Proceedings of the 2016 Winter Simulation Conference
T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds.

USING SIMULATION TO IMPROVE SEMICONDUCTOR FACTORY CYCLE TIMEBY SEGREGATION OF PREVENTIVE MAINTENANCE ACTIVITIESKosta Rozen

Néill M. Byrne

Intel Corporation
Fab/Sort Manufacturing Division, Fab28
2 HaZoran ST
Qiriat-Gat, 82109, ISRAELIntel Corporation
Fab/Sort Manufacturing Division, Fab24
Collinstown Industrial Estate
Co. Kildare, W23CX68, IRELAND

ABSTRACT
Semiconductor manufacturing is a very costly and time-consuming process and any reduction in cycle time(CT) can result in a significant cost saving due to shorter time-to-market, reduced inventory and improved
yield. This paper examines the topic of PM (Preventative Maintenance) segregation where the goal is todetermine the optimum PM frequency that results in reduced fab CT. Much of the literature on this topic
limits the scope of the study to CT performance of an individual toolset, without examining the wider
impact on overall fab CT. The goal of this paper is to examine the fab-wide impact of PM segregation
projects and reveal critical factors that will allow to identify toolsets with the most potential to improveoverall fab CT by splitting PMs. Each toolset identified using these factors may improve fab CT by 1% or
more, resulting in a very significant gain, if applied on multiple toolsets.
1

INTRODUCTION

Semiconductor factories, commonly known as fabs, are unique manufacturing environments, characterized
by an extremely long manufacturing process, consisting of hundreds of sequential processing steps,
performed by shared toolsets subject to restrictions such as step dedications, dynamic lock-outs of tools to
steps, process time window limitations (manufacturing steps that must be completed within a predeterminedamount of time) and rapidly changing product mixes. Operationally, the process has many sources ofvariability, mainly induced by a combination of machine down-time and highly re-entrant process flow.
One of the main goals of a typical high volume semiconductor fab are reducing cycle time (CT), given
a defined fab capacity and loading plan. Reduced CT results in lower inventory costs, improved quality,
better response to demand changes and shorter time-to-market for new products.
There are numerous methods for cycle time reduction, which can be roughly grouped in 3 maincategories:•••

Operational improvements: identification and efficient utilization of constraints, effective
scheduling methods, elimination of non-value-added activities using lean manufacturing methods,
variance reduction, standardization and efficient management of process time windows etc.
Process improvements: removing operations, removing restrictions, removing process time
windows, merging toolsets, reducing process time, etc.
Equipment improvements: improving toolset availability, reducing availability variance byreducing frequency of unscheduled (unexpected) events or duration of scheduled (planned) events,
scheduling planned events at desired times in order to reduce impact of unavailable tools.

978-1-5090-4486-3/16/$31.00 ©2016 IEEE

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Direct experimentation on the factory line can be expensive and infeasible, therefore models of thesystem are typically employed to assess the gain from a particular improvement and identify which
improvements deliver the most economic gain. There are several modelling strategies, such as discrete
event simulation, data mining and analytical approximations, discussed in depth by Hopp and Spearman(2011) .
A large portion of machine down-time is spent in Preventive Maintenance (PM) activities, which reduce
the likelihood of unscheduled down-time events and ensures that the machine runs within defined control
limits. Based on SEMI E10-0701, a typical fab PM comprises three sequential parts: pre-PM preparations,intrusive maintenance, and tool qualification. Pre-PM preparations and tool qualification are considered as
Non-Value-Added (NVA) activities, while intrusive maintenance is a Value-Added (VA) activity. NVA
activities are sometimes also referred to as PM Setups (PMSU).
Each PM has a duration and a PM frequency which is generally either defined in time between PM or
wafers between PM. These times can be changed by modifying PM content or frequency, in order tomaximize a given objective function. Most common PM optimization policies strive to maximize toolset
availability (Barlow and Hunter 1960), but recent studies have shown that reduced variability may be abetter objective function (Tirkel 2013).
2

