Hello
everyone,
thanks
for
joining
us
today
and
I
really
appreciate
to
have
the
opportunity
to
tell
you
my
story:
How
to
enhance
data
analytic
skills
by
advocating
JMP in
a
Company.
So
this
is
the
outline.
After
introducing
my
company
Siltronic
and
myself
Georg
Raming,
I
will
tell
about
data
analytics
at Siltronic
and
then
how
I
started
with
JMP
and
what
my
way
was,
what
my
first
target
was,
second
target,
and
what
our
current
approach
is.
So
about
Siltronic.
We
have
four
world-
class
production
sites
in
United
States,
in
Europe,
Germany:
Burghausen
and
Freiberg,
and
in
Asia:
Singapore.
We
have
around
4,000
employees
with
global
scale
and
reach
and
profound
knowledge
in
silicon
technologies,
more
than
50
years.
So
this
is
the
history
of
Siltronic.
So
the
first
silicon
wafers
have
been
developed
in 1962.
The
first
200
millimeter
wafer
in 1984,
and
meanwhile
we
have
found
that
some
sites
in
Portland,
United
States,
and Freiberg
in
Germany
the
first
300
millimeter
wafer
has
been
developed
in 1990
and first
300
millimeter
production
in
Freiberg
in
2004.
And
currently,
we
are
developing
a
new
fab
like
here
written
in
Singapore
2021.
So
this
is
the
electronics
value
chain.
Here
you
can
see
coming
from
the
raw
material,
its
ultra-
pure
silicon
worth
about
$1.2
billion,
and
semiconductor
silicon
wafers
are
around
tenfold
worth
$11.2
billion.
The
semiconductors
are
even
worth
much
more,
and
the
electronics
are
around
$1
,650 billion
And
the
high
demand
for
these
products
drive
our
business
with
silicon
semiconductor
wafers.
This
is
where
our
sites
are
in
more
detail.
So
we
have
a
200
millimeter
fab
in
Portland,
United
States.
We
have
300
millimeter
wafer
fab
and
small
diameter
fabs
in
Burghausen
and
Freiberg
as well
crystal
pulling
and
300
millimeter
wafer
fab.
And
in
Singapore
we
have
a
200
millimeter
wafer
fab,
and 300
millimeter
wafer
fab
and
300
millimeter
crystal
pulling.
So
the
Singapore
fabs
are
among
the
world's
newest
and
largest
,
and
Central
R&
D hub
is
in
Burghausen,
Germany.
This
is
how s
ilicon
wafers
are
produced.
So
starting
at
the
raw
material
ultra-pure
silicon,
we
have
two
methods
for
growing
single
crystals
that
is
Czochralski pulling
and
Float
Zone
pulling.
And
after
growing
the
ingot,
the
mechanical
preparation
takes
place
like
ingot
grinding,
multi
wire
slicing,
edge
rounding.
And
then
the
wafer
steps
come
like
your
laser
marking,
lapping,
cleaning,
and
edging
polishing.
And
for
a
part
of
the
product
epitaxi.
Our
product
portfolio
mostly
is
300
millimeter
wafer
with
CZ
process
Czochralski
for
memory
logic
and
analog,
and
smaller
part
is
200
millimeter
and
125
millimeter
with
pulling
Czochralski ingots
and
Float
Zone
ingots.
And
there
we
have
applications
like
Logic,
Analog,
Discretes,
image
sensors,
Power
Optoelectronics,
and
IGBTs.
And
special
products
like
highly-
doped
wafers
as
well.
So
our
key
requirements
on
the
ingot
side
are
purity,
homogeneity,
mechanical
stability,
oxygen
content,
and
the
more
like
this.
On
the
wafer
side,
we
have
flatness,
uniformity,
edge
flatness,
surface
cleanliness,
and
the
like
more.
And
to
make
the
requirements
a
little more
impressive
and
understandable,
what
means
purity
of
one
part
per
trillion?
It
is
not
more
than
three
to
four
dissolved
sugar
cubes
in
a
lake
like
Chlemsee in
Bavaria,
Germany.
And
flatness
of
a wafe
r means
20
nanometer
in
height
on
a
wafer,
like
a
flat
leaf
on
the
surface
of
the Chlemsee
.
Now
about
me.
So
I'm
an
Electrical
Engineer
with
a
PhD
in
simulation
of
electrothermal
processes.
I
also
have
some
statistical
background
like
Six
Sigma
Black
Belt,
and
my
task
is
development
of
silicon
single
growth
processes
at
Siltronic
in
Burg
hausen.
