|
Other Abstracts
|
 |
|
FabTime Cycle Time Management
Newsletter Abstracts - Volume 4 (11 Issues)
This month’s main article is about
the cycle time effect of changing factory size for semiconductor
wafer fabs. Everyone knows that for a given fab, as start rates
increase (as they seem to be doing for many fabs) cycle time is
likely to also increase. What’s less obvious is the behavior
that one of our subscribers pointed out in this month’s subscriber
discussion forum: sometimes when start rates decrease, cycle time
increases. This wouldn’t normally happen if there were no other
changes in the fab. Utilization would go down, for tools and
operators, and cycle time would almost surely go down. However,
that’s not a realistic case. What really happens in many fabs
is that when start rates go down, tools are turned off and staffing
is reduced. The net result from this is that the bottleneck
utilization of the fab may stay the same, or even increase. So,
no cycle time payoff from the decreased start rate. What also
happens is that the number of tools per tool group decreases,
sometimes to the point of having one-of-a-kind tools in operation.
This lack of tool redundancy is a key driver of cycle time
(currently ranked third on FabTime’s cycle time problems survey,
after downtime and bottleneck utilization), and is the primary
subject of this article.
Subscriber discussion topics for this month include two
responses to last month’s article about tool standby and productive
time reporting. New topics include incorporating setup in equipment
utilization calculations, understanding the cycle time effects of
automated material handling and robotic systems, and understanding
cycle time and “under-utilization” in fabs. This month also kicks
off a new newsletter section: Cycle Time in the News. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
using manufacturing execution system (MES) data to calculate
fab performance measures. More specifically, we discuss the
cycle time management benefits of tracking standby and productive
time, in addition to tracking tool downtime states. Tool utilization,
defined as Productive Time / (Productive + Standby Time) is the
largest driver of operation-level cycle times. For this reason,
we recommend reporting tool utilizations on a short-term (e.g.
shift-level) basis, and automatically flagging situations where
utilization approaches 100%. Fabs may be able to do proactive
things, like reassigning operators, or deferring engineering
or maintenance time, to nip short-term cycle time problems
in the bud. To do this, however, fabs will need to ensure that
their manufacturing execution systems either track productive
and standby state changes directly, or generates them in some
other manner.
We have no subscriber discussion topics in this issue. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
metrics for identifying short-term bottlenecks in a
semiconductor wafer fab fab. Last month we proposed
the metric Dynamic X-Factor as a short-term indicator
of overall fab performance. In this article, we focus
more on tool-level performance metrics. The idea is to
identify metrics that can be used at the start of the
shift to highlight current or anticipated cycle time
problems in the fab. We first discuss a few simple
metrics, and their relative applicability to this
problem. We then propose a simple calculation (WIP hours)
for identifying short-term bottlenecks without performing
simulation, by estimating the hours of work in queue for
a toolset. We don’t have all the answers here, but we
would like to start a discussion with the FabTime
newsletter community about this. Ultimately, we want to
work towards developing useful short-term metrics for
identifying temporary bottlenecks in wafer fabs.
Subscriber discussion topics for this month include
two responses to last month’s article about the performance
metric Dynamic X-Factor, and new questions about managing
in high-mix and R&D environments. We also have announcements
about a new one-day version of FabTime’s cycle time
management course, a Cost of Ownership task force meeting,
and the acquisition of WWK by its management team. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
the wafer fab performance metric Dynamic X-Factor.
Dynamic X-Factor measures, on a point-in-time basis,
how much of the WIP in the line is currently being
worked on, instead of sitting in queue. If Dynamic
X-Factor drifts upward, cycle time will probably
start to increase in the future (because either
there is more WIP, or WIP in the line is sitting
more than it should be). Dynamic X-Factor is calculated
by taking the total number of wafers in the fab and
dividing by the number of non-rework wafers actually
being processed. While Dynamic X-Factor works out to
be the same as the regular cycle time X-Factor
(cycle time / theoretical cycle time) on a long-term
basis, Dynamic X-Factor is easier to calculate, and
is more forward-looking than an X-Factor based on
shipped lot cycle times. While there are some
limitations to this metric, we think that it
provides a useful indicator of current fab cycle
time performance. We recommend its use for
semiconductor fabs.
We have no subscriber discussion topics in this issue. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
identifying real-time cycle time problems
in a wafer fab. We wrote this article in
response to an informal survey that we have
been conducting about cycle time problems
in semiconductor wafer fabs. The
fourth-most common response to date has
been real-time identification of cycle time
problems (e.g. problem tools or
operations). This is a nuts-and-bolts kind
of topic that we’ve addressed only
indirectly in this newsletter so far. In
this issue, we propose metrics and methods
for identifying cycle time problems in the
fab on a short-term basis, so that they can
be addressed and improved. Metrics
discussed include operation-level cycle
time, summed operation cycle time,
inventory age, arrival coefficient of
variation, and availability variability. We
also touch on some more detailed methods
for using real-time data to understand
problems and improve operational decisions.
