dynamic simulation: guiding manufacturing from … · avs natl symp, oct 2000 avs00inv.dynsim.ppt...

11/20/00 1AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000

Dynamic Simulation: Guiding Manufacturing from Process Mechanisms

to Factory Operations

G. W. Rubloff

Director, Institute for Systems ResearchProfessor, Materials Science & Engineering, and Electrical & Computer Engineering

University of Maryland

Dynamics plays a critical role in the behavior and performance of semiconductor manufacturing from the unit process level to full factory operations, yet major gaps exist in our ability to simulate the consequences of this dynamics. At the process level, process models can provide a reasonable description of steady-state process behavior, but the realities of semiconductor equipment dictate that both total process times and thermal histories depend on the dynamics of the equipment and control systems, as well as on the raw process itself. We have developed physically-based dynamic simulation strategies which accurately reflect time-dependent behavior of equipment, process, sensor, and control systems, and we have used them to understand and optimize equipment systems and process recipes. Another dimension of dynamics appears in the behavior of cluster tools, where the tool architecture, process module populations, and scheduling algorithms add further dynamics to tool behavior. We have integrated reduced-order process models, reflecting dynamic unit process simulations, with discrete event simulations of cluster tool performance to enable co-optimization of process recipes, cluster tool configurations, and their scheduling algorithms. Finally, we have incorporated these integrated models into factory-level operational models to facilitate the evaluation of factory-level performance as a function of process, equipment, and logistics choices. These simulation strategies seem attractive in terms of their ability to represent dynamics, from continuous parameter dynamic recipes at the unit process level, to discrete-event dynamics associated with scheduling and throughput at the factory level.


Dynamic Simulation: Guiding Manufacturing from Process

Mechanisms to Factory OperationsG. W. Rubloff

Director, Institute for Systems ResearchProfessor, Materials Science & Engineering, and Electrical & Computer

EngineeringUniversity of MarylandOUTLINE

• Motivation• Dynamic simulation:

– Unit process & factory infrastructure systems level (continuous parameter systems)

– Cluster tool & factory operations levels (discrete event systems)

• Heterogeneous simulation environments for optimization and control

• Opportunities


Acknowledgements

• Dynamic equipment and process simulation– G. Lu, B. Levy, A. Rose– N. Gupta, M. Bora– F. Shadman (U. Arizona)

• Integrating continous and discrete dynamic systems– J. W. Herrmann– L. Henn-Lecordier– S. Marcus– M. Fu

• Other– R. Adomaitis– K. Jensen (MIT)– M. Kushner (U. Illinois)

• Support– National Science Foundation– Semiconductor Research Corporation– Sematech


Motivation

• Modeling & simulation are basis for efficient manufacturing enterprises => virtual manufacturingvirtual manufacturing

• Dominant modeling and simulation strategies– Process technology – molecular-scale chemistry/physics for rates,

microfeature profile evolution– Equipment technology – steady-state computational fluid dynamics,

plasma behavior– Factory operations – discrete event simulation, optimization/control

algorithms (including cluster tool behavior)

• Limitations– Dynamics in simulation largely limited to logistics at factory and cluster

tool levels– Dynamics of equipment & process rarely treated, though crucial for unit

process metrics (throughput, consumables use, ESH, …)– Equipment & process dynamics should carry forward into consequences

at factory level


Process & Equipment Modeling

Multiscale modelingMultiple length scalesChemistry & physics have

consequences at multiple length scales

Process models available in numerous cases

FundamentalEmpirical

Dynamics treated at atomic and microfeature scales

BUT…Dynamics rarely treated at

equipment levelRTP the exception

Control system dynamics dependent on equipment transient behavior

Multiscale Multiscale modelingmodelingMultiple length scalesChemistry & physics have

consequences at multiple length scales

Process models available in numerous cases

FundamentalEmpirical

Dynamics treated at atomic and microfeature scales

BUT…Dynamics rarely treated at

equipment levelRTP the exception

Control system dynamics dependent on equipment transient behavior

Atomic Scale (1 nm)Material & interface propertiesSurface & gas phase chemistryEnergy-enhanced reaction

reactant

product

Microfeature Scale (1 um)

