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DUANE BONING: So I want to welcome everybody here
in Singapore-- or there in Singapore,
and here in Cambridge.
My name is Duane Boning.
I'll be one of the two lecturers for our class.
We'll get into some of those logistics
and details in a minute, but we've
got some preliminary things.
Since we've started videotaping you already,
one thing you need to know is there is a video recording
policy.
Pretty much everybody here will be videotaped.
If you don't want to be videotaped,
there are a couple of seats here on the right.
If you do want to be videotaped, you
can come and be accessible and viewable.
Right at the front, you can see there's these little red tabs
on the desk.
That's in the blind spot of the video.
I think some of these were being handed out
as you were coming in, but if you didn't get them,
here's another copy of that videotaping policy.
So what we do is most of these lectures are, of course, live,
video linked with Singapore.
There are occasions in the calendar
where it's a holiday in one place or the other.
When it's a holiday here, we don't meet.
When it's a whole holiday in Singapore, but not a holiday
here, we will still meet here in Cambridge,
but the video version of the local lecture
will be available on the website so that folks in Singapore
can catch up on that.
And in fact, we understand the first one of those
is this coming Thursday.
Is everybody ready for your holiday?
It's been a long, hard term already, right?
Is that right, Singapore?
OK.
Yeah, that's the Chinese New Year,
so there will be no class meeting in Singapore
on Thursday evening.
Let's see.
OK.
Has everybody found their way to the Stellar website already?
Anybody who has not seen the Stellar website for this class?
I'm asking in part because, if you are not actually
registered-- let me go to the Stellar site here--
oh, this may not be viewable in Singapore.
This is accessible only to those registered in the class,
I believe.
So if you're having trouble getting to the website,
please email me right away, and we will add you to the roster,
even before you get registered.
It will be important, because all
of the materials for the class are
deployed through the website.
In fact, it'll be very rare when we have any kind of handouts
to give in class.
Lecture notes, reading material, reading back-up, assignments,
weekly problem sets-- all of that material
is through the website.
Now, what I will typically do--
let me see if I can get back here--
usually I will try to have the PowerPoint slides loaded
a day before the lecture.
One thing that I noticed in a few past years is sometimes
little groups of students would work together
and delegate somebody to print out the lecture slides
and bring a set of copies for their little group.
So for example, the LFM students would often
have one person printing out that morning or the night
before, if they were available.
So you guys can self-organize.
If you actually like having any kind of print-out
to take notes on a copy of the PowerPoint slides,
feel free to print those out or self-organize
to share that duty.
Again, we're not going to be making copies available
directly in the class.
So what I want to do, first off, is talk a little bit
about the class and the assumed prerequisites for the class,
and then we'll come back and I want
to get us to know each other.
I know many of you have had some classes together.
Others of you are perhaps new to each other,
so want to share a little bit of experiences
so we know what the basin of experience is--
because this class is meant to be fairly interactive.
It is a manufacturing process control class,
and one of the best things about it is many of the students
in the class have an awful lot of experience.
For example, the students and the leaders
for manufacturing program, LFM, are typically
coming from several years of industrial experience.
Similarly, students have had a lot
of experience, whether it be doing experimental work
with processes in--
as part of their program, or what have you.
So it is meant to be interactive.
We depend on you sharing your experiences,
your insights, your doubts.
In many cases, those can be often the most interesting part
of the subject--
say, I tried that-- didn't work.
Here's why-- because a lot of the classes about tools
and experiences for dealing with manufacturing processes.
And then we'll get into a little bit more of the details,
some of the course schedule, and some of the other logistics.
Jumping ahead a little bit here, there's
kind of a long chain listed formally on the course
catalog for prerequisites--
says things like 2.008, or 2.810, or 6152J,
and then also a couple of other subjects like 6041 or 15064J.
What we intend to convey with these prerequisites is, number
one, they're not specific hard prerequisites
for those subjects.
What we mean to convey is you should have hopefully
some substantial experience with some manufacturing process.
So that's things like 2810, 6152J.
So 2810 is essentially the manufacturing physics subject--
manufacturing processes subject in course two.
I recognize some of you for that.
I think I gave a guest lecturer in that subject
on semiconductor manufacturing.
So that would be one example.
6152J is a course six, electrical engineering computer
science subject on microfabrication--
not only semiconductor, but also MEMS microfabrication.
And so that would be another example through coursework,
where you might have gotten that kind of experience.
And then similarly, you might have actual true
industrial experience.
So the goal here is actually to just go deep enough
or build on a deep enough knowledge of some process
technology.
This semester of the subject, I will actually
be using semiconductor process technology a little bit
more than we have some semesters in the past
to illustrate, and demonstrate, and to get
some practice with some of the concepts
that we're dealing with here.
But many of the problems will also
deal with macroscopic processes, from metal
bending to forming and other sorts of processes.
A little later, we expect Dave Hardt, who is the collector.
He'll be here.
And his background especially emphasizes
the panoply of mechanical and broad based process
technologies, whereas my experience is a little bit more
solidly in microfabrication--
both in semiconductor, and more recently,
in micro-electromechanical systems, or MEMS fabrication.
So that's one of the prerequisites.
Now, the other prerequisite that we kind of assume--
and if you don't have, you might be doing a little bit
of quick background reading--
is indicated by things like 6041 or 15064,
and that is some basic probability and statistics.
