![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](https://image.slidesharecdn.com/eeqaehmhistoryshort-13348353062109-phpapp01-120419063828-phpapp01/85/EEQA-and-EHM-Background-10-320.jpg)
![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](https://image.slidesharecdn.com/eeqaehmhistoryshort-13348353062109-phpapp01-120419063828-phpapp01/85/EEQA-and-EHM-Background-16-320.jpg)
EEQA and EHM Background
- 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