And
I
have
also
many
years
of
experience
in
data
science,
like
tasks.
It's
mainly
building
my
own
environment
for
working
and
the
environment
for
my
group
and
others,
and
I'm
responsible
for
JMP
software
at
Siltronic
for
more
than
200
users.
Data
analytics
at
Siltronic.
So
we
have
also
data
science
professionals,
and
they
are
providing
services
to
all,
and
if
we
need
as
engineers
some
reports,
they
are
mainly
static,
and
the
definition
of
new
reports
takes
some
time
and
is
not
that
flexible
as
we
would
need.
So
we
need
to
do
it
often
on
ourselves,
and
the
professionals
are
using
server
technologies
like
Cognos
Analytics,
Python
and
others
more
but
we
are
lucky
to
have
the
most
of
our
data
on
data
bases.
And
on
the
other
side,
JMP
is
the
standard statis
tics
tool
for
everyone.
Excel
is
used
additionally.
And
always
with
JMP
there
are
some
teething
problems
because
some
activation
energy
is
needed
to
make
let's
say,
new
stuff
or
so
working
with
JMP.
But
JMP
allows
full
scale
data
analytics
for
everyone.
It
is
like
data
acquisition,
manipulation,
data
exploration
and
visualization,
advanced
statistics,
modeling,
DOE,
and
others.
How
did
I
made
my
start
with
JMP?
So
I'm
working
at
Siltronic
since
2001,
and
as
far
as
I
remember,
I
have
always
been
looking
for
a
good
general
full-
scale
tool.
And
around
2009,
some
years
already
using
JMP,
I
was
attracted
by
the
nice
explorative
possibilities
of
Graph
Builder
in
JMP,
but
I
did
not
feel
comfortable
with
the
data
table
due
to
lack
of
understanding.
And
I
felt
a
complicated
data-
in
procedure
because
I
had
my
tools
in
Excel
dragging
data
from
database
and
I
had
to
throughput
it
to
JMP.
And
what
me
then
gave
really a
boost
is
that
I
understood
how
to
directly
import
data
from
database
into
JMP.
And
after
I
understood the
how
to,
I decided
to
use
JMP
as
my
standard
tool
for
data
analytics.
And
I
very
much
appreciate
the
ability
to
store
the
queries
in
the
JMP
data
table,
to
have
the
documentation
on
where
the
data
comes
from
and
the
ability
to
update
.
And
also
nice
is
that
JMP
saves
graph
and
other
evaluations
as
scripts,
and
that
I
use
a
lot.
My
first
vision,
I
was
alone.
So
I
did
only
see
me,
but
I
decided
to
become
an
expert
on
JMP.
I
liked
to
know
every
button
in
JMP,
but
this
isn't
possible,
I
learned
later,
and
I
did
not
see
the
others,
only
my
environment,
and
I
hadn't
yet the
idea
of
collaboration
internally.
External
collaboration
is
always
difficult
due
to
confidentiality
of
data.
And
I
a
lot
use
data
tables
with
query
like
shown
here
with
the
nice
scripts
here
to
update
data
from
database,
and
the
JMP
table
to
work
on
like
here
for
famous
big
class
data
table.
I
started
to
explore
the
features
of
JMP,
but
the
requirement
of
my
work
by
far
did
not
reflect
JMP's
full
range.
So
I
started
to
learn
in
the
community,
in
the
web,
and
also
to
explore
cases
of
colleagues,
just
by
interest.
And
later
I
saw
that
deployment
of
many
of
these
features
are
also
beneficial
to
my
work.
And
meanwhile,
I
like
a
lot
the
JMP
starter
window
that
shows
the
dynamic
range
of
the
software,
and
I
use
it
a
lot
for
training
to
show
what
is
possible
to
have
an
overview.
My
second
vision
was
when
I
recognized
the
others.
I
felt
that
I
could
support
others
also
in
using
advanced
data
analytics,
and
I
started
some
activities
like
JMP
Workshop.
It
was
one
show
for
all,
so
I
invited
all
interested
colleagues,
but
I
got
only
very
few
people
presenting.
It's
difficult
to
get
people
involved
into
that,
and
the
skill
level
was
very
different
to
make
the
show
efficient.
And
it
is
even
difficult
to
get
some
representative
data
on skill
levels
of
the
participants.