We'll actually be developing quite a bit of the statistical
machinery, so if you don't--
haven't had a full subject in that, not to worry--
I recognize many of you also from--
is it 2.853, 2.854?
Get that subject right--
that's Stan Gershwin's manufacturing process?
What is it?
Manufacturing systems-- manufacturing
systems, where we had a two or three-lecture sequence
on basic statistics and statistical modeling.
We'll actually revisit some of that,
so you'll see some of that again, but then
be applying that to problems of manufacturing.
I'll go over this detail, and then we'll
come back to get some introductions from you guys.
This semester we actually have two required textbooks.
The great thing is a new book has come out.
It came out nominally 2006, but it was only available
starting last year.
And that's a book by Gary May and Costas Spanos.
Gary May is at Georgia Tech.
Costas Spanos at UC Berkeley, and they had developed a course
on semiconductor process control that
was very close to a subject in the dim, dark past
that I used to teach dedicated entirely to semiconductor
process control.
So finally, we have a book for that.
It's a nice condensed book, and you all should try to get that,
because we're going to have readings
from this book starting pretty quickly.
So you might have to go to Amazon,
do two-day shipping or something like that.
If somebody has Amazon Prime, you
can use that and get free shipping.
I don't know if any of you do that.
The other book is Introduction to Statistical Quality Control.
This is more broad based.
May and Spanos illustrate everything the semiconductor
process technology.
Montgomery is basically a statistical process control,
a little bit of design of experiment.
It really emphasizes the statistical machinery
that we're going to be using.
So you are expected to either go and get
both of those-- both of these books
have been put on reserve in the MIT library,
so if you don't want to spend the money, you can, I guess,
do the painful thing of trying to go to the library
and read there.
At least the Montgomery book is a great book
to have on your bookshelf long term.
It'll be a great reference book throughout your career.
The semiconductor process control-- certainly,
if you're going in the semiconductor area, that's
also very useful.
OK, so the grading in the class--
the way this works is we will have--
I think it's something like eight weekly problem sets,
primarily in the first 2/3 of the class.
And then, in the last third of the class,
we stop the problem sets and you'll go into a team project.
The problem sets will comprise about 40%
of the grade in the class.
We'll have two quizzes--
one about a little bit more than a third of the way through,
another one a little bit more than 2/3 of the way through.
Those are about 40% of the grade in the class.
The good thing about having those quizzes during the term
is we do not have a final exam.
Anybody heartbroken about that?
[APPLAUSE]
OK.
However, the reason we don't is that,
in that last third quarter of the class,
we switched gears a little bit and ask
you to start applying some of the techniques and tools that
will have been talked about through most
of the semester in a team project.
I'll talk more about what that team
project is a little bit later.
So that's meant to be a fairly substantial effort.
The team project is going to be a team project.
It's going to be typically three-student teams.
And of course, that should be very collaborative in nature.
In contrast to that, the other assignments
are meant to be individual efforts.
Now, working on the problem set, I
think it's great to interact, talk with each other
to understand the problems, even to bounce off
possible lines of attack or solution approaches,
but where we mean--
especially on the assignments-- the weekly assignments,
the problems sets-- is everybody should do
their own independent write-up.
I find that writing and explaining not just
writing down the answer number, by the way.
We will expect actual articulation
of your thought process.
That should be an individual effort.
Now, we'll come back to that the assignments a little bit.
We'll try to emphasize this, perhaps, as well a little bit
more on the assignments themselves.
The perspective I'd kind of like you
to take on these weekly problem sets
is not that you're trying to come up with the answer number
like you might on an exam or something like that,
where you just needed the number, but instead,
a little bit of what I think of as a practicing manufacturing
perspective--
I couldn't call it an industrial perspective
or an academic perspective, but the key thing
that you have to do, and will have
to do throughout your career is convince.
So you have to convince us that the answer
that you're putting down makes sense, why it's reasonable.
So you need to articulate the thought process,
not just come up with answers.
And that's part of the reason why we really
want that to be an individual effort, an individual write-up.
And of course, everybody's already
gotten to the course URL.
So going backwards a step here, let me introduce--
I hoped that Dave would be here by now--
must be having a very difficult decision point
and in the voting booth there-- who to vote for.
Again, I'm Duane Boning.
I'm a professor of electrical engineering and computer
science.
Dave Hardt.
Will be here a little bit later.
Let me introduce for you the teaching assistant,
Hayden Taylor.
We do have microphones here.
Why don't you say a couple of words?
HAYDEN TAYLOR: [INAUDIBLE]
DUANE BONING: Yeah-- good.
There's the camera.
HAYDEN TAYLOR: Hello, Singapore.
I'm Hayden.
I'm in electrical engineering.
I work with Duane on manufacturing approaches
for microfluidic devices.
We work a lot with polymers.
And I'm excited to work with you all this time.
Feel free to email me at any time.
I'll be organizing office hours, and I'll
announce that on the website when the time has come.
For those of you in Singapore, I suppose
we'll have to have virtual office hours.
I'm very happy to talk over Skype or something
like that, if you want, so feel free to drop me an email.
Thanks.
DUANE BONING: And Hayden just got
back a little over a week ago-- is that right-- from Singapore.
