Workshop - Statistical methods applied in microelectronics
13. June 2011, Catholic University of Milan, Milan, ItalyApproaches 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, Germany
2) Infineon Technologies Dresden GmbH, Königsbrücker Straße 180, 01099 Dresden, Germany 3) austriamicrosystems AG, Tobelbader Straße 30, 8141 Unterpremstaetten, Austria
Outline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturingf Framework for implementation of VM and PdM
● VM and PdM application examples
f Structured approach for VM and PdM development
f Prediction of etch depth by VM
f PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
Motivation
● Objective European project “IMPROVE”: IMPROVE European semiconductor fab competitiveness and efficiency
f
processes reproducibility and qualityf
efficiency of production equipmentf
shorten cycle times● Approach: Development of novel methods and algorithms for virtual metrology (VM) and
predictive maintenance (PdM)
● Challenges:
f
Implementation of new control paradigms in existing fab systemsf
Reusability of developed solutions amongst the nine IC manufacturers’ fabs gathered in IMPROVEOutline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturing
f Framework for implementation of VM and PdM
● VM and PdM application examples
f Structured approach for VM and PdM development
f Prediction of etch depth by VM
f PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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
Place of execution of VM in a process flow
Key requirements of a VM system
●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
VM module components
Concept of PdM for IC-manufacturing
Current situation of scheduled maintenance in semiconductor manufacturing
● Maintenance scheduled based on elapsed time or fixed unit count usage ● Maintenance frequency depends on:
f process engineer’s experience
f known wear out cycles of certain parts of the tool
● PdM considerations based on worst case scenarios to avoid unscheduled downs
Ideal maintenance strategy - “Run to almost fail”
● Predictive maintenance aims at replacing/repairing an equipment part when it has nearly reached its end of life
PdM workflow utilizing Bayesian Networks
PdM objectives, benefits and key requirements
PdM 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
Outline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturingf Framework for implementation of VM and PdM
f Structured approach for VM and PdM development
● VM and PdM application examples
f Prediction of etch depth by VM
f PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
● Abstraction from existing fab infrastructures
applying UML as project standard
● Adoption of architectures following SEMI and
SEMATECH, including
existing SEMI standards (interface A, B)
f Consideration of user requirements for fab-wide master framework
f Develop component- and service-based models for VM and PdM
Concept for a generic VM and PdM implementation
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
UML description of the EE system and of a generic VM/PdM module
Outline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturingf Framework for implementation of VM and PdM
● VM and PdM application examples
f Structured approach for VM and PdM development
f Prediction of etch depth by VM
f PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
Structured approach for VM and PdM development
Phases in VM and PdM development as adapted from the Cross-Industry Standard Process for Data-Mining (CRISP-DM)
Outline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturingf Framework for implementation of VM and PdM
● VM and PdM application examples
f Structured approach for VM and PdM development
f Prediction of etch depth by VM
f PdM for prediction of filament break-down in ion implantation
● Conclusions and outlook
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 step
3. Conditioning step
4. 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
status first process step
status fourth process step
Data understanding and preparation
● Data analysis and understanding
f Step and summary data collected over three months for two slightly different etch recipes performed on four chambers
● Data reduction step
f Derivation of 8 subsets of data with 130 predictor/target
● Predictor selection by rule
based elimination of
correlated variables
f Prioritization of data sources, e.g. logistic, equipment, and sensor data
Modeling approach - overview
Modeling approaches
●Stepwise linear regression-algorithm
f Identification of a small set of predictor variables
f Inclusion of model on FDC system
●Time-series neural network
f Model development for variables selected in stepwise linear
regression
●Test of models on new data collected on the FDC system
Predictor selection using stepwise linear regression
Modeling
●Predictor selection and model development using bagging, and repeated stepwise linear regression
Result
●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
Prediction capability of different models
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
Assessment of model adaptation
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 model Regression model
Outline
● Motivation
● Virtual metrology (VM) and predictive maintenance (PdM)
f Concept of VM and PdM for IC-manufacturingf Framework for implementation of VM and PdM
f Structured approach for VM and PdM development
● VM and PdM application examples
f Prediction of etch depth by VMf PdM for prediction of filament break-down in ion implantation
Ion implantation overview
Implantation process
● Different ions (B, BF2, P, As, Sb,…) for doping of certain chip regions
● Ion source:
1. Electron generation from heated cathode
2. Creation of ions in process gas through collisions with accelerated electrons
3. 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!
PdM modeling
Predictor 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
p(NextBreakdown=State1|Fil-I,Ext-I,Arc-I,GAS)
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
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 f Development may follow a structured approach
f Data quality and preparation is of key importance
f 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
Acknowledgment
● 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)
