11/20/00 1 AVS00inv.dynsim.ppt AVS Natl Symp, Oct 2000 G. W. Rubloff ã2000 Dynamic Simulation: Guiding Manufacturing from Process Mechanisms to Factory Operations G. W. Rubloff Director, Institute for Systems Research Professor, 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.
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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.
11/20/00 2AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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)
11/20/00 6AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 7AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 8AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
Continuous parameter systems for dynamic process & equipment
modeling
11/20/00 9AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 10AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 11AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 12AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 13AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
Process and wafer state: mass spec chemical sensing
11/20/00 14AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 15AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 16AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 17AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 18AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
Dynamic Simulator Complexity
• 30,000 Vissim elements; reverse osmosis example
11/20/00 19AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
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
11/20/00 20AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
Manufacturing Training
Equipment and process training based on dynamic simulation
EquiPSim:Equipment and Process
Simulation
www.isr.umd.edu/CELS/
11/20/00 21AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
EquiPSim Learning Modules(Equipment and Process Simulation)
Physics-based dynamic simulation
Active learning through exploration, anytime, anywhere
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.
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
11/20/00 32AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
Heterogeneous Simulation Environment
Processrecipe
Processrecipe
FactorysimulationFactory
simulation
Cluster toolscheduling
Cluster toolscheduling
Sensitivityanalysis
Sensitivityanalysis
Cluster toolconfigurationCluster tool
configuration
11/20/00 33AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
• 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
11/20/00 34AVS00inv.dynsim.pptAVS Natl Symp, Oct 2000G. W. Rubloff �2000
• 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