HAYDEN TAYLOR: That's right.
DUANE BONING: [? We ?] spent a couple of weeks
there for some of the SMA program things.
The core secretary will be my assistant
over near my office in EECS headquarters in building 38.
And that's [? Charlene Blake. ?] So if you
have some administrative questions,
your best bet is probably emailing Hayden and me
directly.
I'll be the lead lecturer this term.
Normally, Dave Hardt is the lead lecturer.
He's actually nominally on sabbatical this term, but--
so I'll be taking care of more of the administrative things,
and he'll get to give fun lectures.
So if you have administrative things, please--
probably best to contact either me or Hayden directly.
If you have questions or need to drop off,
for example, late problem sets, that
can be done with [? Charlene. ?]
And we'll have to have to let [? Charlene ?] know
that she really outdid herself on the breakfast here.
So at any time, as soon as you're done with one,
we have lots more to eat, so feel
free to wander over and grab a little bit more.
OK, we'll dive back into a little bit more
about the course, but I thought it would be a little bit
fun to just quickly go around and hear a sentence or two
about--
and I'm particularly interested in what kind
of process background, or manufacturing background,
or technology background you have--
or if you don't have much background, what kind of areas
you're especially interested in, or potentially interested in.
So we'll do that quickly here on the Cambridge end first.
I know many of you, again, have been taking classes together,
so you know each other better than I you.
I recognize some faces here a little bit.
But this is part of my way to get to you as well as for you
guys to get to know each other.
OK, let me go through a few of the other logistics,
and then we'll start getting into a little bit
of introductory material.
I think we've already done some of that.
Let me come back to the team projects,
because I think it'll be important
for you folks to be starting to think very
early about possible projects and possible team formation,
because that will help-- as you're learning some
of the tools and techniques in these different kinds
of topical areas, you can pay more attention,
because you'll know, OK, I really
want to think about process diagnosis
for this particular project, or what have you.
So the typical kinds of topics--
generically, the topics in these team projects
involve things like process diagnosis;
process improvement, often with a little bit
of statistical process control, detection,
and debug kinds of things; process optimization
and robustness--
things like use of design of experiments
to characterize the process, and then
seek to optimize some outputs, as well as minimize
some variations and get to a robust process;
and then a number of more advanced applications,
perhaps dealing with things like yield modeling or defect
modeling--
these sorts of projects.
And the basic expectation in these team projects
is you'll need to learn a little bit
more background on the basic process
and what the problem is.
A really interesting aspect of these projects is it's best--
it really works nicely if we can tap
into a nice rich set of existing data,
or in some cases, even generation of new data
arising out of either your past experience
or your current research.
So for example, one of the, say, typical three-team members
might have access to--
hopefully only public, shareable-- nothing secret--
public data from their work experience.
Some of you may have generated a big set of data
for your master's thesis, or this or that,
and we're only able to look at this aspect of the problem,
because that was the key question,
but it was sort of nagging at you.
There's all this other rich data that you
would have thought it interesting to look
at from other perspectives--
things like building very simple response surface models for.
But you didn't really have the chance to go down that path.
So keep your mind open, thinking ahead to the project
as we go out-- go throughout the term,
and say, oh, that's an interesting topic.
That's an interesting technique.
That reminds me of a very interesting data
set that I came across back a year ago or whatever--
because that might form the basis for a very
interesting team project.
The output of this are two parts.
There will be a world presentation.
So the team will present--
in fact, the last two class periods,
the very last week of class, will be dedicated
to these team presentations.
And then there will also be a project report.
Unlike the problem sets, where everybody
writes their own separate problem set,
the team writes the project report.
So that's one document that comes together from the team.
Good-- so let me pop back.
We're not going to ask you who you voted for,
but we figured it was a tough decision, because it
was taking a little while.
We've already done all the introductions,
so now you, fortunately, know most everybody.
DAVID HARDT: I hope everybody knows that I
got dressed for the occasion.
What am I [INAUDIBLE]?
DUANE BONING: You have to wear a tie to vote in the US.
You may not know that.
DAVID HARDT: For those of you who don't already know me,
I'm David Hardt.
And I'm actually on sabbatical this semester, which
means I don't teach any classes and I
don't sit on any committees.
DUANE BONING: Why do you wear a tie if you're on sabbatical?
DAVID HARDT: So I'm here today to sit on a committee
and do some teaching.
Yeah.
But I welcome you all to probably the best
class at MIT--
and hello to all of our students there in Singapore as well.
I hope you're enjoying the weather.
It's beautiful here.
And the only reason I'm late is that I
had to drive the other way to get to the polling station,
and I got trapped by a huge traffic jam.
So I'm a dedicated voter.
DUANE BONING: Thank you, Dave.
OK, so let's get a little bit into the--
an overview of what we're going to be dealing
with throughout the semester, and then dive
in a little bit on some concepts and basic background
and terminology.
So some of the key ideas that we're after in this subject
is really dealing with basically the problems in manufacturing.
And the biggest problem of all is really
dealing with quality and manufacturing variation.
Other aspects of manufacturing are often
very particular to the specific process technology that's
arising, but there's a lot of very generic issues
with variation, and the control, and elimination,
and modeling of the process, and the variation,
and ways to reduce it.
So in some sense, dealing with variation
is one of the key themes in the subject.