PROBLEM STATEMENT

The topic of PM segregation (also called PM splitting) has been covered by Kalir (2013), Regev andBenson-Karhi (2015) amongst others. Longer PM activities increase the variance of WIP flow in the
manufacturing line, resulting in increased cycle time. Reducing the PM duration usually leads to reduced
variability and reduced cycle time. Hence, there is an incentive to reduce PM duration, perhaps even at
expense of increased PM frequency and duration (Kalir 2013; Tirkel 2013).
When segregating a PM into two or more parts, we split the value added activities of the PM, but each
new PM has to perform the PM Setups (PMSU). For example, if the value added PM time is 50 hours andnon-value-added PMSU time is 10 hours, overall duration of PM is 60 hours. Splitting the PM VA time
into 2 equal parts results in 2 PMs, lasting 35 hours each (25 VA hours + 10 NVA hours). Performing both
halves of the split PM takes 70 hours instead of 60 hours of the original PM.
Therefore the problem of assessing the benefit from segregation of a specific type of PM is an
optimization problem, examining the trade-off between reducing variance (due to shorter PM durations)and increasing tool down-time (due to more frequent PMs). In practice this can be a time-consuming task
as there are hundreds of PM types in a typical fab. This paper proposes a generic guideline that will quickly
identify a manageable amount of PMs that can be further examined in more detail and optimized. The initial
filter involves selecting PMs with relatively long durations and relatively short PMSUs. This is a reasonableapproach, and it will usually reduce the amount of “candidate PMs” from hundreds to several dozen.
The goal of this study is to examine the impact of additional toolset properties (factors) and identity the
relative importance of each factor on expected gain from PM segregation. Following that examination, we
will provide a set of heuristic guidelines for quick selection of toolsets with the greatest potential gain. The
following 4 toolset factors will be examined: toolset loading, number of machines, number of operations
and position in the manufacturing line. The objective of the current study is to identify which of thesefactors are more important than others, and as such should be used as main filters for selecting “candidate
PM” for a more detailed segregation study.
Each of these factors can be examined in greater detail, but for the purpose of this study, only twovalues were selected for each factor: one typical value and one extreme value. By comparing the gain fromPM segregation between scenarios with these two values, it is possible to estimate the relative importanceof each factor. Future studies may then be able to focus on factors that carry the most impact and perform
a more detailed study and sensitivity analysis of each parameter on the potential gain from PM segregation.
Other relevant parameters not examined in this study are PM and PMSU duration. Both of them havea major impact on the gain from PM segregation, but their impact have already been examined in other2677

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studies, like Kalir (2013). Therefore, these parameters were set to have constant representative values, as it
is safe to assume that toolsets with short PMs or with very long PMSU durations are not good candidates
and will be excluded from examination.
When analyzing the potential gain from PM segregation or PM scheduling, most studies (Kalir 2013;
Morrison et al. 2014), focus on optimization of cycle time of the toolset under examination, without
accounting for the cumulative effect of variance propagation to subsequent toolsets in the line. This is an
incomplete analysis due to re-entrant nature of the fab production flow and complex scheduling rules oneach toolset. Therefore, if we are to assess whether a project is beneficial for the entire fab (global optima
vs local optima), it is necessary to analyze the CT impact on a full fab and not just the toolset itself. Usinga full fab simulation model is required in order to assess the impact of local change on the entire fab.
Analyses that limit results to the toolset under examination skew results in favor of non-segregation, as theycapture the full loss from reduced availability due to PM segregation in the toolset’s cycle time but do notcomprehend potential gain in downstream toolsets CT due to variance reduction resulting from PM
segregation.
3

