FabTime Cycle Time Management
Newsletter Abstracts - Volume 2 (10
Issues)
Our article this month is a continuation
of last month’s discussion on
including cycle time in the capacity
planning process. Last month we talked
about how people do this implicitly, though
the use of capacity loading factors. This
month, we talk about a more explicit method
of including cycle time in the capacity
planning process, through the use of
simulation models. This article is based on
a project that we did for Seagate
Technology several years ago. The method
involves using simulation to estimate the
cycle time of candidate models, and adding
tools on the basis of greatest cycle time
reduction per dollar of fixed cost. The
main point from this study is that other
factors besides equipment loading have an
influence on the cycle time contribution of
individual toolgroups. Considering those
other factors can allow you to plan for
more cost-effective toolsets. Navi Grewal,
one of the original authors, collaborated
with us on this article.
Discussion topics in this issue include: several responses to the 300mm lot size question; a proposal for calculating the cost of cycle time; a statement of the continued need for moves as a daily fab performance metric; a modification to the cycle time calculations in the characteristic curve generator; a case study comparing actual performance to short-term goals; and questions about the implications of 300mm factory size, relating OEE to cost per wafer, modeling operator impact, modeling cycle time and WIP during a volume ramp, the industry definition of "loading", calculation of product and factory line yield values, and benchmarking cycle time for wafer production.
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Cycle time is always considered in the
capacity planning process for wafer fabs.
In most cases, however, cycle time is
considered implicitly, rather than
explicitly. If your capacity planning team
was not considering cycle time, they would
plan for the minimum toolset to meet
throughput requirements, with perhaps some
additional tools to account for potential
product mix changes. Instead, they include
planned idle time for essentially all tool
groups. They also try to avoid
one-of-a-kind tools, frequently
recommending duplicates of even very
lightly loaded tools. In this article, we
will talk about these traditional methods
of implicitly accounting for cycle time in
the capacity planning process. Next month
we will look at ways to be more explicit,
and shoot for specific cycle time targets.
Discussion topics in this issue include: a question about the standard for 300mm lot size; a question about quantifying cost savings from cycle time reduction; an inquiry about the availability of published productivity report indices for fabs; a request for references on literature regarding new product introductions; and a practical best-case X-factor for cycle time goals taking human performance into account.
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We are surrounded by performance
measures. Goals help us to convert these
absolute numbers into relative “good
or bad” indicators. At higher levels
of an organization, you deal with
aggregated goals. More detailed goals must
be set, however, at lower levels of the
organization. These detailed goals must be
consistent with the higher-level goals, and
must be useful for day-to-day operations.
The closer you look at the process, the
more you see the proliferation of goals. If
you can address this proliferation, you can
generate appropriate goals for a wide
variety of intermediate performance
measures. It’s important to remember
the implicit assumptions behind long-term
goals, however, and to mix long-term goals
with appropriate short-term targets.
Discussion topics in this issue include: a question about generating operating curves for the wafer test area; a description of experiences in measuring process time variability; and a request for the logic behind the variability parameters in the FabTime characteristic curve generator.
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The FabTime Cycle Time Characteristic
Curve Generator is an Excel-based tool for
exploring cycle time and utilization
trade-offs for single tools with failures.
You can enter parameters for process time,
mean time between failures, downtime
percentage, and system coefficients of
variation for up to three scenarios. The
calculator then displays characteristic
curves for the scenarios, allowing you to
get a quick visual impression of the impact
of both downtime and variability
attributes. The curves are based on a
queueing approximation that we received
several years ago from Ottmar Gihr of IBM
Germany. You can download the
characteristic curve generator from
FabTime’s website (here).
Discussion topics in this issue include: the method for ordering the SEMI E-79 Standard document; a description of where to find abstracts to INFORMS articles; a request for fab cycle time benchmark data; and a request for tool cycle time benchmark data.
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The article was written by Frank Chance,
with assistance from Stuart Carr
(consultant and FabTime affiliate), and Ken
Beller. Frank started thinking about this
question because, as President of a cycle
time management software company, he is
frequently asked about the dollar benefit
of cycle time reduction. This article
outlines several potential ways to quantify
this benefit, and focuses in particular on
the timely issue of inventory write-off
during an industry downturn. The article
references an Excel-based cycle time
payback calculator that was formerly
available from FabTime’s website. The
calculator has since been replaced by a
more comprehensive calculator described in
Issue 3.5.
Discussion topics in this issue include: the SEMI E-79 Standard definition of ideal process time; and a clarification of the OEE calculations for quality rate.
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This issue contains the abstracts to all
previous issues. It also contains
additional discussion on OEE, and several
industry announcements. (
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Most of our readers are familiar with
the general concept of Overall Equipment
Efficiency (OEE). OEE is a tool-level
measure reflecting how much good product
the tool produced relative to some
theoretical amount that it could have
produced. Typical OEE values in a wafer fab
are less than 50%. Given the high cost of
equipment, there is a clear incentive to
make OEEs as high as possible. OEE is the
measurement that’s used in TPM (Total
Productive Maintenance), a methodology for
improving the entire manufacturing
process.
In this article, we review the formulas
for calculating OEE (both the full formula
and a short-cut version), as well as some
of the reasons for low OEE in wafer fabs.