And what we're going to start with in the first couple
of lectures is basically dive in, get a little bit
of process physics background.
On Thursday, we'll dive in a little bit more
on semiconductor fabrication, and then, on Tuesday, we'll
see a variety--
Dave will talk about a variety of forging and other process
examples that give a wider perspective on manufacturing,
and the physics that are at work, and where
variation naturally, inherently arises in those processes.
Then, well, once we've got a little bit of a feel for where
variation comes from, we want to dive
into some of the techniques for understanding that and dealing
with it.
And that really gets us into statistical models.
And we'll talk in great length about the contrast
between physical models of the process based
on detailed understanding of the specific mechanisms
at work versus empirical modeling of the process based
on data--
data and observations.
The wonderful thing about data is it's real
and it encompasses all of both the ideal behavior
and the non-ideal behavior.
And a lot of what we've dealing with, again,
is the problems, really trying to understand
where variation is coming from, what its characteristics are.
If we can start to get a model for some of that variation,
now we've got a handle that we can
use to try to eliminate it--
either eliminate it up front or control it
and compensate for it.
Some of the techniques that we deal with for dealing with,
again, that data is building effects models,
designed experiments.
You may have run across DOE, design of experiments.
It's a form of statistical technique,
and we will be learning the details of that
and applying some of those approaches
with basic input-output data sets and data models,
both in specifying the right set of experiments
to perform to very efficiently sample sample
our process, and then the ways to model that.
And perhaps one of the most important ways
of using those empirical models that we get of the process
is to improve the process, do process optimization, really
looking for improved operating points that have reduced
sensitivity to variation, that really improve the robustness,
as well as meet multiple objectives.
So we'll do a little bit of optimization-- multi-objective
optimization coverage as well.
So let's talk a little bit about, what are the--
oops-- what are the goals of manufacturing process control
and manufacturing processes?
What are some of the key characteristics you
would have of a good process?
What are the things you would like
to either minimize or optimize?
So I already gave you an advanced look at one.
I'll throw out one.
Of course, we're very often interested
in minimizing the cost, and cost rises
in a lot of different ways.
What are some of the ways that cost arise?
There's the inherent materials coming in.
What are some of the other costs associated with a process?
Anybody-- Singapore as well--
AUDIENCE: Labor.
DUANE BONING: Labor, yeah.
AUDIENCE: [INAUDIBLE] overhead, support.
DUANE BONING: Overhead, support.
AUDIENCE: Time.
DUANE BONING: Time.
Yeah, in fact time process--
time is certainly a characteristic
that we might want to optimize for.
That would be a typical characteristic.
Cost has many of these flavors.
Yes?
AUDIENCE: Setup costs--
DUANE BONING: Setup costs--
so setup costs are an interesting one.
In various times, we'll touch on some
of these operational issues.
Many of you have had [? 2853. ?] We said--
which was really about manufacturing systems
and things like scheduling, throughput--
those kinds of issues.
And so time is a little bit of that as well--
not just the inherent manufacturing,
physical action on the product time,
but also transport time-- all of these kinds of other issues.
This subject will not be dealing so much with logistics.
It'll really be much closer to the things
that impact directly the product itself.
But certainly, setup time can have an interaction--
processes setup in general can have a strong impact
on processed quality.
Very often, for example, in semiconductor processing--
maybe this will resonate with your experiences
in other processes--
if we change substantially the process setup
in a particular piece of equipment,
the first few wafers will behave very differently
before the equipment gets to a stable state.
Sometimes we even run dummy wafers
in order to equilibrate that setup.
So setup is a good one.
Some more-- what are other things?
I'm sorry--
AUDIENCE: [INAUDIBLE]
DUANE BONING: [INAUDIBLE].
Certainly.
AUDIENCE: Equipment.
DUANE BONING: Equipment, yep.
We want to use the equipment as efficiently as possible,
and maintain it.
AUDIENCE: [INAUDIBLE] a good machine or a bad machine--
DUANE BONING: Good machines and bad machines-- absolutely.
And in fact, probably the first third
of the class, the classic statistical process control,
will be all about detecting whether the equipment is
behaving as it should.
Is it in a good state or in a bad state?
AUDIENCE: [INAUDIBLE]
DUANE BONING: So here we had quality control--
AUDIENCE: And rework.
DUANE BONING: --and rework, yeah.
AUDIENCE: Energy.
DUANE BONING: Energy, yeah.
Yes, that's a good point.
Actually, I think, dealing with energy, as well
as the consumption of materials and the output of materials,
is becoming a more and more interesting
aspect of manufacturing processes.
Let's see.
When was it?
Sunday night-- just to share another anecdote--
I was serving on a panel at a big semiconductor conference.
Actually, it's the semiconductor--
biggest circuit design conference
in the semiconductor industry.
And I was on a panel all about environmental or green
manufacturing.
And so the reduction of energy usage
is becoming a very big deal, as well as the output of things--
in semiconductor side, output of global warming gases and so
on-- the reduction of those.
So that's a good point.
By the way, I was on a panel at 7:30 on Sunday evening.
People here-- what happened on Sunday?
That was Super Bowl here.
I was not very happy to have to attend the panelist
dinner the second half of the Super Bowl,
but as it turned out, I'm very glad I missed it.