SOLUTION METHODOLOGY

3.1

Simulation Model

In order to analyze the impact of proposed experiments, a full factory simulation model was used, based on
AutoSched AP simulation software provided by Applied Materials. The software was further enhanced bymultiple extensions developed by Intel’s Decision Support Technology group, described in (DeJong andFischbein 2000). Following the classification method in Jimenez et al. (2008), the details level of thesimulation model may be classified under the third quadrant with a high level of capacity detail (level 5-6
per their classification) and a low level of AMHS detail (level B).
This simulation model has been used extensively and produces highly accurate and well-validated
results. It has consistently captured fab CT within several percent of actual observed fab CT, and thus
operates as a good test-bed for our experimentation. The model is highly complex and factors in process
times, scheduled and unscheduled downtimes, setups, batching and dispatching rules, layer lock outs
(inability of certain tools to run specific operations that tools of this toolset should usually run), lithography
reticles, tool dedications, rework, test wafer production, time-constrained production sequences and manyother typical fab complexities.
Typically, the model is used to examine transient simulations without a warm-up period, but for the
purpose of this study, it was adjusted to a steady-state simulation by removing time-related changes andadding a warm-up period that lets the simulation stabilize regardless of initial starting conditions. The
proposed simulation has not yet been validated as it requires a fab to be in relatively steady-state. Therefore,
this constitutes a direction for further work.
3.2

Design of Experiments

The following 4 toolset factors will be examined in order to determine the gain from PM segregation on atoolset with different values of these factors: toolset loading, number of machines, number of operations
and toolset position in the manufacturing line.
For each examined factor, two values were selected: one typical value (based on average expectedvalues in a semiconductor fab) and one extreme value (in order to examine the impact of this factor). By
comparing gain from PM segregation between scenarios with these two values, it is possible to estimate therelative importance of each factor. Selected values for each of the examined factors are listed below:

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Table 1: List of selected values of each examined factor.
FactorToolset loading
Number of machines
Number of operations
Toolset position in the line

Typical Value
80% (Non-Constraint)
63
Spread evenly

Extreme Value
95% (Constraint)
2 (Few)
20 (Many)
Close to start of the line

To examine the impact of each of these toolset factors, the duration of base PM was set to the samevalue: 40 hours – based on an average PM duration for all PMs exceeding 24 hours in a real fab (shorter
PMs are usually not good candidates for segregation due to major impact of PM setup time on segregatedPMs). The duration of PMSU time in each scenario was set to be constant as well: 4 hours – based on anaverage NVA portion of a long PM in an average toolset, mostly consisting of repeated runs of calibration/ cleaning / monitor wafers on the tool and completing the PM only when these wafers show expected
results. Intuitively, the PM and PMSU durations have critical impact on gain from PM splitting, but the
purpose of this article is to define a guideline for identifying a group of toolsets that are likely to have thisgain, assuming reasonably long PM duration and reasonably short setup duration. All other toolset
parameters, like mean time between PMs, unscheduled down-time, etc. were held constant in all scenarios
to rule them out of interfering with experiment results. The only change between the scenarios is the relevant
parameter (factor) of the examined toolset and its PM policy.
Two PM options were simulated for each scenario: original PM (without segregation) and PM split to
two PMs with equal durations. Further PM splitting was ruled out of the examination as if there is no gainin splitting a PM into two parts, there is unlikely to be any in further splitting it into three or more parts.
This notion is backed up by Kalir (2013) who showed that for reasonable values of PMSU (4 to 6 hours),
the optimal gain is in splitting PM into 2 parts even when the duration is as long as 120 hours. In addition,
further PM splitting is not always practical from a resources perspective.
Table 2: List of simulated PM duration scenarios.
PM
Original
Split (to 2 parts)

