data-driven architectures
DESCRIPTION
Slides of presentation given by Axcelis Technologies and Cimetrix at AEC/APC 2008 in Salt Lake CityTRANSCRIPT
1
Data-Driven Tool Architectures The Gateway to Quality Equipment Data
Authors:
Glen Gilchrist, Senior Systems Engineer,
Axcelis Technologies
Larry Bourget, Director Product Management,
Axcelis Technologies
Kourosh Vahdani, Vice President Global Services,
Cimetrix, Inc.
2
The Challenge
A higher quality and quantity of data is necessary to achieve optimized equipment and process control performance, resulting in higher wafer yield and equipment reliability.
Current tool architectures do not allow for high frequency data collection from lowest levels of the tool.
3
What is Tool Control?Supervisory Control
User Interface ServerUser ManagementJob ManagementStandard / Custom UIsData VisualizationData AnalysisSchedulerConfiguration ManagementAlarm ManagementRecipe ManagementStatus Message LoggingFactory Automation
Equipment Control
EFEMLoadportsLoadlocksTransfer ModuleProcess ModuleSub-systemsDevice Logic ModulesI/O ServicesI/O Level Simulation
Implemented Standards
E5 – SECSE30 – GEME37 – HSMSE39 – OSSE40 - PJME94 - CJME87 - CMSE90 – STS
E95 – UIE116 - EPTE84 – AMHS PIOE99 – Carrier IDE120 – CEME125 – EqSDE132 – CA&AE134 – DCM
Integra RS™
4
What is Data Distribution?
• Process Performance
• Process Parameters
• Equipment Parameters
Internal Interfaces External Interfaces
Internal Tool Data
Database(TDI)
SECSGEM 300 Interface
EDA InterfaceASCII
Data File
Process Module(s)
I/O Controller(s)
EFEMVacuum & Internal Hardware Modules
On-Tool AnalysisGUI
5
Old Way…..
Database(TDI)
SECSGEM 300 Interface
EDA Interface
ASCII Data File
SequencerGUI On-Tool Analysis
Process Module(s)
I/O Controller(s)
EFEMVacuum & Internal Hardware Modules
6
Data-Driven Architecture
Database(TDI)
SECSGEM 300 Interface
EDA Interface
ASCII Data File
Process Module(s)
I/O Controller(s)
EFEMVacuum & Internal Hardware Modules
SequencerOn-Tool Analysis
Data Distribution Framework
GUI
Tool Services
7
Data-Driven Architecture Enables Advanced Features
High-speed, high quality diagnostic and processing data are fed to factory interfaces, an on-tool database, and the GUI to optimize productivity.
This data speed and quality is required for: Equipment Data Acquisition (EDA)/ Interface A Advanced Process Control (APC) Fault Detection & Classification (FDC) Run-to-Run Control (R2R) Predictive & Preventative Maintenance (PPM) Enhanced Equipment Quality Assurance (EEQA) Enhanced Equipment Quality Management (EEQM)
8
Integra Using CIMControlFramework™
Data-Driven architecture provides high-speed access to higher quality and quantity data
Simple interfaces ensure extensibility for future enhancements
Uses the latest Microsoft™ .NET technology Uses WCF and SOA for scalability and
distribution Use Cases…
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Analysis of Integra Development Data for PPM
High quality data can be analyzed from a local database using a commercial package
Data can be published at a high throughput via Interface A
Data is also available via traditional
SECS interface
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Data Analysis Capabilities Process Control FDC / PPM and Plasma Characteristics Vacuum System Characteristics (“health”) MW Power and Source Gas Box and Manifold Chamber, Chuck and Pin Lifter Transfer Module and Load Locks Wafer Handling and Robots
Use Cases EP signal charting and statistics Plasma ignition time, plasma ignition retry counter Preheat pressure control
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Time
3 M
inu
tes
25 W
afer
s6
Ho
urs
2000
Waf
ers
10 S
eco
nd
sS
ing
le
Pro
cess
Process Control: EP Signal Charting
Initial process control provided through EP signal matching Drill down to investigate out of specification cases
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Overlay signals or tool operating parametersTimes
Sig
nal
In
ten
sity
Par
amet
er V
alu
e
Process Control: EP Signal Charting
13
Track Vital Statistics provide warning at low (or high) end of specification provide alarm for out of specification condition
Process Control: EP Signal StatisticsS
ign
al A
rea
Sig
nal
Hei
gh
t
terminal failure caused process module to error out and shut down
Wafer Number
14
Time from power supply command to plasma detected Delays and multiple retries indicate defective system
Times
MW
Po
wer
an
d P
lasm
a S
ign
al
Daily Statistics Table
Daily Box Plot
Ign
itio
n T
ime
Single Wafer Ignition
FDC / PPM: Plasma Ignition Time and Retry Counter
out of control ignition times
15
Use daily statistics to create time series plot Investigate out of specification and adverse trends
FDC / PPM: Plasma Ignition Time
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Preheat Pressure Control
Setting requires interaction between vacuum and gas supply systems
Daily Statistics Table
Pre
ssu
re, T
orr
Box Plot: showing distributionand outliers
Time Series: showing 2 processes in 2 chambers
Leaky vacuum valve identified
17
Preheat Pressure Control
Setting requires interaction between vacuum and gas supply systems
PM1CH1recipe 1-5
PM1CH2recipe 1-5
PM2CH1recipe 1-5
PM2CH2recipe 1-5
Pre
ssu
re, T
orr
Bar Chart: showing chamberand recipe
PM1CH1 PM1CH2 PM2CH1 PM2CH2
Box Plot: showing variation of preheat pressure around set point
experimental flow control component
18
Leak isolation valve and change in flow control affect preheat pressure
Preheat Pressure Control
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Conclusions
With Integra’s Data-Driven architecture, based on CIMControlFramework, the capability to meet the stringent tool performance and reliability requirements of the future is available today.
As presented in the Use Cases, Process Control, FDC/PPM and Equipment “health” monitoring is possible due to the availability of quality data.
20
Acknowledgement
This work was part of a successful joint development project between Axcelis Technologies and Cimetrix.
Significant contributions were made by both teams, led by Dan Mattrazzo (Axcelis, Project Manager) and Bill Grey (Cimetrix , Director of R&D).
Questions?