OK, so we've hit some of these other ideas here.
Here's one that nobody mentioned.
Flexibility is also very key in manufacturing processes
as well.
We're increasingly finding that the same manufacturing
line needs to be adaptable rapidly
to be able to deal with a bigger product mix than ever before.
So some of the focus in this subject,
again, is going to really be on the processes, and variation,
and quality in the processes.
A little bit bigger emphasis is going to be on unit processes.
The entire aggregation of the overall sequence of product
to make the overall--
or sequence of processes, aggregation of the unit
processes to make the entire process line, and process flow,
and the final product is important.
We'll touch on some of these kinds of things of stack up
of quality across multiple processes,
but we'll be-- end up emphasizing developing
techniques like statistical process control, design
of experiments and optimization, with a little bit more
of an emphasis on the unit operations.
Part of the reason is more and more for really high-quality
manufacturing--
the recognition is you can't simply
inspect or hope to control at the end of the overall process.
You have to have every unit process along the way running
as effectively and at the highest quality possible.
So again, the number one emphasis
here is going to be dealing with maximizing
quality, conformance to specifications, and so on.
And some of these other things that we've already
talked about here--
things like improving throughput, improving
flexibility, reducing cost--
those are going to be secondary.
There will be interactions with some of these topics
that you have run into or might run
into another in other subjects.
And actually, a very interesting area of research--
in fact, an active area of research of Stan Gershwin,
who some of you had in 2853--
is this interaction between quality and quantity--
that is, typical operations and how
you manage the sequencing of parts through the line--
the interaction between throughput and quality--
for example, dealing with issues of setup,
dealing with issues of slight degradations in quality
and how that might impact things like scheduled maintenance
or unscheduled maintenance.
So it's a very interesting topic.
We'll occasionally, again, overlap slightly with that,
but our number one emphasis here is dealing with the process,
understanding and modeling the process,
and dealing with quality and variation.
So here's a few typical process control problems.
I'll share a couple of these.
And just to wake up Dave, on the next slide,
we'll let him say a few words about some
of the other examples.
So here's a typical example.
Probably, in fact, the number one problem--
or number one challenge in semiconductor manufacturing
is the minimum feature size is very, very
small-- becoming smaller with each technology generation.
The minimum feature size is typically
the channel length of an individual MOS transistor.
That's often the most critical parameter, and it varies.
With current technology, we're down
in heavy-duty manufacturing at 90 nanometers.
Some of the leading edge manufacturers
have gate or channel patterned lengths of 65 nanometer.
Occasionally, you'll hear those terms thrown out.
What that means is the minimum feature size being patterned
is about 65 nanometers wide.
That's small.
That's tiny.
And as we continue to scale, that variability
in the width of that is becoming tougher and tougher to achieve.
So very often, there will be challenges on the manufacturing
line to detect--
monitor, detect, and then compensate
for those kinds of variability.
An emerging technology-- here's an interesting one
that connects up with, for example,
some of the Singapore MIT Alliance research efforts.
A DNA diagnostic chip might have uneven flow channels.
So for example, here, these are often microfluidic devices
manufactured through embossing processes, where
one might be using a polymer and trying
to create channels that might be 10 microns wide, 10
microns deep--
and maybe, in some places, very, very narrow, for example,
or very, very shallow--
maybe 20 to 40 nanometers deep.
I mention those because those are actually
real numbers for some of the researchships
that are being developed here at MIT in electrical engineering,
mechanical engineering, and elsewhere-- very interesting
properties, when one gets down to that small dimension.
And some of those properties depend
critically on those dimensions, as you can imagine.
So in fact, the manufacturing process problem
is, how do you get processes that
work in a controllable fashion?
How do you inspect and detect feature sizes,
geometric parameters, roughness of the geometry,
as well as some other material properties in order
to achieve the optimal functionality
that one might need--
for example, controlling the flow of individual DNA from one
place to another through these incredibly tiny, tiny channels?
What's another example?
Somebody mentioned assembly.
Assembly is a manufacturing process,
and the worst example of this is that toys that come
disassembled-- they never fit, at least when
I try to assemble them at home.
Geometric fit and how those imperfections can build up,
even if--
an interesting aspect-- even if a few parts
are close to nominal when you try to put them together,
sometimes the aggregate doesn't work out so well.
And then there may be other example--
for example, very high-density electrical connectors.
And I think, Dave, you had some example--
this comes out of an LFM thesis, or?
DAVID HARDT: No, this was actually
research we did [INAUDIBLE] a number of years ago.
And it's actually just like--
it's actually a trend with all these
that [INAUDIBLE] for years.
Of course, [INAUDIBLE].
DUANE BONING: Can you guys in Singapore hear Dave?
No?
AUDIENCE: No, we cannot hear.
AUDIENCE: [INAUDIBLE]
DAVID HARDT: [INAUDIBLE] mic's not working.
Just because this is sort of a theme--
you can imagine electrical connectors-- big heavy ones,
like used in cars or in old electronics back in the tube
days, where you could grab hold of some of these connectors
with your hand--
just the metal part itself.
Now, as electronics has become more and more dense,
as things have become smaller and smaller,
the requirement for higher and higher density of connectors--
if not just to handle these high-density chips,
but just look at the back of your computer-- some of those
connectors there-- particularly the interconnect type
of connectors.
