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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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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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Structured approach for VM and PdM development

● VM and PdM application examplesPrediction of etch depth by VM



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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








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Phases in VM and PdM development as adapted from theCross-Industry Standard Process for Data-Mining (CRISP-DM)








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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





● VM and PdM application examplesPrediction of etch depth by VM



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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)

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Approaches for Implementation of Virtual Metrology and ...convegni.unicatt.it/miproco/meetings_Roeder2011_06_13_Statistical... · PdM modeling Predictor parameters Different currents