3-D profile shapeProcess chemistry & physicsReactant flux distribution

Tool Scale (1 m)UniformityLoading & microloadingPattern factorEquipment design & architecture


Dynamics Through the Process Cycle

RTCVD polySi Deposition from SiH4Real-time mass spectrometry

Gas On Lamps OnGas Off

Lamps Off

SiH2+

H2+

650oC

750oC

0 25 50 75 100 125 150

10-11

10-10

10-9

10-8

10-7

10-6

10-5

10-4

10-3

Part

ial P

ress

ure

(arb

. uni

ts)

SiH4 reactant depletion

H2 reaction product

Time (sec)

RTCVD process cycleEstablish reactant gas pressuresHeat wafer rapidly (lamps)Deposit film at elevated pressure/temperatureCool wafer and pump out gases


Overheadtime

RawProcess

time

Dynamics Through the Process Cycle

Steady-state process behavior occurs only during raw process time

Significant overhead timeoverhead time accompanies process cycle:overhead time = process cycle time – raw process time

Dynamics of process and equipment through the process cycle determines total process cycle time

Total process cycle time is the critical throughput metric for factory level logistics performance and optimization

RTCVD polySi Deposition from SiH4Real-time mass spectrometry

Process cycle time

Gas On Lamps OnGas Off

Lamps Off

SiH2+

H2+

650oC

750oC

0 25 50 75 100 125 150

10-11

10-10

10-9

10-8

10-7

10-6

10-5

10-4

10-3

Part

ial P

ress

ure

(arb

. uni

ts)

SiH4 reactant depletion

H2 reaction product

Time (sec)

RTCVD process cycleEstablish reactant gas pressuresHeat wafer rapidly (lamps)Deposit film at elevated pressure/temperatureCool wafer and pump out gases


Continuous parameter systems for dynamic process & equipment

modeling


Dynamic Simulatorfor RTCVD PolySi

Second LevelCompound Block

Second LevelCompound Block

Multi-level StructureMulti-level Structure

Visual Solutions, Inc.

Film Thickness (A)Film Thickness (A)

QMS Partial Pressures

Ar, SiH4, H2

QMS Partial Pressures

Ar, SiH4, H2

wafer T (oC)growth rate

(A/sx10)

wafer T (oC)growth rate

(A/sx10)

Time-dependent behavior of equipment,process, sensor, and control systemthrough process cycle


Dynamic Simulator for RTCVD PolySi

Sensors and Control System

Sensors and Control System

Gas Flow• Vacuum chambers• Mass flow controllers• Pumps, valves• Conductances, volumes• Partial and total presssures• Pressure control system• Viscous/fluid flow

CVD Reaction• Gas phase transport• Boundary layer transport• Surface-condition-dependent

reaction rates - surface kinetics

Manufacturing Process Efficiency

• Cycle time• Consumables

volume• Energy consumption

Environmental Assessment• Gaseous emissions• Reactant utilization• Power consumption• Solid waste

partial pressurestemperatures

rates

Process Simulator

ManufacturingFoM Simulator

Sensors• Total and partial pressures• Temperatures• Valve and MFC status

Controls• PID controlers for

temperature and pressure• Lamp power output control• Throttle valve positions

Wafer State• Deposition rate• Film thickness• Thickness control system• Product properties -

uniformity, conformality, material quality, topography, reliability

EquipmentSimulator

Heat Flow• Wafer absorptivity,

emissivity• Wafer thermal mass• Wafer radiation, conduction• Wafer temperature• Temperature control system• Process-dependent

absorptivity, emissivity• Convective heat loss in fluid

flow

• Valves, MFC’s vs. time, status• Lamp power vs. time• Overall process timing, conditionsProcess Recipe

• Dynamic simulation can realistically represent complex systems, including

– equipment– process– sensors– control

• Results validated against experiment

– timing/dynamics– subtle systematics

• Numerous applications– systems analysis– optimization – sensor-in-tool models– control system design– training ==> learning

• Platforms commercially available (Windows)

• Exploit rapidly growing software base


Flow Rate Dependence of Mass Spec Sensor Signal

Mass spec H2 signal during polySi RTCVD at 750 oC, 5.0 torr SiH4/Ar for 40 sec

-10

TIME (sec.)0 25 50 75 100 125 150

0.0

5.0x10-11

1.0x10-10

1.5x10

2.0x10-10

2.5x10-10

1000 sccm

500 sccm

200 sccm

H2

QM

S Si

gnal

(am

p.)