Well, the industry that made that
made it the same way for years, and it worked fine.
And it's a great example of how variability is actually
a relative term.
And so the variability was relatively small.
It was small relative to the characteristic dimension
that you needed.
But as that shrunk down, these major manufacturers
found out that they really couldn't
use their old technology to meet this.
Of course, they went through a lot of the procedures
we're going to go through and they improved things,
but they couldn't meet those dimensions,
and had to make a technology change as well.
DUANE BONING: This is my sneaky way
to get an extra [INAUDIBLE].
DAVID HARDT: Also, on this minimum feature size--
and I'd ask Duane to comment on this--
I was once with a gentleman from IBM Research
who told me they were just now-- this was maybe
five, six years ago-- just now getting
into some of the advanced techniques
that we're going to talk about near the end of the term here.
And I was a little bit surprised,
but then he pointed out, back when the feature
size on the chips was large--
I don't know what large was-- micron scale or several
microns--
the inherent variability of the processes was fine--
fine to manufacture these things.
But when you get down to the nanometer range,
all of a sudden, you have to look at some other techniques.
So there are these timeless things,
like toys that don't fit together
and other things related to larger dimensions,
but then this idea of the ever-shrinking mechanical
dimensions of things leads to an ever-increasing importance
of process control.
So our future is guaranteed.
DUANE BONING: I'll skip ahead, because maybe [? not ?]
going through all of these, but if there's another one or two
of these from your experience that you wanted [INAUDIBLE]..
DAVID HARDT: Well, these are three large-scale issues.
This is usually where I ask if anybody here
works for Boeing or any other airframe manufacturer.
DUANE BONING: We have some automotive.
We have some [INAUDIBLE].
DAVID HARDT: Yeah.
The automotive actually does a lot better
on variation control, because they have--
as you'll see, it helps to have some volume
and to be able to learn and to get your processes
in steady state.
Things like airframes are still sort of one-off.
And because of the large dimension there,
it's really not an unpleasant-- it's not a pleasant thing
to watch two halves of an airplane being put together--
although they've gotten a lot better than they used to be.
What was it they used to say-- that the number of thousands
of pounds [INAUDIBLE] flying in a 747?
[INAUDIBLE] we're also going to talk later--
actually, I'm not sure we will this term,
but there's an interesting LFM thesis
done on these plastic throttle bodies for fuel injection.
This is a little bit different issue.
This was an entirely different way of making a classical part.
It had been made for years out of aluminum--
machined aluminum-- well-defined process,
well-understood process control.
Then, for good and sufficient reasons--
mainly weight and production economy--
they went to injection molded parts,
and variation became a big problem.
And how to get rid of it was a-- was and still
is an interesting issue.
So a number of things come up, often because of change--
not always just because of things being in stasis.
DUANE BONING: Thank you.
So again, some of these points that Dave
made about, for example, the question or example you posed
on semiconductor manufacturing we'll talk a little bit more
about on Thursday, because it was
a very interesting transformation
from defect-oriented problems as the real yield and quality
limiter to dimensional control.
And essentially, parametric variation is the killer now.
OK, so one of the things--
this connects up again also to a point that Dave just made--
is that there is an evolution of manufacturing process control
many industries had gone through.
And you will often see a trajectory
where an industry may actually start with a process that
is not all that stable, and they essentially
have to inspect every single part that comes out--
100% inspection may have relatively high scrap rates--
even a few percent, for example--
with low throughput, and correspondingly high cost.
So this might be thought to be a characteristic
of immature processes, but it's also
a characteristic of incredibly complex processes.
So you would really like to avoid that.
Quick question for-- well, some of the guys from semiconductor
know this.
Do we have 100% inspection of semiconductor chips today?
What do you think?
Do you think every single chip that
comes off the line gets inspected, measured,
verified that it produces?
Do we have high costs or--
somebody who knows the answer--
well, I think perhaps it depends on what the chip is.
You think every single microprocessor
chip gets inspected before it gets packaged and put in a PC?
You bet.
On the other hand--
AUDIENCE: [INAUDIBLE]
DUANE BONING: Oh, absolutely.
A microprocessor chip might sell for a couple hundred dollars,
so it's worth it.
Now, if you're talking about a semiconductor chip that
is a discrete diode worth probably
a fraction of a penny in terms of the manufacturing
cost, yeah, you--
it's a much more simple and less complex thing,
and you probably don't need 100% inspection there.
But most integrated circuits, at the end of the day,
still need inspection at the end.
Yeah, Hayden?
HAYDEN TAYLOR: I thought it might be worth
remarking that I think--
DUANE BONING: Speak loudly.
AUDIENCE: Can everyone hear me in Singapore?
No.
DUANE BONING: Just shout.
HAYDEN TAYLOR: OK.
I think Intel, when they test their chips,
they determine what the maximum operating
frequency of any given chip is, and they sort them
so the chips that happen to operate at the faster end
are put in more expensive machines,
and those that are slower go in the cheaper machines.
So they actually get some extra benefit
from the testing process.
DUANE BONING: Right.
So based on the quality or the performance,
they can bin the chips and get differential pricing.
Of course, they would be most beneficial if all of the chips
operated at the high end.
And so in some sense, that's both a limitation
and an advantage.
You can get a premium for the highest performing.