We also include a series of links to OEE
resources on the Internet (including
primary resources from SEMI and SEMATECH),
as well as some additional published OEE
references.
The power of OEE is that it provides a
clearly defined metric by which equipment
performance improvement projects can be
measured. SEMI and SEMATECH have gone to
great lengths to define OEE, and also the
necessary supporting metrics like the SEMI
E-10 equipment states. The nice thing about
this is that it means that you can compare
OEE values across factories, and even
across companies, and get a true picture of
your factory’s performance. Another
nice thing about OEE is that it drives you
to do good things, like reduce setup and
rework and scrap and starvations due to WIP
or operator shortages. By focusing on the
six types of losses highlighted by OEE, you
can design a strong equipment improvement
program, and monitor your progress through
trends in the overall metric.
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Downturns are a fact of life in the
cyclic semiconductor industry. Various
factors contribute to their existence -
capacity buildup (and the long lead-time
required in capacity purchases), decline in
selling prices, inventory build-up, and the
general state of the economy. This one
seems to have been triggered mainly by the
last two factors, but explanations and
predictions also seem to change every
day.
The quickest way to reduce cycle time in
a wafer fab is to significantly decrease
start rates. This moves your factory to the
left on the cycle time vs. factory loading
curve, to a region of lower cycle times.
The irony is that just when customers
aren’t clamoring for product, your
fab can delivery product with record cycle
time and on-time-delivery performance.
It’s very easy under these conditions
to get a bit sloppy, and to take the lower
cycle times for granted. But then when
start rates begin to increase, when
customers are paying attention again, your
cycle times will degrade rapidly. If you
don’t have great cycle times now, you
certainly won’t have great cycle
times when start rates go back up.
Therefore, we suggest using this time to
focus on low cost cycle time improvement
efforts, including setup/dedication policy
investigation, process analysis, layout
analysis, bottleneck analysis, OEE/TPM
analysis, simulation model validation,
system upgrades, and education.
A downturn is a tough time - stressful,
hard on your stock portfolio, and filled
with the specter of layoffs. But it does
offer at least one potential benefit: time
to think. Time to think about manufacturing
issues like lot size and batch size
policies. Time to think about tool
dedication schemes, and layout changes.
Time to get your fab in order, and drive
your cycle times to a minimum, before the
next upturn comes along.
Discussion topics in this issue include: a success story on cycle time reduction through batch size decision rule changes; and a clarification of the units in the P-K formula.
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This article concerns possible changes
to production lot sizes for cycle time
improvement. For fabs running 50 wafer
lots, changing to 24 or 25 wafer lots
offers a potential cycle time reduction
opportunity. However, there can be
tremendous resistance to this idea, and
there are a number of potential pitfalls.
In this article, we first review the
reasons for the cycle time reduction
opportunity, and then discuss some of the
pitfalls.
The justification of lot size reduction
for cycle time reduction comes into play
primarily due to time savings at per-wafer
tools, which can include critical tools
such as steppers and implanters. In
addition to providing these direct cycle
time benefits, smaller lot sizes also make
a fab more flexible, more adaptive in the
event of problems, and can reduce
variability. However, there are a number of
issues to consider before changing the lot
size, any one of which might keep a lot
size reduction from being worthwhile, or
even render it detrimental. These include
capacity, material handling, MES, and
dispatching/complexity issues, and are
discussed in detail in the full
article.
We have no black-and-white
recommendation to make concerning lot sizes
and cycle time. Smaller lot sizes may
reduce cycle time, and make a fab more
flexible. However, reducing the lot size
can cause problems with material handling,
capacity, MES performance, and fab
complexity, particularly during the
transition period. We suggest then, that
you consider lot size reduction to reduce
cycle times, but that you consider it very
carefully.
Discussion topics in this issue include: observations about time constraints and batch size decisions, and sequence dependent setups and batch size decisions; and a question about defining utilization at batch tools.
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Batch tools are tools in which more than
one lot may be processed at one time. They
are generally used for very long
operations, such as furnace bake
operations. Processing time is usually
independent of the number of lots in a
batch, and once a batch process begins, it
cannot be interrupted to allow other lots
to join. From a local perspective, when a
furnace is available and full loads are
waiting, the decision to process a batch is
obvious, since no advantage can be gained
at that work area by waiting (although a
decision may still be needed concerning
which product type to process). However,
when there is a furnace available and only
partial loads of products are waiting, a
decision must be made to either start a
(partial) batch or wait for more products
to arrive.
There are two problems with running a
partial batch. One is that the unused
capacity of the furnace will be
“wasted.” The other problem is
that lots that arrive immediately after the
batch starts cannot be added to the batch,
and might have to wait many hours until
another furnace is available. There are
also problems that stem from waiting to
form a full batch. The lots that are
waiting to be processed incur extra queue
time while waiting for other lots to
arrive. The furnace is held idle, driving
down its efficiency. And full batches
contribute more to variability after the
furnace operation.
This article discusses policies for
deciding when to form a partial batch,
using simple numerical examples and
simulation results. We conclude that for
batch tools that are not highly loaded,
forcing full or near-full batches can
significantly increase local cycle times,
as well as overall fab cycle times. (
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