__o__o__o___ Experimental___________ Simulation

Mass spec sensitive to reactor flow rate at constant pressure

Dynamic simulator captures flow rate dependence

Sensor is influenced by process dynamics

Consequences:– minor if fixed process recipe– tractable for varying recipes with

simulator available

System dynamics introduces complexity in sensor response

SensorSensor--inin--tool modeltool modelnot just a sensor model


Unit Process Optimization for Manufacturing & Environment

Pressure (torr) at which heating begins

Pressure (torr) at which heating begins

Constant flow rate300 sccm

Constant temperature650o C

A A BB

A: start gas flow and heating simultaneouslyB: start heating after gas flow established

0 2 4 6 80

10

20

30

40

50

1000 sccm600 sccm450 sccm300 sccm200 sccm

100 sccm

SiH

4U

tiliz

atio

n (%

)

0 2 4 6 80

50

100

150100 sccm

200 sccm300 sccm450 sccm600 sccm

1000 sccm

Proc

ess

Cyc

le T

ime

(sec

)

0 2 4 60

10

20

30

40

50

700°C

0 2 4 60

20

40

60

80

100

650°C

Proc

ess

Cyc

le T

ime

(sec

)

650°C

750°C

SiH

4U

tiliz

atio

n (%

)

700°C

750°C

SiH4 UtilizationEnvironmentManufacturing

Process Cycle TimeManufacturing

Dynamic, continuous parameter simulation ��Optimization for desired utility functionsInnovation in process recipes


Hardware-Software CoDesign

Integrate equipment state signals with chemical process sensor signals

Validate dynamic simulator against experimentImplement new tool controller

Use dynamic simulator to debug and enhance controller software

Integrate equipment state signals with chemical process sensor signals

Validate dynamic simulator against experimentImplement new tool controller

Use dynamic simulator to debug and enhance controller Use dynamic simulator to debug and enhance controller softwaresoftware

Ulvaccontroller

W CVD Reactor

Centralwafer

handler

W CVD Reactor

massspec

Pumpsystem

Loadlock

PC LabView

xj (t)

Ulvac ERA-1000W CVD cluster tool

PC VisSim

Dynamictool

simulator

NewBrooks

controller

Controller operates real tool

Controller operates virtual tool (simulator)

Equipment state: valve status, pressures, flows, temperatures,

...

Process and wafer state: mass spec chemical sensing


Impurity Concentrations in Liquid Source Delivery (w/ Motorola)

100 80 60 40 20 00

100

200

300

400

500

600

700

Impurity Concentrationin Delivered Gas (ppm)

Experimental Data for a20000 lb. Tank (DuPont)

liquid-dry point

total pressure (psi)

% of Initial Content Remaining

Impu

rity

Con

cent

ratio

n (p

pm)

in D

eliv

ered

Gas

N Fx dtBout

Bg= �

GasPhase

LiquidPhase


Impurity Concentration Delivery Profile vs. Source Temperature

Impu

rity

Leve

l in

Gas

(ppm

)

% of Initial Content Remaining100 80 60 40 20 0

0

100

200

300

400

500

600

700

800

900

1000

Changes in the Gas Phase Impurity Level (ppm) as the Cylinder is emptiedat Various Cylinder Temperatures.The Initial Total Impurity Level in theLiquid is 100 ppm.

