A background presentation of the history behind fingeprint

1. Accelerating Manufacturing Productivity
From Enhanced Equipment Quality
Assurance (EEQA) to Equipment
Health Monitoring (EHM)
Gino Crispieri (PMP)
Senior Member of the Technical Staff
ISMI
gino.crispieri@ismi.sematech.org
copyright 2010 Advanced Materials Research Center, AMRC, International SEMATECH Manufacturing Initiative, and ISMI are servicemarks of SEMATECH, Inc. SEMATECH,
and the SEMATECH logo are registered servicemarks of SEMATECH, Inc. All other servicemarks and trademarks are the property of their respective owners.

2. EEQA - EHM Project Timeline
EEQA Data EEQA Data
Usage Usage Software
Application
Demonstration
EEQA Common
EEQA Component
Project Templates
Kickoff
EHM
2009 2010 2011 2012
EEQA
EEQA General Equipment
Guidelines V1.0 Implementation
Guidelines
EEQA
Equipment
Assessments
April 19, 2012

3. What is EEQA?
Definition
“The enactment of quality control practices and
methodologies to ensure component functionality,
reliability, performance, and traceability for
semiconductor equipment through validation of
supplier’s specified equipment performance values.”
Enhanced Equipment Quality Assurance (EEQA) is a
method that defines a new approach requiring equipment
suppliers and IC makers to share data and collaborate to
achieve the improvements needed for next generation
factories.
April 19, 2012 3

4. EEQA Goals and Objectives
Primary Goal
1 Reliable and consistent verification of equipment base functional capabilities
2 More efficient and rapid equipment’s ramp-up
Reduce Machine-to-Machine or Chamber-to-Chamber difference in terms of
3
equipment base functional capbility
Objectives
1 To reuse collected information for efficient troubleshooting
2 To confirm the base functional capability performance as purchased
To reduce Machine-to-Machine difference in terms of equipment process
3
performance
To encourage more efficient energy / consumable consumption and effective
4
maintenance
April 19, 2012 4

5. EEQA Main Focus
Successful Equipment
Functional Performance Drives Product Quality
Validation
CD
anisotropy
Gas flow as
monitored
expected;
Burst, off-set
drift, valve
defects
checked
While Heater
temperature is power
checked for checked for
proper operation
consistency
APC valve
While pressure
is checked for opening Surface
proper operation checked for morpholog
consistency
y
φ monitored
April 19, 2012 5

6. EEQA Typical Data Collection
Scenario Cases
Verification of functional performance for process condition generation
Examples.- Use of “wave” trace data - very common process parameter data
Validation of process condition generation delay
Validation of accuracy and repeatability to process condition generation instruction
Verification of vacuum evacuation performance between process execution
Example.- Verification of evacuation speed after each sputtering process
execution
Verification of mechanical functional performance
Example.- Verification of valve open/close behavior or robot movement
Verification of variations in performance
Example.- Verification of variations of range of automatic pressure control
capability against accumulative CVD deposition
Verification after maintenance work
Example.- Verification of process evacuation performance from atmospheric
pressure after process chamber maintenance
April 19, 2012

7. Wave Data Characterization
Example
Settling time (ts)
Accuracy +/- %
Recipe
instruction
target
Transient Response
(Vs Max, Vs Min, Vs
Average ）
Rise time (t ｒ
)
Fall time (tf)
Response time
(td)
Recipe － Recipe －
STEP START STEP END
Repeatability analysis of rise time wave signal
April 19, 2012

8. Factory Level EEQA Application
Visualization and
Extraction
("Equipment View",
"Wafer View")
CMP I/F A Tool
1
Supplier
A Tool EEQA
2 EEQA
Data
Data
Loader
Storage
Furnace (Transform)
Raw Tool
Trace data 3
Supplier DB
GEM reports
B
Tool
EEQA
4 MES,
Job
Tool Execution RMS,
Etch Trace
Supplier Tool Other DB
File
C Sys 5
Custom
EEQA
Job
CVD Data Source Definition EEQA
Transformation Analysis
Models Jobs
Tool Supplier Raw
EES Data EEQA Application System
April 19, 2012