But it's a great example of the range of variation
and the effective, ultimately, on the end performance.
A next step typically in some of the evolution
of these manufacturing processes involve--
whoops-- involve rework, both at the end of the line,
but more often pushing down to unit processes--
where, if you could do a inspection,
you still have limited control perhaps
over the individual unit process,
but you can inspect it, determine something went wrong.
And if there is a possibility of fixing it then and there,
now you've got a big advantage.
We already talked about also high durability at changeover,
and that's an example we already talked about,
about how cost rate and flexibility are linked.
Of course, all of these-- if you can get quality up,
get to higher and higher yield--
that has a dramatic effect, perhaps the strongest effect
on overall throughput and the optimal output
from your manufacturing plant--
especially in those industries where yields may not
be all that high.
For example, in many of the semiconductor processes,
if you have 90% yield on a very complex microprocessor,
you're doing pretty well.
That's not great, is it?
Can you imagine 90% output of--
90% functioning output of syringe needles
that we saw, where 10% of them were defective in some way?
That wouldn't be very satisfying, I think.
OK, so one of the things that we're
going to do a little bit in the next couple of lectures--
and I'm going to talk about a little bit generically
to set the stage for that last 10 minutes or so of class--
is characteristics of manufacturing processes--
in fact, something of a taxonomy that Dave
will cover on Thursday based on some
of the inherent characteristics of the physical action
of manufacturing processes.
And we've already chatted a little bit,
and we'll see more as we dive into those processes
about why they don't always work correctly.
So here I'm going to try to get a little bit of the terminology
out and give you a little bit of a framework for thinking
about the processes, and then some reflection
on the different segments of the subject
that we mentioned are coming up, and how
they relate to a very-high level generic view of the process.
So the key idea in a process, of course,
is that we have some part that we want to produce at the end.
We do that by working on a work piece.
There is some physical material that we are transforming
in some important way.
We are changing the geometry.
We are changing the material properties of that part.
How do we do that?
Well, we have to subjected to some process,
and we do that through equipment, and in some cases,
parts of the equipment-- things like
tooling that come in contact with the part.
So some examples of broadly, equipment-- and here,
equipment we may even rise a little bit
as both the physical machinery and the process environment
that that machinery generates around the work piece.
So examples might be an an etch bath.
So that is a liquid chemical environment
that might be done under the control
of a piece of equipment.
Other equipment that many of you may have come in contact with--
things like injection molders, lathes, drop presses--
and the whole goal of those are to act on some workpiece.
So we may have silicon with certain layers already coded
on them.
We may have feedstock, things like plastic pellets
or [INAUDIBLE], that come in.
We may have sheet metal.
Sheet metal already may have gone
through an awful lot of processing,
and we're looking at a unit step that's
doing some additional processing on that.
And then the output can either be a finished part
direct for use or a part that is in some intermediate stage that
will, again, go and be used in some larger manufacturing
process.
So it might be a shaft or a hood or something
like that that's going to be further assembled,
or an IC chip--
which, from the semiconductor fab,
that's the finished product.
But from somebody-- can't remember who said they worked
at Dell--
the IC chip-- that's not a finished product.
The PC and the assembly--
the packaging and assembly onto boards and the assembly
of boards into the [? back plane ?] and so on
are an important manufacturing process.
So by definition, we're thinking of a manufacturing process
as a change or the sequence of changes in some work piece
material.
The easiest way to conceptualize this
is that it's going to be a change in geometry.
We're building up some part.
But equally important is a change
in some material properties, some constitutive properties
of that structure.
We'll see lots of examples of that
in semiconductor fabrication.
Here I want to give you a kind of a generic conceptual
semiconductor process model.
And actually, I want to mention a little bit--
this ties into the history of the evolution of this subject.
Back-- oh, God-- it's getting to be 18 years ago already--
when we were starting to look at ways to formalize discussion
of semiconductor processing, we came up with--
myself and some other co-workers here at MIT
came up with some terminology for this conceptual
semiconductor process model.
And we talked about it in terms of states
and transformations of states, these states
being the geometric state and the constitutive properties
of the work piece In semiconductor fabrication,
that's typically the wafer.
And around that, one is generating
a process or some environment around the wafer.
That may be gases, temperature environment,
other kinds of ways of generating and directing
energy and material at the surface of the wafer.
The machine itself is a key controlling parameter for how
one generates that environment, and the rest of the facility
also impacts that environment-- the feed material coming in,
the very, very high purity chemicals, for example.
Now, what's important about the machine
is that the operator only has limited access.
The operator, which may be human or may be an automation system,
has only limited access to that equipment and the facility
through some settings-- some knob settings, if you will,
that you can perform on the equipment.
But you also have other directional sensing capability.
You have some number of readings that are telling you indirectly
things about the machine--
machine states, sensor states, thermocouple states,
which are telling you things about the wafer environment--
and then, indirectly-- if you're lucky, directly
things about the state of the work
piece itself, telling you things like the thickness of the film
being grown on the wafer.
And then we had a term, which is really kind
generic to semiconductor processing, of a recipe that
basically defined the settings, and perhaps even could
be generalized to a control algorithm,
if you will-- which might be a little bit more
of a generic terminology-- for dealing
with responses to the readings.
