Approaches for Implementation of Virtual Metrology and...
Transcript of Approaches for Implementation of Virtual Metrology and...
Workshop - Statistical methods applied in microelectronics13. June 2011, Catholic University of Milan, Milan, Italy
Approaches for Implementation of Virtual Metrology and Predictive Maintenance into Existing Fab Systems
G. Roeder1), M. Schellenberger1), U. Schoepka1), M. Pfeffer1), S. Winzer2), S. Jank2), D. Gleispach3), G. Hayderer3), L. Pfitzner1)
1) Fraunhofer Institute for Integrated Systems and Device Technology (IISB), Schottkystraße 10, 91058 Erlangen, Germany2) Infineon Technologies Dresden GmbH, Königsbrücker Straße 180, 01099 Dresden, Germany3) austriamicrosystems AG, Tobelbader Straße 30, 8141 Unterpremstaetten, Austria
Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
● VM and PdM application examplesStructured approach for VM and PdM development
Prediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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Motivation● Objective European project “IMPROVE”: IMPROVE European
semiconductor fab competitiveness and efficiencyprocesses reproducibility and qualityefficiency of production equipmentshorten cycle times
● Approach: Development of novel methods and algorithms for virtual metrology (VM) and predictive maintenance (PdM)
● Challenges:Implementation of new control paradigms in existing fab systemsReusability of developed solutions amongst the nine IC manufacturers’ fabs gathered in IMPROVE
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Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
● VM and PdM application examplesStructured approach for VM and PdM development
Prediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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VM objectives● Predict post process physical and electrical quality parameters of wafers and/or
devices from information collected from the manufacturing tools including support from other available information sources in the fab
VM benefits ● Support or replacement of stand-alone and in-line metrology operations ● Support of FDC, run-to-run control, and PdM● Improved understanding of unit processes
VM objectives and benefits
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Place of execution of VM in a process flowPlace of execution of VM in a process flow
Key requirements of a VMsystem ●Capability for estimation of the equipment state or wafer quality parameter within predefined reaction time, typically at wafer-to-wafer level●Inclusion of metrology to control and adjust VM prediction and models●Capability for integration into a fab infrastructure and interaction with other APC modules, e.g. run-to-run control
VM key requirements
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VM module componentsVM module components
Concept of PdM for IC-manufacturingCurrent situation of scheduled maintenance in semiconductor manufacturing● Maintenance scheduled based on elapsed time or fixed unit count usage● Maintenance frequency depends on:
process engineer’s experience known wear out cycles of certain parts of the tool
● PdM considerations based on worst case scenarios to avoid unscheduled downsIdeal maintenance strategy - “Run to almost fail”● Predictive maintenance aims at replacing/repairing an equipment part when it has
nearly reached its end of life
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PdM workflow utilizing Bayesian NetworksPdM workflow utilizing Bayesian Networks
PdM objectives, benefits and key requirementsPdM objectives● Predict upcoming equipment failures or events, their root causes and corresponding
maintenance tasks in advance
PdM benefits ● Improved uptime and availability - by reducing or eliminating unplanned failures● Reduced operational cost – by enhanced consumable lifetimes and efficiency of
service personnel● Improved product quality – by eliminating degraded operation and tightening process
windows● Reduced scrap – by maintenance actions before a failure occurs
Key requirements of a PdM system ● Capability for reliable prediction of upcoming equipment failures, root causes and
corresponding maintenance tasks● Capability for integration into a fab infrastructure
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Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
Structured approach for VM and PdM development
● VM and PdM application examplesPrediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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● Abstraction from existing fab infrastructures applying UML as project standard
● Adoption of architectures following SEMI and SEMATECH, including existing SEMI standards (interface A, B)
Consideration of user requirements for fab-wide master frameworkDevelop component- and service-based models for VM and PdM
Concept for a generic VM and PdM implementation
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Definition of VM and PdM as EE applications on a conceptual level
Mapping to existing infrastructures●Consideration of specific user infrastructure●Inclusion of configuration, data analysis, and filter modules as plug-ins● First framework realization available
Architecture for generic VM and PdM implementation
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UML description of the EE system and of a generic VM/PdM module
With contributions from the University of Augsburg
Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
● VM and PdM application examplesStructured approach for VM and PdM development
Prediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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Structured approach for VM and PdM development
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Phases in VM and PdM development as adapted from theCross-Industry Standard Process for Data-Mining (CRISP-DM)
Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
● VM and PdM application examplesStructured approach for VM and PdM development
Prediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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Introduction to the etch process
Trench etch process
● The IT etch defines the active regions
● The process is carried out in four steps: 1. Etching of the organic ARC and nitride layer
(mask open) 2. Conditioning step3. Conditioning step4. Etching of the poly silicon (IT etch)
● Strip of resist and of anti-reflective coating (ARC) by etching in a plasma
● Steps 4 and step 1 are expected to primarily define the etched depth
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status first process step
status fourth process step
final process result
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Data understanding and preparation● Data analysis and
understanding Step and summary data collected over three months for two slightly different etch recipes performed on four chambers
● Data reduction stepDerivation of 8 subsets of data with 130 predictor/target
● Predictor selection by rule based elimination of correlated variables
Prioritization of data sources, e.g. logistic, equipment, and sensor data
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Modeling approach - overview
Modeling approaches
●Stepwise linear regression-algorithm
Identification of a small set of predictor variablesInclusion of model on FDC system
●Time-series neural networkModel development for variables selected in stepwise linear regression
●Test of models on new data collected on the FDC system
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Criteria for model selection
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Predictor selection using stepwise linear regression
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Modeling●Predictor selection and model development using bagging, and repeated stepwise linear regressionResult●Prediction of etch-depth is possible●Predictor selection is unique and independent from selection of sample subset●Prediction capability for sub-sets identical as for training on complete data set
Prediction of etch depth by stepwise linear regression
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Prediction capability of different models
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Prediction capability of stepwise regression and time-series neural network
● Prediction capability of TSNN slightly better than for stepwise regression
● Modeling techniques provide comparable results
Parameter Stepwise regression TSNN
Std. dev. abs. 4.0 nm 3.8 nm
Std. dev. rel. 0.8 % 0.75%
MAE 3.2 nm 3.0 nm
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Assessment of model adaptation
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Capability of prediction after model adaptation on additional FDC test data
● Due to modifications in database, errors occur in VM for test set● Prediction errors are lower for TSNN● Capability for model adaptation can be tested: Comparable adaptation for both
models (MAE: 12 nm); models rebuilding from full predictor set necessary
TSNN modelRegression model
Outline● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)Concept of VM and PdM for IC-manufacturing
Framework for implementation of VM and PdM
Structured approach for VM and PdM development
● VM and PdM application examplesPrediction of etch depth by VM
PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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Ion implantation overviewImplantation process
● Different ions (B, BF2, P, As, Sb,…) for doping of certain chip regions
● Ion source: 1. Electron generation from heated cathode2. Creation of ions in process gas through
collisions with accelerated electrons3. Extraction and acceleration of ion beam
● Degeneration of cathode/heating filament through sputtering
● End of lifetime: breakdown of filament
● Problem: measurement of filament degradation not possible => PdM!
source: http://en.wikipedia.org/wiki/Ion_implantation
PdM modelingPredictor parameters
● Different currents and voltages related to ion source, power
● Gas flow rates, source pressure, time
Modeling● Method: Bayesian Networks with
soft discretization (discretization required for non-Gaussian data)
● Model learning with real production data (noisy, different recipes/ions)
● Soft discretization used for broadening of data basis and reduction of quantization error
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p(NextBreakdown=State1|Fil-I,Ext-I,Arc-I,GAS)p(NextBreakdown=State1|Fil-I,Ext-I,Arc-I,GAS)
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PdM Results
Data● 7 maintenance cycles for training● 2 maintenance cycles for test● Simple model with 4 predictors
Prognosis● Calculation of probability not
to fail within the next 50h based on actual data
● Can be directly used as filament health factor
Further investigations● Improved pre-processing for more robust prediction (considering influence of
recipe changes for outlier prevention)
Conclusions and outlook
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Achievements● Common architecture to integrate VM and PdM into the different existing fab
systems developed● Software for implementation of VM and PdM modules in fab environments
available● VM and PdM modules for important fabrication steps demonstrated
Development may follow a structured approach
Data quality and preparation is of key importance
Prediction quality but also other properties (e.g. model adaption, automation) are key to model selection
Next steps in IMPROVE● Refinement of VM and PdM algorithms● Continued testing of VM algorithm for etch-depth prediction
Acknowledgment
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● This research is funded by the German Federal Ministry of Education and Research (BMBF) and the European Nanoelectronics Initiative Advisory Council (ENIAC)
● The work is carried out in the ENIAC project “IMPROVE” (Implementing Manufacturing science solutions to increase equipment PROductiVity and fab pErformance)