Single PM Value
Added Time [hrs]3636 / 2 = 18

Single PM
Duration [hrs]36 + 4 = 40
18 + 4 = 22

Overall PMs
Duration [hrs]40
22 X 2 = 44

% of PMSUTime in PMs
10%
18.2%

In order to examine the impact of each factor, a total of 10 simulation experiments were run, with eachexperiment isolating one parameter only and keeping the values of the remaining parameters constant. The
experiments are outlines in table 3.
For each scenario shown in table 3, 8 replications of a steady state simulation were run, resulting in a
total of 80 simulation runs. Length of the warm-up period was defined as 20 weeks, based on the longest
stabilization time observed in all simulated replications. Length of the single simulation (including warmup period) was set at 100 weeks, resulting in 80 weeks of usable data, after excluding 20 warm-up weeks.
The selected period of 80 weeks is significantly longer than the CT of a typical fab product, hence thislength is sufficient for observing the long-term effect that each factor has on overall line performance.
Running several replications instead of one long continuous simulation allows to separate typical fab
variability perturbations (which take a long time to dissipate) from the variability resulting fromexperimental controls (Dummler 2000).

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Table 3: List of simulated experiments.
EXP#
1
2
3
4
5
6
7
8
9
10
4

Examined Factor
Typical Toolset
Constraint
Few Machines
Many Operations
Line Start

PM
Original
Split
Original
Split
Original
Split
Original
Split
Original
Split

Loading
80%
80%
95%
95%
80%
80%
80%
80%
80%
80%

Machines
6
6
6
6
2
2
6
6
6
6

Operations
3
3
3
3
3
3
20
20
3
3

Toolset Position
SpreadSpreadSpreadSpreadSpreadSpreadSpreadSpreadLine Start
Line Start

SIMULATION RESULTS AND ANALYSIS

Averages were calculated on the following statistics (for steady-state period only, after discarding warmup period data):
•

•
•

•

Normalized Fab Cycle Time – measures the average velocity of the entire line. Fab CT of all
experiments was normalized based on baseline fab CT of scenario “Typical Toolset with Original
PM schedule”. In order to avoid local optima (optimizing PM duration for lowering CT of thetoolset, without regard for downstream impact of variance generated by that toolset) we will define
the true impact of each toolset factor based on this statistic.
Normalized Toolset Cycle Time – measures the performance of the toolset being examined.
Toolset CT of all experiments was normalized based on baseline Toolset CT of scenario “TypicalToolset with Original PM schedule”.
Toolset-Fab CT Gain Ratio – measures what percentage of fab CT gain is attributed directly to
CT gain on the examined toolset, as opposed to the gain derived from reduced on the manufacturingline. This statistic is calculated as toolset CT Gain in hours divided by fab CT Gain in hours. For
example, if examined toolset has gained 1 hour of CT due to PM splitting, while the fab gained 10hours, the value of this statistic will be 10%, meaning that major part of fab CT gain is due toreduced down-stream variance and not from direct CT reduction of the toolset.
Statistical significance of difference – result of statistical test, that assesses the probability that
two samples (CT in Original PM and Split PM scenarios) belong to the same population. The higher
the percentage, the higher the confidence that relevant factor has statistically significant impact on
Toolset or Fab CT.

The summary of all collected statistics is listed in Tables 4 and 5 below, with Gain% column
representing percentage of CT gain resulting from PM segregation in this scenario (calculated as % of
improvement in CT from same scenario with Base PM policy). Normalized Fab CT is presented in Figure
1, while Normalized Toolset CT is depicted in Figure 2.

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Table 4: Simulation results - Normalized toolset CT.
ExperimentBase PM