9. 2011 EHM Project Timeline
EHM
Equipment Health Monitoring
Data Model Development and Guidance for Key Parameters
Develop Template for
Fingerprint Specification
Develop Use Cases for
Fingerprint
Data Application
Equipment Fingerprint
Methodology
Fingerprint General Guidance
Document
Develop
Contract with Publish
Third Party and Execute Fingerprint Lessons
Supplier Implementation Pilot Learned
Education and Workshops
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
April 19, 2012

10. 2011 Equipment Health Monitoring
(EHM)Project Objectives class DataSourceTypes
«XSDcomplexT ype»
DataSourceModel
 Demonstrate effectiveness of data
+SourceId
1..1 1..1
+RootLocation
«XSDcomplexType»
«XSDcomplexT ype» DataSourceId
DataSourceLocation
«XSDattribute»
+ChildLocation 0..*
+ Name: Path
model usage for fingerprint scenarios
«XSDattribute»
+ Name: Path + Type: DataSourceT ype
+Dictionary
1..1
«XSDcomplexT ype»
DataSourceDictionary
 Create industry guidance showing the «XSDcomplexType»
«XSDattribute»
Descriptor
application and usage of the proposed
- Description: Description [0..1]
- Key: KeyType
- Name: Name
data model for fingerprinting
+Event
0..* +Parameter 0..*
«XSDcomplexT ype» «XSDcomplexT ype»
ParameterDescriptor Ev entDescriptor
string
«XSDattribute» «XSDattribute»
+ DataT ype: ParameterDataT ype «XSDsimpleType» + Category: EventCategoryType [0..1]
+ ScalingFactor: float [0..1] ParameterValueType + SubCategory: EventCategoryType [0..1]
+ Units: ParameterUnits [0..1]
 Create guidance for practical creation «XSDextension»
+ParameterRef
«XSDcomplexT ype»
0..*
and use of equipment health
ParameterRefType
«XSDattribute»
+ Key: KeyT ype
monitoring and fingerprinting of
components using the data model
 Demonstrate standardized methods,
data requirements and management of
equipment health monitoring and
fingerprinting specifications
April 19, 2012

11. 2011 EHM Project Deliverables
 Publish equipment fingerprint method
and data usage scenarios for common
equipment capabilities COMPLETED
 Provide industry guidance to show the
application and usage of the proposed
data model for fingerprinting
COMPLETED
 Publish factory level fingerprinting
general guidance for data model usage
COMPLETED
 Publish Guidance for Fingerprint Data
Model and Key Parameters COMPLETED
 Publish lessons learned from
fingerprinting pilot COMPLETED
April 19, 2012

12. Definitions
 Equipment Fingerprint - a representation of the state
of equipment or equipment component at a particular
point or range in time
 Equipment Fingerprinting - the creation, collection,
and storage of the attributes of an equipment
fingerprint for an application such as equipment or
chamber matching, maintenance requalification,
equipment acceptance, etc.
 Equipment Health Monitoring – the assessment of
equipment performance and condition based on
equipment measurable states, e.g., equipment
fingerprint
April 19, 2012

13. Differentiation Between Data
Mining and Fingerprinting
 Data mining uses mathematical
techniques that are applied to data
stored in the factory database
searching for indicators of problems
or potential issues with the equipment
or processing.
 Fingerprinting is a systematic
approach that uses equipment
component data values to calculate
additional parameters that determine
equipment health and performance.
April 19, 2012