We
also
offered
some
special
support
one to
one,
and
this
worked
well
for
a
few
people
and
it
was
important
for
me
and
other
trainers
to
learn
what
the
requirements
are
of
the
colleagues,
what
they
really
need.
Additionally,
we
offer
basic
training
and
this
turned
out
to
be
the
most
important
and
effective
measure
to
get
also
into
contact
with
new
staff
and
other
people
and
so
on.
It
was
also
a
nice
story
how
to
get
others
involved
as
a
trainer.
So
we
tried
to
encourage
recently
hired
staff
because
they
are
eager
to
learn,
they
have
available
time,
resources,
and
good
communication
skills
and
this
was
quite
successful
to
encourage
these
people.
Last
but
not
least,
involvement
of
management
is
important
to
establish
a
visible
collaboration
and
to
justify
the
effort
that
is
put
into
this.
And
this
all
is
not
a
self-
seller,
there
is
a
driver
needed.
My
current
vision
is
more
on
establishing
a
network
and
making
like
a
snowball
effect
because
with
a
growing
number
of
users
it's
not
possible
anymore
by
one
person
to
address
all
the
people
using
JMP.
So
the
workload
has
to
be
distributed,
and
more
communication
lines
are
needed.
So
my
target
currently
is
to
make
everyone
knowing
a
JMP
expert,
and
to
offer
easy
access
to
JMP
knowledge
internally
without
the
before
mentioned
know-how
problem.
And
to
increase
usage
and
knowledge
of
JMP,
and
to
make
the
whole
story
visible,
including
management
and
included
into
the
procedures.
And
that's
why
we
built
up
a
communication
structure
like
shown
here.
And
on
the
top,
there
is
JMP
component o
wner
we
have
for
each
site.
And
the
component
owner
is
responsible
for
the
technical
things,
software
topics,
and
knowledge
training and so.
And
then
we
have
the
power
users
that
are
in
good
contact
with
each
other
and
with
the
component
owner.
And
these
are
that
people
that
should
be
known
by
every
user,
every
user
should
know
power
user
in
his
Department,
in
her
Department
that
she
or
he
can
reach
out
for
when
any
questions
occur.
And
other
current
measures
where
we
get
also
good
support
from
the
JMP
team.
So
thanks
to
JMP
is...
Yes,
beginners
training
I
already
mentioned
this
is
really
most
important,
and
also
most
easy
to
establish.
It's
a
network
for
free and
you
get
high
visibility.
And
we
also
included
STIPS
by
JMP
in our
training
program,
and
this
is
excellent
for
learning
statistics
and
JMP.
With
Martin
Demel
from
JMP,
we
installed
Jour-Fixe every
month,
and
there
we
have
very
good
discussions
and
more
and
more
people
get
encouraged
to
participate
into
this
meeting.
It's
very
well
working.
We
included
the
courses
in
our
internal
training
system.
We
also
installed
a
ToolBox.
This
is
a
JMP
script
that
collects
all
files
in
a
folder
structure
and
makes
data
and
analysis
accessible
by
all
users.
And
there
are
also
other
measures,
of
course,
depending
on
the
company
like
special
workshop
courses,
also
with
other
focus
like
SQL
database
language,
the
infrastructure
of
data
and
statistics
in
general.
My
summary
is
learning
and
implementing
JMP
in
a
company
takes
its
time.
It
does
not
come
for
free
and
it
needs
a
lot
of
personal
engagement.
But
it's
worth
doing.
It
will
enhance
data
analytics
skills
in
a
company.
You
need
management
support.
Last
but
not
least
to
pay
the
licenses,
but
also
for
other
things
like
make
it
better
visible
and
make
it
running.
And
all
the
solutions
of
course,
depend
on
the
company
and
on
the
people.
I
felt
it
was
a
good
idea
to
start
small
some
projects
and
to
see
how
it
developed.
It's
important
to
build
and
enhance
networks
on
this
and
to
evaluate
the
interactions,
what
is
happening
with
the
people
using
JMP
and
how
they
interact,
and
if
necessary,
to
rethink
the
strategy.
It's
worth
doing
and
it
will
pay
off
after
a
short
time
by
enhanced
evaluation
possibilities
and
better
decision.
And
last
not
least
you
can
see
it
in
the
ease
of
use
of
JMP
resulting
in
fun.
Okay.
Thank
you
for
listening.
I'm
finished
with
my
presentation
and
I
would
be
lucky
to
answer
questions
if
there
are
any
from
you
or
how
others
do
on
this
topic.
Thanks.