Split PMGain%

Typical Toolset
Constraint
Few Machines
Many Operations
Line Start

100.0%
709.5%
261.7%
249.7%
84.4%

92.1%
881.4%
234.0%
225.0%
84.6%

7.9%-24.2%
10.6%
9.9%
-0.3%

Statistical significanceof difference
83%
100%
89%
93%
8%

Table 5: Simulation results - Normalized fab CT.
ExperimentBase PM

Split PMGain%

Typical Toolset
Constraint
Few Machines
Many Operations
Line Start

100.0%
104.7%
105.8%
103.7%
96.5%

99.9%
106.5%
104.7%
102.4%
96.6%

0.1%
-1.7%
1.1%
1.3%
-0.2%

Statistical significanceof difference
5%
90%
63%
80%
18%

Normalized Fab CT

Normalized Toolset CT
1000%
900%
800%
700%
600%
500%
400%
300%
200%
100%
0%

TypicalToolsetConstraint

Few
Machines

Original PM

Many
Operations

Toolset-Fab
CT Gain Ratio
99.2%
65.5%
16.7%
12.8%
0.9%

Line Start

108%
106%
104%
102%
100%
98%
96%
94%
92%
90%

TypicalToolsetConstraint

Few
Machines

Original PM

Split PMMany
Operations

Line Start

Split PM

Figure 1 and 2: Charts of normalized toolset and fab CT for all examined scenarios.
When applying PM segregation on a typical toolset, there is a slight gain on toolset level, but not on
fab level. Fab gain of 0.1% is not statistically significant, and if it exists it’s fully derived from direct CTgain of the toolset and not from variance reduction (as the typical toolset is not a major variance generator,
not being a constraint and having 6 machines). This is a good example of toolset that has a relatively large
CT gain due to PM segregation, but has no real impact on overall fab CT.
When applying PM segregation to a constraint toolset, there is a negative impact both on the toolset
itself (24.2% loss of CT compared to an original toolset) and on fab-wide CT (1.7% loss). 65.5% of fab CT
loss is derived from increased toolset CT, and remaining 34.5% - due to increased variance generated by
the constrained toolset, that becomes even more constrained in the PM segregation scenario because ofincreased down-time resulting from additional PMSU. This is an important finding and carries a practical
impact when deciding the cases in which PM segregation is expected to be beneficial.
When applying PM segregation on a toolset with few machines, the gain is positive both in toolset andfab level: 1.1% gain in fab CT, most of it (83.3%) deriving from variance reduction and not from CT

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reduction on the examined toolset. As expected, the gain to toolset CT is larger than in a typical toolset,
demonstrating that PM segregation has larger gain in a toolset with few tools, but the fab-wide gain is much
more significant than toolset-level gain. Still, statistical significance of fab gain is only 63%, and more
replications are required in order to increase the confidence levels (direction for future research).
When applying PM segregation on a toolset with many operations, the gain is positive both in toolset
and fab level: 1.3% gain in Fab CT, most of it (87.2%) deriving from variance reduction and not from CT
reduction on the examined toolset. Examined from a manufacturing physics perspective, the results arereasonable and expected: having long PMs on a toolset having many operations creates significant variancein many points in the manufacturing line. PM segregation reduces that variance, resulting in significant
gain to overall fab CT. Toolsets with few machines or many operations constitute good examples of toolsets
where PM segregation gain on fab level is much higher than the CT gain on the relevant toolset, due tocumulative effect of reduced variance on downstream toolsets. In this experiment as well, it isrecommended to run additional replications to increase the significance of the results (80%).
When applying PM segregation on a toolset located in start of the line, there is no statistically significant
gain neither on toolset nor on fab level, meaning that location of the toolset makes no major difference on
gain from PM segregation.
5