Specifically, we focus on tool dedication,
staffing decisions, batch loading policies,
and maintenance schedules.
Subscriber
discussion topics for this month include a
response to last month’s main article about
operators and cycle time, several responses
to last month’s question about how
companies calculate On Time Delivery
percentage, a new question about the
productivity of engineering staff, and a
new question about wet bench capacity. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
planning and managing operators in
semiconductor wafer fabs. In looking over
the past issues of this newsletter, we
observed that we have had a considerable
amount of subscriber discussion related to
staffing. This discussion has primarily
fallen into two categories: 1) operator
modeling/planning and 2) operator
management (including dedication,
cross-training, and performance
evaluation). The first category concerns
understanding how many operators will be
required, and how they will impact cycle
time and throughput. The second category
concerns managing operators once staffing
levels have been determined, to minimize
cycle time and maximize throughput. In this
article, we summarize the subscriber
discussion to date on operators, bringing
it into one place, instead of scattered
across two years of newsletter issues. We
will also summarize FabTime’s
thoughts on the operator-related questions,
and highlight industry resources that we
know of related to operators (software,
papers, etc.). (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
arrival variability and cycle time in
semiconductor wafer fabs. While working
with our FabTime cycle time entitlement
calculator (described in Volume 4, Number
3), we observed some interesting behavior
for cases with a high degree of arrival
variability. We found that arrival
variability due to batching tended to have
less of an impact on cycle time than other
types of arrival variability for the cases
that we investigated. In this article, we
show examples generated from simulation
models, and discuss the impact of this
behavior on the formulas in our operating
curve generator and entitlement calculator.
We also introduce a modification to our
operating curve generator that accounts for
arrival batching.(
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
the cycle time effect of equipment
downtime. When we ask people what factors
contribute to cycle time in their fabs, the
number one response that we get is
"downtime". Certainly equipment downtime is
a fact of life in wafer fabs. In this
article we review the reasons why downtime
has such a significant influence on cycle
time (utilization and variability). We also
propose three steps for mitigating the
effect of downtime on cycle time.
Subscriber discussion topics for this
month include material handling system
metrics and cycle time reduction; the
metric mean time to recover; and the cycle
time effects of integrated metrology in the
lithography area. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
cycle time entitlement for semiconductor
wafer fabs. This newsletter has frequently
addressed topics related to managing and
improving cycle times, and the various
metrics for reviewing historical cycle
times and benchmarking cycle time
performance. But what people who work in
fabs really need to know is: what is a good
cycle time for our fab, under our current
constraints? And where should we focus our
cycle time improvement efforts? Cycle time
entitlement is FabTime’s answer to
these questions. More formally, cycle time
entitlement is the best achievable cycle
time for a fab given short-term realities
related to tool utilization, staffing, and
downtime characteristics. In this article
we define cycle time entitlement, and
discuss ways of estimating it, ways of
using it, and associated data issues.
Subscriber discussion topics for this
month include responses to our article
about quantifying availability variability
and to last month’s subscriber
question about train schedule batch
policies, as well as a new question about
estimating company-wide savings from cycle
time reduction. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
This month’s main article is about
quantifying the variability of availability
in a fab. Last month we discussed
calculating coefficient of variation for
interarrival times and process times. We
could calculate the coefficient of
variation of availability. However CV is a
dimensionless metric that may not carry
intuitive meaning for people. Instead, we
discuss the metrics A80 and A20, recently
described by Peter Gaboury in a Future Fab
International article. A80 is the best
availability reached within 80% of the
periods in a set of periods (shifts, days,
weeks, etc.), while A20 is the best
availability reached (or exceeded) in at
least 20% of the periods in a set. By
tracking the spread between A20 and A80,
and trying to reduce it, we can reduce the
variability of availability, and hence
improve cycle time. And by dealing with
percentiles, we can use metrics that carry
more meaning for people on an ongoing basis
than CV values.
In this month’s subscriber
discussion forum we have a response to last
month’s article about process time
variability, a question about the cost of
having the entire fab down for a period of
time, a question about a "train scheduling"
batch loading policy, and some comments on
wafer moves per operator. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
Our main article this month is about
quantifying variability in wafer fabs. We
have talked many times about how wafer fab
cycle time can be reduced by reducing fab
variability. In this article, we describe a
metric for quantifying this variability
(coefficient of variation), and discuss how
to calculate it for times between arrivals
and for process times. We believe that by
measuring variability, particularly
relative levels of variability at
individual tool groups and operations,
readers will be better able to identify
potential improvement areas.
In this month’s subscriber
discussion forum we have responses from
three subscribers to our recent topics
regarding operator productivity. (
Return to newsletter subscription page to
subscribe and receive the current issue
free each month)
|
