benefits and drawbacks of simple models for complex

Oliver Rose

Dresden University of TechnologyDepartment of Computer ScienceChair for Modeling and Simulation

Benefits and Drawbacks of Simple Models

for Complex Production Systems

2

Dresden University of Technology

Department of Computer Science

Oliver Rose

Simple Models


Dresden located in the state Saxony, often called “Silicon Saxony” (factories from Infineon, Qimonda, AMD, and ZMD; lots of suppliers)Largest German University of TechnologyFocus on Computer Science, Electrical Engineering, and Mechanical EngineeringAbout 35,000 students, 3,000+ students in Computer Science, 600+ beginners in winter semester 2006/2007Faculty of Computer Science consists of 28 chairsInstitute of Applied Computer Science has 5 chairs, dealing with different aspects of factory planning, control, and automation

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Oliver Rose

Simple Models

Research Overview

Complex Production System

Model

Simulation

Dispatching Simulation-based

Scheduling

Me

asu

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An

aly

sis

Au

tom

ati

on

of

Da

ta T

ran

sfe

rHigh-level Production Control

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Oliver Rose

Simple Models

Cooperations

IndustryInfineon Technologies AG, Munich and DresdenAMD Saxony LLC & Co. KG, DresdenKBA Planeta, Radebeul (Offset Printing Machines)Airbus Deutschland GmbH, Hamburg

AcademiaArizona State University, Tempe, AZ, USASingapore Institute of Manufacturing TechnologyGeorgia Institute of Technolgy, Atlanta, GA, USAFernUni Hagen, GermanyFraunhofer IPA, Stuttgart, Germany

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Oliver Rose

Simple Models

Semiconductor Manufacturing

Back End

Front End

Wafer Fab

TestAssembly

Probe

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Oliver Rose

Simple Models

Flow of material

Fotos: Fullman-Kinetics, Varian, Sematech International

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Oliver Rose

Simple Models

Characteristics of wafer fabrication facilities

Large number of processing steps, typically several

hundreds

Large number of tools of different types: photo

equipment, ovens, etching equipment, ion implanters, …

Wafer are build up in layers: reentrant flow of material,

jobshop type way of production

Machine breakdowns (typical availability: 70-90%)

Auxiliary resources, e.g., reticles (photo masks)

Batch tools with complex batching criteria

Sequence dependent setups

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Oliver Rose

Simple Models

Important performance measures

Cycle time: low

Output: high

Machine utilization: high

Inventory (work in process, WIP): low

Yield (percentage of good dies on a wafer): high

Cost per die: low

Conflicting goals!

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Oliver Rose

Simple Models

Motivation for simple models

Traditionally, only full detail models used for operational planning and control of semiconductor fabs

Consequences:Long run times of simulation experimentsLong run times of scheduling algorithmsToo complex to be included in enterprise models for SCM (Supply Chain Management)

Need for simple fab models

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Oliver Rose

Simple Models

Delay

Simple modeling approaches

RequirementsCorrect representation of characteristic curve (cycletime-over-utiliziation curve), i.e., typically 1/(1-utilization) shapeSame cycle time distributions as for real fabMimic typical behavior of fab over time

Very simple model: cycle time distributionDoes not depend on utilizationHas no capacity

0

5

10

15

0 0.2 0.4 0.6 0.8 1

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Oliver Rose

Simple Models


Simple queuing systemBehavior over time not appropriateIn general, shape of characteristic curve problematic

Delay DelayBuffer

Bottleneck

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Oliver Rose

Simple Models


Simple queuing system with loop(re-entrant flow of material)

Delay Delay

Bottleneck

Delay

?

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Oliver Rose

Simple Models

Mimic behavior of real fab

Goal of the study: Estimate the transient behavior of

a wafer fab after recovery from a catastrophic

failure

Motivation: fab engineers reported that large amounts of

WIP are present in the fab even weeks after end of repair

Problem of the analysis: estimation of the required

inventory curves not feasible with full model; hundreds of

replications of experiment needed

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Oliver Rose

Simple Models

Simple fab model

fixed number of cycles

bottleneckworkcenter

delay unit(rest of the fab)

finished?

yes

no

lot release

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Oliver Rose

Simple Models

Model details

Dispatching strategies:

FIFO

Critical Ratio

Slack Time

Delay time distributions of the delay unit

Lot release policy

Partial bottleneck breakdowns

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Oliver Rose

Simple Models

Modeling the Rest of the Fab

?

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Oliver Rose

Simple Models

Delay time distributions

Constant amount of time for sum of processing times

Distributions of different variability for sum of non-

processing times (waiting times, transport times, etc.):

Constant (coefficient of variation: 0.0)

Erlang-5 (coefficient of variation: 0.447)

Exponential (coefficient of variation: 1.0)

Independence of consecutive and parallel delays assumed

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Oliver Rose

Simple Models

Constant Delay

FIFOCritical RatioSlack Time

time

WIP

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Oliver Rose

Simple Models

Shifted Exponential Delay


time

WIP

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Oliver Rose

Simple Models

Shifted Erlang Delay


time

WIP

Dura

tion o

fbre

akdow

n

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Oliver Rose

Simple Models

Delay Distribution from Real Data

0

0.01

0.02

0.03

0.04

0.05

0.06

0 20 40 60 80 100 120 140hours

datagamma

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Oliver Rose

Simple Models

Characteristic curve

Utilization

Simple

Full detail

Flow

fac

tor

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Oliver Rose

Simple Models

Model improvement approach

Make delays load dependent! But how to measure load?

Delay Delay

Bottleneck

Delay

?

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Oliver Rose

Simple Models

Load measurement

Simply count lots in loop!

Delay Delay

Bottleneck

Delay

?

Lots in loop

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Oliver Rose

Simple Models

Generation of the load dependent data

Very long simulation run with stepwise (5% steps) increase of fab loadCollection of the delays for given range of lots in loop (interval width of 5 lots)

Simulated time [days]

Lots

in loop

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Oliver Rose

Simple Models

Load dependent “loop” delays

Lots in loop

Del

ay [

min

]

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Oliver Rose

Simple Models

Delay time percentiles

Lots in loop[100,124]

Lots in loop[25,49]

Probability

Del

ay [

min

]

In most cases, very close to exponential (correlation > 90%)

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Oliver Rose

Simple Models

Improvement of the characteristic curve

Utilization

Simple

Full detail

Flow

fac

tor

Load dependent

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Oliver Rose

Simple Models

Estimation of cycle time distributions

Detailedmodel

Simplemodel

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Oliver Rose

Simple Models

Estimation of cycle time distributions

0

0.005

0.01

0.015

0.02

0.025

0.03

0 100 200 300 400 500 600 700 800 900hoursHours

0

0.005

0.01

0.015

0.02

0.025

0.03

0 100 200 300 400 500 600 700 800 900hoursHours

0

0.002

0.004

0.006

0.008

0.01

0.012

700 750 800 850 900 950 1000 1050hours

fullsimple

FullSimple

Hours

FIFO

0

0.01

0.02

0.03

700 800 900 1000hours

fullsimple

FullSimple

Hours

CR

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Oliver Rose

Simple Models

Conclusions

Simple models useful for analyzing and understanding

complex production systems

Not a tool for beginners

Not appropriate for all problems

Pitfall of oversimplification

Simplification must not be the goal but only the method

to reach the goal

Keep the model as simple as possible but not simpler!

benefits and drawbacks of simple models for complex

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