CONCLUSIONS AND RECOMMENDATIONSThis paper has identified the most opportune toolset types to target as candidates for PM segregation basedon the characteristics of the toolset, whether it is a constraint, has a low tool count (low parallel processing
capacity), performs many reentrant steps or is positioned at the start of the line.
The results indicate that one of the most favorable characteristics for PM segregation in terms of both
local toolset improvement and production line performance is number of machines. Our results indicatethat the gain from splitting PM’s with low parallel processing capacity (few machines) is significant andthat the majority of the gain comes from reducing the downstream variability in the line. Here, the cost of
decreasing the toolset availability due to splitting its PM and thereby introducing additional non-value
added time can be absorbed by the toolset. The subsequent reduction in output variability from the toolset
eliminates the ‘boom-bust’ phenomenon generally associated with tools with large burst capacity but withfew tools and hence higher probability of being “lines-down”.
Similarly toolsets that have many operations across the line and are therefore subject to high reentrancy
can also benefit significantly from PM segregation. In this case as well, the gain was seen on both the local
toolset cycle time and on the overall line cycle time, with the vast majority of the gain being attributed toline cycle time. The conclusion here is that tools with many operations (even if they are not deemed
constraints) are good variability modulators for the factory and can control the variability propagation
throughout the line by reducing the variability of their availability.
Splitting or segregating PMs on constraint tools has an adverse effect on both toolset and factory cycle
time. Here, the availability of the toolset is a high modulator for factory output performance and hence any
reduction in availability due to PM splitting removes ‘precious’ capacity from the constraint. Furthermore
any potential downstream improvement in variability is outweighed by the reduced capacity and restrictedflow.
It was also shown that tools positioned at the start of the line with all other parameters fixed do not see
any statistically significant change by splitting PMs and therefore this attribute should not be considered as
an individual positive characteristic for PM splitting decisions. The result suggests that toolset with
operations spread throughout the line still generates sufficient variance on downstream toolsets, so that
there is no statistically significant difference between it and toolset located in the start of the line. Still, it isexpected that toolsets located only at the end of the line would have less impact on overall fab CT thantoolsets located in the start of the line or spread throughout the line. This experiment was not inside a scopeof the current article and may be examined in a follow-up study.

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Another learning from this study is that measuring the true gain from PM segregation or other varianceimpacting policies, requires measurement of overall fab CT, which is the real objective of the improvement.
CT gain on a particular toolset is not a reliable estimator for gain in overall fab CT, as demonstrated by thetypical toolset experiment that has CT gain on a toolset level but barely features on a fab level, and by
experiments with few machines and many operations, that have fab CT gain significantly exceeding toolset
CT gain.
In conclusion, it is generally considered that reducing variability of available capacity can lead to amore predictable factory output and reduce cycle time. However, improving variability of availability by
reducing the impact of PM through PM splitting is not a panacea and comes with the additional cost of
introducing non-value added maintenance related activities. It has been shown in this paper that there are
only certain types of candidate tools that will improve factory velocity by segregating PMs. Most notably,
non-constraint toolsets with many operations, few machines, long PMs and where possible short PMSUs
are the best candidates for selection. Our results have shown that splitting PMs at toolsets with these
individual characteristics showed an improved line performance and by deduction it is expected that tools
combinations of these ‘positive’ characteristics will see an improvement as well. At best case, theimprovement will be compounded due to combinations of these ‘positive’ characteristics, and at worst case,it will be close to an improvement based on their best individual characteristic. Furthermore, it is notablethat much of the gain comes from downstream variability reduction, which is often not examined in thesetypes of studies. As semiconductor manufacturing becomes more complex and the manufacturing cycle
time increases, any gains to the manufacturing line will become more significant and a more holistic
approach to PM segregation studies may be required.
ACKNOWLEDGMENTS
We express our appreciation to Dr. Adar Kalir, for his invaluable direction and advice.
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Regev, I. and Benson-Karhi, D. 2016. “Segmentation of the Expected Duration of Maintenance Activities
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AUTHOR BIOGRAPHIESKOSTA ROZEN received his M.Sc in Industrial Engineering and Management from Ben-Gurion
University, Israel. He is currently a Senior Simulation Engineer at Intel Corporation in Qiriat-Gat, Israel.
He is also an Adjunct Lecturer with Ben-Gurion University, Israel. His email address iskosta.rozen@intel.com.
NEILL BYRNE received his M.Sc and PhD in Industrial Engineering from Dublin City University,
Ireland. He is currently a Simulation Engineer at Intel Corporation in Leixlip, Ireland. His email address isneillbyrne@gmail.com.

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