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.