So now you can think about things like real-time control--
or run by run control, which we'll talk about later--
that looks at how one determines what the settings should
be on the equipment.
Now, we came up with a slightly simplified version
of the semiconductor process model just focused
on the wafer, the process, and the settings.
And I want to give you a real quick example of this.
Actually, I'll probably skip this and come back
to this next time.
This is looking at examples for one particular process dealing
with oxidation.
Now, what was interesting is we came up
with this generic process model.
This was early '90s.
When I came back to MIT, I started teaching
a subject on semiconductor process
control dealing with statistical process modeling,
yield, design of experiments, and so on.
And then I got involved in some research
that overlapped with Dave Hardt and some others.
And he had a course, and--
on manufacturing process control,
and he had a process model for control circa 1995.
Maybe you had come up with it even earlier.
And one of the readings that you need to grab off of the website
is an overview of manufacturing processes that
is circa 1995, '96, where the terminology is almost
exactly the same.
And in fact, the overlap was astonishing.
The tools and techniques that I was teaching
in my separate subject and Dave was teaching in his subject
was about 80% the same, and then just the process details
kind of changed.
So in fact, we merged the subject,
and that's how this subject came about.
And again, the key ideas here are there are controls.
There is the operator, and some settings,
and the equipment, material--
that we're all about changing the geometry and property
through the action of the process environment,
or these energy states.
Now, in this class, we're really going
to be focused a lot on the overall process
outputs [INAUDIBLE] processes, and in some cases,
the aggregation of those.
And just to get a little bit of terminology
as a prelude for both Thursday and next Tuesday,
one can think about a vector y of characteristics
of the product that are important--
shape parameters, particular thicknesses,
perhaps material properties-- like index of refraction
of thin films--
the conductivity of particular layers, as well as transistor
characteristics, for example.
And that is a function of the process parameters,
both controls that one can change
and other parameters that may be fixed and set for the process.
So a key question is, what are these alphas?
What are these process parameters?
And how do we go about controlling those?
So we'll touch on this picture a little bit more next time,
but what I want to skip to here is a--
we'll hear about these different characterizations or taxonomy
of the process next time.
I want to give you one last description
building on this very simplified mathematical terminology
at least for the process, which is we can go in and--
here we go.
Here we go.
We can split out some of those process parameters
as those things that we have control over
as inputs to the process.
And now, if we do just a very, very simple first-order Taylor
expansion, we get some nice insight
into where variation comes in a typical process.
So this is the very simple variation equation
that, in some sense, helps structure
what we're going to be doing through the rest of the term.
One can think about deviations in the output
deltas in those characteristics, and we
can expand that in terms of some of the alpha parameters
and some of the controllable parameters.
So this dy d alpha-- that's sensitivity to delta a alphas.
What's delta alphas?
Those are disturbances that you would prefer were not there.
They're not things that you intentionally are changing.
Those are inherent disturbances in the process.
So there's one component that deals with disturbances.
And then there's also intentional changes
we might have, when we want to or intentionally make a change
to the control inputs.
And so what's interesting is the strategies that we might use
for minimizing this delta y.
And in some sense--
let's see.
Let me get to a nice summary here.
Here we go.
In some sense, the goal of the first third of the class--
the techniques we'll be talking about our statistical process
control, which is all about detecting
these inherent deviations, these disturbances,
and seeking to minimize them.
Then we can talk about process optimization,
things like design of experiments,
where we're basically trying to build a model for y--
the output-- as a function of process
parameters, and seek to minimize the sensitivity
to those disturbances, to have as robust a process as
possible.
And then finally, we can also think
about active modes of process control, where you actually
manipulate some of the control parameters,
perhaps in response to observed deviations,
to ultimately minimize or counteract,
through feedback control, the effects of those disturbances.
So that in the nutshell is the map
for what we're going to be doing through the rest of the term.
We're going to be dealing, first off, with statistical process
control and the statistical background for that.
We're then going to be modeling the process,
building up design of experiments, response surface
modeling technologies for modeling
and optimizing the process, and then
thirdly, we'll talk about some basic strategies
for feedback control to compensate
for some of these processes.
So with that, we'll leave you.
Folks in Singapore, we'll see you again next week.
Again, do catch the videotape of Thursday's lecture.
Please go to the website.
This lecture is on there, but also there are two--
well, there's one reading that you should grab
right away, which is about a--
was is it-- six or seven pages process overview
which defines some of this terminology in written form
that we've talked about here.
And I will also post what the reading assignment
is that you can get started on.
I don't think it's posted up there yet,
but go out and try to get these books,
because there will be-- the reading assignment is basically
start on the first two chapters, which are very talkative
overview of manufacturing processes.
So we'll get you started on those.
One quick question, Dave--
is there any information on--
is there a pro seminar when that starts up,
that you want to mention?
DAVID HARDT: Yeah.
2.888 is already-- there's a Stellar site on it.
I think I've sent most of the preregistered students notes
on this, but it starts a week [INAUDIBLE]..
DUANE BONING: OK.
So we'll have more information on that.
Many of you, like those enrolled in the mechanical engineering,
MEng program, will know about this already,
but this series of seminars from folks from industry and so on
will quite likely be of interest to other people in the class.
So we'll start alerting you about those evening seminars
here as well.
So welcome to the class, and we'll see you on Thursday.