14. Common Component Fingerprint
p-CDV
CMP
Etchers
Component capabilities Plasma control
RF feed capability
capability
specific to equipment
types Eq. Eq.
Common Common
specific specific
part part
part part
Gas feed capability Evacuation capability
Eq. Eq.
Common Common
specific specific Wafer handling
part part
part EFEM capability part capability
Common Eq. Eq.
Common Common
component specific specific
part part
capabilities part part
April 19, 2012

15. Common Equipment Component
Templates Definition and Application
Verification Category
Component Related
Component Property Description Item To Monitor
Property Events
Accuracy Stability Repeatability
Switch Name The logical name of this switch
Atmospheric Verification
Switch
categories sec, Switch
Switch State Time
Switch State Changed status √ √
Gauge Name The logical name of this gauge
The pressure units that will be used
Gauge Units
when pressure data is reported by the
Pressure Time sec,
Gauge State Changed Pressure √ √ √
Vacuum
The logical name of this vacuum
Chamber
chamber
Name EVENT and
Context
Gas Detection
Chamber supports gas detection DATA required
The pressure value below which the data
Vacuum
chamber is considered to be at
for analysis
Threshold
vacuum.
Vent Started Time sec √ √
Vacuum Equipment
Chamber Vent component Vent Time sec,
Completed Pressure √ √ √
capability
Vent Failed Time sec
Vacuum
Start Time sec √ √
Vacuum Vacuum Time sec,
Completed Pressure √ √ √
Vacuum
Failed Time sec
April 19, 2012

16. class DataSourceTypes
«XSDcomplexType»
DataSource
+Child 0..*
«XSDattribute»
+ Path: PathType
+ Type: string
«XSDtopLevelElement»
RootDataSource
1..1
+Dictionary
Equipment
«XSDcomplexType»
DataSourceDictionary Data Model
«XSDcomplexType»
Transformation
Descriptor
«XSDattribute»
+ Description: string [0..1]
+ Key: KeyType
+ Name: NameType  Equipment data
+Parameter +Event
model defines
0..*
«XSDcomplexType»
0..*
«XSDcomplexType»
and correlates
ParameterDescriptor Ev entDescriptor
event and data
«XSDattribute»
+ DataType: ParameterDataType
+ ScalingFactor: float [0..1]
«XSDattribute»
+ Category: string [0..1]
+ SubCategory: string [0..1]
available in the
+ Units: UnitSymbol
equipment (raw
data) to monitor
+Trigger 0..*
«XSDcomplexType»
equipment
ParameterValueType
«XSDattribute»
health and track
+ Key: KeyType
+ Value: string
performance
April 19, 2012

17. From UML to XML
Tool Transformation
 Allows device maker to
map equipment raw
data into its own
hierarchical structure
 Helps organize OEM-
provided equipment
data and reports into
structures that are well
understood by device
maker personnel
 Fixes problems
encountered in the
definition of supplier’s
own view of the
equipment and data
April 19, 2012

18. Example of Equipment Data and Event Definition
using Fingerprint Transformation
April 19, 2012

19. Start
Perform
Fingerprint Use Cases
baseline test
Being Considered
Determine
critical
parameters
 Equipment Acceptance and Qualification
Critical
parameters
– Functional capability
identified?
– Process capability
Perform
 Chamber-to-Chamber Matching
Chamber
adjustments
 Machine-to-Machine Matching
Retest
 Equipment Qualification After Maintenance
– Maintenance & part replacement
Matching
accopmplished?
Document and
store results
End
April 19, 2012

20. End-to-End Equipment Data Flow
DataReport DataSource
.xsd .xsd
(schema) (schema)
SECS
equipment SECS Log • Fingerprint
Data Converter Equipment templates
(SML)
Transformation
Models
Applied Equipment
Applied Materials Data Equipment
Materials
equipment Nexus Reports Data Importer Data storage
Data (SML) File Converter
Equipment Equipment
Data
(CSV)
Export File
Converter
• Trace data
• Event data
• Context data
20
April 19, 2012