40oC

-20oC

-10oC

0oC

10oC

20oC30oC


WaterSim, v/3.0 Education Module• Collaboration with F. Shadman, Director, NSF ERC for Environmentally Benign

Semiconductor Manufacturing, U. Arizona• Physically based simulator in learning environment provides practical experience• Explore design parameters, analyze/visualize data, experimental history-keeping

B. Levy et al


Water Recycling Simulator for Engineering & Control System Design

• Dynamic system visualization and analysis for ESH metrics & upset response• Complexity approaching a full chemical plant

B. Levy et al


Dynamic Simulator Complexity

• 30,000 Vissim elements; reverse osmosis example


Dynamic Network Reconfiguration

• Prototype for simulator network reconfiguration from graphic user interface• Generalized approach to network development for dynamic simulation• Choose UPW treatment processes and connect with data “wires”• Automatically generates underlying network model in simulation engine

B. Levy et al


Manufacturing Training

Equipment and process training based on dynamic simulation

EquiPSim:Equipment and Process

Simulation

www.isr.umd.edu/CELS/


EquiPSim Learning Modules(Equipment and Process Simulation)

Physics-based dynamic simulation

Active learning through exploration, anytime, anywhere

Powerful learning aidesTightly coupled guidanceLearning historiesDistance collaboration &

consultation

Extendible authoring architecture

www.isr.umd.edu/CELS/


Discrete event systems for operations modeling


Single Wafer W CVD Cluster Tool

Example Tool ConfigurationW CVD W CVD

Orient &Degassing Load Lock

Lot Makespan vs. Pressure(at different temperatures)

900

950

1000

1050

1100

1150

1200

1250

1300

1350

64 68 72 76 80 84 88 92 96

Pressure (torr)

Lot M

akes

pan

(sec

onds

)

T = 448

T = 464

T = 484

T = 492

Results using:• Cluster tool simulator with scheduling capability (L. Shruben)• W CVD Process RSM linked to cluster tool simulator

Lot process time (makespan) vs. pressure (at different temperatures)


3-stagecluster tool3-stage

cluster tool

Cluster Tool Simulator

Input timesInput times

Lot process time & utilization

Lot process time & utilization

Simulation results

Simulation results


05

10152025303540

CT1-1 CT1-2 CT2-1 CT2-2 CT1-1-1 CT1-2-2 CT2-2-1 CT2-2-2

Optimizing Cluster Tool Scheduling

Search algorithms in our independent cluster tool simulator can find optimal and near-optimal operations sequences.

These algorithms can give substantial reduction (to 40%) in lot processing time compared to conventional dispatching rules, which are common in industrial practice (Shin and Lee, SMOMS, 1999).

J.W. Herrmann and M.-Q. T. Nguyen, “Improving cluster tool performanceby finding the optimal sequence of wafer handler moves,” in preparation for J. Scheduling. ISR Technical Report 2000-3.

Category Move & Raw processexchange time time

Short 1-10 secs. 20-40 secs.Medium 10-20 secs. 10-20 secs.Long 20-40 secs. 1-10 secs.

Percent Reduction in Total Lot Processing Timefor Optimized Sequence over Dispatching Rule

Percent Reduction in Total Lot Processing Timefor Optimized Sequence over Dispatching Rule


Factory Operations

• Discrete event simulation for entire factory – e.g., Factory Explorer

• Inputs– Process sequence, # tools per step, time per step, re-entrant flow,

scheduling algorithms, …• Outputs

– Throughput, cycle time, WIP, …

•• Huge impact on manufacturing costHuge impact on manufacturing cost– Essential everywhere


Continuous parameter and discrete event systems

see L. Henn-Lecordier’s talk MS-ThA5, Thursday 3:20pm, Room 304see L. Henn-Lecordier’s talk MS-ThA5, Thursday 3:20pm, Room 304


Integrating Process & Operations Models

Enterprise management

Full factory operations

Module (subfactory) integration

Sector operations

Process RSM’s

Reactor- & feature-scale models

Experimental results

Operations models => manufacturing performance

Process models => technology performance

Relationship Relationship not not systematically systematically

analyzedanalyzed Dynamicsimulations

NSF/SRC programM. FuS. MarcusJ. W. HerrmannG. W. Rubloff


Novellus Multistep/Multiwafer Process Module for W CVD

Sputter Clean /Liner Dep

EntranceLoad Lock

ExitLoad Lock

Transfer point

Nucleation

Growth

Growth

Growth

Growth

Lot process time (makespan) vs. growth temperatureat different pressures

492Temperature (C)

0

500

1000

1500

2000

2500

3000

448 452 456 460 464 468 472 476 480 484 488

Tota

l tim

e (s

ec)

