
screen
seven
factors
with
three
-level
designs
in
a
very
short
period
of
time
.
I
do
think
it
's
worthwhile
just
to
point
out
how
efficient
these
new
modern
DOE
s
are
.
If
we
looked
at
what
we
're
really
doing
,
we
have
seven
factors
that
we
started
with
.
All
of
these
are
three
-level
designs
.
For
a
three
-level
,
seven
-factor
design
,
that
would
be
over
2
,100
runs
.
We
could
run
90
out
of
the
experimental
designs
and
achieve
this
increase
in
the
Y
value
.
I
think
these
modern
designs
,
the
optimal
designs
combined
with
the
Definitive
Screening
DOE
s
are
a very
powerful
tool
for
us
to
get
the
most
value
with
the
fewest
number
of
experiments
.
The
final
thing
I
want
to
touch
on
is
when
we
switched
to
a
different
precursor
.
This
is
really
a
different
challenge
we
faced
.
The
goal
here
was
to
evaluate
different
precursors
to
compare
how
it
stacked
up
against
the
initial
baseline
film
.
What
we
tried
to
do
is
use
all
of
our
learning
from
those
four
DOEs
and
become
even
more
efficient.
Instead
of
90
runs,
can
we
do
this
in
52
runs
?
With
Jed
's
help
,
we
put
together
an
eight
-factor
A
-optimal
design
,
but
what
we
found
is
that
the
chemistry
was
shockingly
different
.
All
of
the
parameter
space
that
we
could
operate
easily
with
Precursor
1
was
not
the
case
here
.
In
fact
,
I
put
together
a
slide
to
show
you
how
bad
some
of
these
films
could
look
,
so
we
could
get
perfect
films
.
You
'd
be
hard
-pressed
to
tell
there
's
a
film
.
This
is
a
nanometer
-thick
film
,
edge
to
edge
on
a
12
-inch
silicon
wafer
,
perfectly
uniform
.
Then
we
would
have
films
that
look
like
this
.
Obviously
,
that
's
not
a
design
that
we
wanted
.
But the
challenge
that
we
faced
was
,
we
're
doing
an
eight
-factor
DOE
,
and
we
're
trying
to
do
this
quickly
and
efficiently
,
and
30
%
of
the
runs
failed
.
I
'm
looking
at
a
table
with
eight
different
factors
.
How
do
I
pick
out
the
factors
that
are
contributing
to
this
?
What
we
did
is
we
created
a
table
for
our
eight
input
factors
and
then
identified
all
of
the
films
that
had
delamination
,
arcing
,
or
other
issues
,
and
then
created
a
metric
,
just
a
film
metric,
pass
or
fail
.
It
turns
out
we
can
fit
this
categorical
variable
and
see
if
we
can
get
a
model
that
will
help
us
understand
what
is
really
causing
these
issues
through
all
these
films
.
The
first
thing
we
can
do
quickly
is
again
go
to
our
Model Screening platform,
Predictor
Screening
,
and
get
a
handle
on
what
factor
,
if
any
,
is
really
controlling
film
quality
.
If
we
look
at
this
,
it
's
pretty
clear
and
it
's
shocking,
because
this
was
not
the
case
with
Precursor
1
.
The
flow
rate
of
the
precursor
was
by
far
and
away
the
most
dominant
factor
impacting
film
quality
.
But what
we
needed
to
run
these
experiments
is
not
just
knowing
this
factor
,
but
what
value
can
we
safely
run
to
generate
quality
films.
That
's
where
we
did
the
Neural
net
.
Again
,
we
'll
go
into
Predictive
Modeling,
Neural
net,
we
'll
take
our
factors
,
and
we
're
going
to
fit
film
quality
as
a
categorical
variable
.
I
'm
going
to
go
to
boosting
of
20
,
as
Jed
mentioned
.
That
's
typically
what
the
Model Screening
comes
up
with
,
and
we
'll
generate
our
model
,
and
you
can
see
we
get
excellent
Rsquare
values
.
This
is
a
categorical
model
.
I
believe
that
looking
at
the
ROC
curve
provides
insight
into
how
well
the
model
fits
.
If
the
curve
is
along
this
diagonal
,
it
's
just
basically
a
guess
.
This
looks
like
a
half
square
wave
.
These
are
basically
perfect
fits
in
the
ROC
curve
.
Then
the
question
is
,
how
can
we
utilize
that
data
?
The
nice
thing
about
the
Neural
net
is
that
you
have
a
Categorical Profiler.
I
can
execute
the
Categorical Profiler
,
and
now
I
set
this
up
where
we know
we
want
to
operate
at
lower
pressures
.
We
know
from
the
previous
work
we
want
to
operate
at
lower
spacings
.
We
want
to
go
to
the
lowest
possible
temperature.
We
'll
just
set
this
in
the
middle
.
Then
basically
we
have
this
profiler
that
tells
us
the
primary
factor
affecting
this
is
really
the
argon
flow
rate
.
If
we
can
keep
our
flow
rate
below
400
sccms
,
we
can
have
a
100
%
success
rate
for
the
films
that
we
're
trying
to
optimize
.
With
this
,
we
set
up
a
new
DOE
,
limiting
the
total
flow
rate
to
400
sccms
.
We
'll
go
back
to
our
JMP
journal
.
We
were
able
to
come
up
with
a
new
design
and
complete
41
of
the
42
runs
,
and
we
're
still
executing
that
study
.
But it
just
shows
how
powerful
the
Neural
network
is
for
a
categorical
variable
where
we
can
do
this
in
an
afternoon,
where
at
one
o
'clock
we
found
these
films
weren
't
working
.
Three
hours
later
,
we
had
a
model
that
told
us
how
to
set
up
a
new
design
and
we
were
executing
that
later
that
day
.
I
think
that
's
the
material
we
wanted
to
cover
.