21. Fingerprint Pilot
April 19, 2012

22. Deliverable Schedule
ISMI provides User Scenarios and Data Model Schema 5.1 09/16/2011
Files
Cimetrix refines User and OEM scenarios, develops data 5.2 09/30/2011
models for key components for ion implanter equipment
ISMI approves final scenarios and selected equipment 5.3 10/7/2011
components
Cimetrix implements OEM scenarios and demonstrates 5.4 11/15/2011
fingerprint capabilities for selected components in its
common software platform
Cimetrix implements User defined scenarios and 5.5 12/15/2011
demonstrates fingerprint capabilities for selected
components in its common software platform
Cimetrix provides lessons learned report and suggests 5.6 12/20/2011
future work
April 19, 2012

23. SOW Pilot Objectives
 The objectives of this fingerprint pilot
implementation project include:
1. Selecting and refining a set of key use cases that demonstrate
the importance and value of the fingerprint methodology and
the technology associated with it for ISMI members and their
equipment suppliers
2. Piloting a consensus approach to fingerprinting and
validating an instance of the support technologies required by
this approach
3. Demonstrating the resulting software with real production
equipment data with an OEM participant to realize the
selected use cases
4. Summarizing the lessons learned for ISMI stakeholders, with
special focus on the equipment data availability requirements
April 19, 2012

24. Fingerprinting data flow: extraction,
transformation and storage process
Visualization and
Data Source Application Usage
CMP
Transformation ("Equipment View",
GEM "Wafer View“, etc.)
CMP Models
Custom
CMP
GEM Extract Transform Store
Storing the
EDA data
Furnace
Data
Data Storage
Custom Raw Transformation
Furnace data
reports
GEM
Tool Fingerprint
EDA MES,
Execution RMS,
Other DB
Etch
Fingerprint
CVD Custom Definition
Fingerprint
Analysis
GEM Raw
Data
Treat
CVD
Fingerprint Application System
04/19/1224

25. Equipment Health Monitoring
Multi-Year Plan
Equipment Health Monitoring Overall Project
Fingerprint Method and Usage Fingerprint Equipment Supplier EHM Factory Level Project Goal: EHM
Deployment
Scenarios Implementation Guidance Infrastructure Deploymeny in Factories
Supplier guidance for the
Common templates and 3rd Party Supplier and Actual application of
implementation of fingerprint
methodology MC Guidance EHM by industry
methodology
Fingerprint Factory Level Standardization
Pilot 1 (Tool Type 1) Data Infrastructure
General Guidance Maintenance Demonstrate data
requirements and
management of fingerprint
Identification of key functions Modularity and reuse specifications to promote
Supplier demostration of
and capabilities for Proof of Concept of supplier templates equipment reliability, health
fingeprint implementation
fingerprinting and fab wide models assessment, improve fleet
utilization, and reduce cost
Fingerprint Data model and of ownership
Pilot 2 (Tool Type 2) Data Manipulation
Key Parameters
Improve equipment
traceability lifecycle
Definition of selected Supplier demostration of
Proof of Concept
fingerprint models fingeprint implementation
Data availability for multiple
Visualization and applications (e.g., FDC,
Standardization
Reporting PHM, EPT, SPC, etc.)
Modularity and reuse of supplier
Proof of Concept
templates and fab wide models
2011 2012 2013 2014 Total Project $$
April 19, 2012

26. Value Proposition for Suppliers
and Device Makers
Proposition Suppliers Device Makers
Improved tool acceptance Shorter acceptance time
Increased tool availability Potential
 Decrease MTTR and increase MTBF competitive OEE improvement
advantage
Improve effective throughput
 Increase visibility of equipment Reliable and Flexibility to understand
component behavior predictable equipment behavior and
 Increase confidence in process equipment process recipe use
performance
Improved field service
Field service Improved equipment
 Improved diagnostic data and performance availability and field
expertise to resolve problems improvement service support
April 19, 2012