P = 68P = 80

P = 92

Results using:• Deterministic cluster tool simulator (Excel)• W CVD Process RSM linked to cluster tool simulator


Alternate:replace by compact model

HSE Modeling Architecture

TiN PVDProcessModel

W CVDProcessModel

Factory Model(discrete event simulator)

Step Time (min) # tools…TiN PVD liner 1.5 2Via 1 W CVD fill 2.4 3CMP 1.9 2Clean 0.8 2Oxide 2 CVD 2.3 3P/R apply 0.7 1Litho 3.5 3P/R remove 0.7 1TiN PVD liner 1.5 2Via 2 W CVD fill 3.2 3CMP 2.1 2Clean 0.8 2Oxide 3 CVD 2.9 3P/R apply 0.7 1Litho 3.2 2…

TiN PVD cluster tool# process chambers 2Scheduling algorithm pushOD time 15 secRobot move time 6 sec

W CVD cluster tool# process chambers 3Scheduling algorithm pullOD time 12 secRobot move time 5 sec

TiN PVDThickness 30 nmPressure 80 torrPower 600 WPumpdown time 2.5 min

Via2 W CVDThickness 270 nmPressure 80 torrTemperature 470 CPumpdown time 2 min

Oxide 3 CVDThickness 30 nmPressure 80 torrPower 500 WPumpdown time 1.9 minOD time 9.6 secRobot move time 4.5 sec

User Interface

W CVDCluster

ToolModel

TiN PVDCluster

ToolModel

Oxide CVDProcess/Equipment

Model/Simulator

Models and SimulatorsContinuous & discrete-eventStatic and dynamicEmpirical and physics-based


FactorySimulator

Cluster ToolSimulators

Heterogeneous Simulation Environment (HSE)

HSE integrates:• Process models• Cluster tool models• Factory operation models

HSE integrates:• Process models• Cluster tool models• Factory operation models

Inputs:Process input parametersCluster tool configuration Equipment overhead timeFactory resources Changes in technology & product mix

Outputs:Process module and cluster tool efficiencyFactory throughput, cycle timeSensitivity of input parametersFuture: optimization, cost-of-ownership, yield, risk,

reduced-order model generation

USER GROUP:process, equipment, & operations engineers

User Interface

Lot ProcessTimes

Throughput,Cycle Time

Raw ProcessTimes

ProcessParameters

ProcessSimulators

SimulationSupervisor

J. W. HerrmannG. W. Rubloff

Processengineer

Equipmentengineer

Operationsengineer

see L. Henn-Lecordier’s talk MS-ThA5, Thursday 3:20pm, Room 304see L. Henn-Lecordier’s talk MS-ThA5, Thursday 3:20pm, Room 304


Heterogeneous Simulation Environment

Processrecipe

Processrecipe

FactorysimulationFactory

simulation

Cluster toolscheduling

Cluster toolscheduling

Sensitivityanalysis

Sensitivityanalysis

Cluster toolconfigurationCluster tool

configuration


• Dynamic simulation systems can be– Vertically integrated from unit process to factory metrics– Hybrid, including both continuous parameter and discrete systems character– Physically accurate with regard to transient and control systems response– Usable and valuable to a broad community

Discrete event systems

Factory operations

Continuous parameter systems

Unit process & equipment

Factory infrastructure systems

Integrated/cluster tools

Factory sectors/subsystems

Factory metrics


• Exploiting dynamic simulation– Constructed from mechanistic or empirical models– Emphasizes key manufacturing metrics– Both continuous and discrete parameter systems– Build on commercially available software platforms– Integrate with effective user environments

• Building hierarchical modeling structures– Enables system-level evaluation– Reveals vertical interactions in system behavior

o E.g., impact of unit process changes on factory operations– Model reduction methods

o Capture and integrate knowledge at multiple levelso Keep system-level models manageable

– Integrating software supervisorso Organize and execute different simulation levels which are linked

– Effective user environmentso Graphical interfaces and experimentation tools to facilitate engineering design

and analysis

dynamic simulation: guiding manufacturing from … · avs natl symp, oct 2000 avs00inv.dynsim.ppt...

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