Prognostics and Health Management (PHM) in Semiconductor ... 8... · SEMATECH, Inc. SEMATECH, ......
Transcript of Prognostics and Health Management (PHM) in Semiconductor ... 8... · SEMATECH, Inc. SEMATECH, ......
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Accelerating Manufacturing Productivity
Copyright ©2009SEMATECH, Inc. SEMATECH, and the SEMATECH logo are registered servicemarks of SEMATECH, Inc. International SEMATECH Manufacturing Initiative, ISMI, Advanced Materials Research Center and AMRC are servicemarks of SEMATECH, Inc. All other servicemarks and trademarks are the property of their respective owners.
Prognostics and Health Management (PHM)in Semiconductor Manufacturing
SEMATECH Japan Symposium, June 22 2011
David [email protected]
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PHM agenda
Problem statement
What is PHM?
ISMI’s view of PHM in fabs
As part of holistic advanced equipment
monitoring and control
What ISMI has done & will do in PHM
Vision & guidelines
Pilots 2009–2011
Future 2011+
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Problem statement
IC factory ROI can be improved.
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Problem statement
IC factory ROI can be improved.
The factory has a wealth of DATA that can be consumed & transformed to improve ROI. Data Information Action
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Problem statement
IC factory ROI can be improved.
The factory has a wealth of DATA that can be consumed & transformed to improve ROI. Data Information Action
Applications can be developed to transform data into actionable information to improve factory performance. Examples: FDC, YMS, APC
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What is PHM?
Prognostics & Health Management (PHM) — The discipline that links studies of failure mechanisms to system lifecycle management.
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What is PHM?
Prognostics & Health Management (PHM) — The discipline that links studies of failure mechanisms to system lifecycle management. PHM uses information to allow early detection of impending or incipient faults, remaining
useful life calculations, and logistical decision-making based on health assessments and predictions.
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What is PHM?
Prognostics & Health Management (PHM) — The discipline that links studies of failure mechanisms to system lifecycle management. PHM uses information to allow early detection of impending or incipient faults, remaining
useful life calculations, and logistical decision-making based on health assessments and predictions.
The objective of PHM is to maximize equipment ROI. A PHM system will optimize scheduled maintenance, predictive condition-based maintenance, and non-predictive condition-based maintenance to an operational objective. Non-predictive condition-based maintenance is accomplished by instantaneous
monitoring of equipment and performance of maintenance when an equipment health indicator reaches a predetermined threshold. Includes traditional FDC-triggered and usage-based maintenance.
Predictive condition-based maintenance is accomplished by acquiring relevant equipment and factory data and applying an equipment degradation model to predict the equipment’s remaining useful life (RUL).
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What is PHM?
Prognostics & Health Management (PHM) — The discipline that links studies of failure mechanisms to system lifecycle management. PHM uses information to allow early detection of impending or incipient faults, remaining
useful life calculations, and logistical decision-making based on health assessments and predictions.
The objective of PHM is to maximize equipment ROI. A PHM system will optimize scheduled maintenance, predictive condition-based maintenance, and non-predictive condition-based maintenance to an operational objective. Non-predictive condition-based maintenance is accomplished by instantaneous
monitoring of equipment and performance of maintenance when an equipment health indicator reaches a predetermined threshold. Includes traditional FDC-triggered and usage-based maintenance.
Predictive condition-based maintenance is accomplished by acquiring relevant equipment and factory data and applying an equipment degradation model to predict the equipment’s remaining useful life (RUL).
A PHM system will enable effective cost versus performance decisions for joint scheduling of maintenance and WIP.
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PHM in the semiconductor fab
• PHM for semiconductor factories will apply mathematical models to equipment and factory data to assess health and predict tool failure and then act on those outputs to optimize factory productivity.
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PHM in the semiconductor fab
• PHM for semiconductor factories will apply mathematical models to equipment and factory data to assess health and predict tool failure and then act on those outputs to optimize factory productivity.– PHM will be developed for key equipment where
• Technically possible, and• Adequate business value exists.• This is NOT every tool!
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PHM in the semiconductor fab
• PHM for semiconductor factories will apply mathematical models to equipment and factory data to assess health and predict tool failure and then act on those outputs to optimize factory productivity.– PHM will be developed for key equipment where
• Technically possible, and• Adequate business value exists.• This is NOT every tool!
– At/near the equipment level, the system will monitor tools and predict failures.
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PHM in the semiconductor fab
• PHM for semiconductor factories will apply mathematical models to equipment and factory data to assess health and predict tool failure and then act on those outputs to optimize factory productivity.– PHM will be developed for key equipment where
• Technically possible, and• Adequate business value exists.• This is NOT every tool!
– At/near the equipment level, the system will monitor tools and predict failures.
– At the factory level, the system will make business rule-based decisions about scheduling PM & WIP, parts, and personnel.
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PHM data to information to action
Equipment
Select tools
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PHM data to information to action
Equipment Raw Data- Features
Select tools
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PHM data to information to action
Equipment Raw Data- Features
&Metrology Data
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
&Metrology Data
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
&Metrology Data
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
&Metrology Data
&Maintenance Record
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
Prediction Models- Planning Info
&Metrology Data
&Maintenance Record
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
Prediction Models- Planning Info
WIP
$$$
PM
Optimization- WIP & Maint
&Metrology Data
&Maintenance Record
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
Prediction Models- Planning Info
WIP
$$$
PM
Optimization- WIP & Maint
&Metrology Data
&Maintenance Record
Scheduler – WIP, PM
Resources
Select tools
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PHM data to information to action
Equipment Raw Data- Features PCA, Fisher
- Feature Importance
Health Models- Actionable Info
Prediction Models- Planning Info
WIP
$$$
PM
Optimization- WIP & Maint
&Metrology Data
&Maintenance Record
Scheduler – WIP, PM
Resources
Select tools
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Health dashboard viewTOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
• The RF generator is the lowest level as the RF generator is the field replaceable unit (FRU), what you change as a unit when the failure occurs
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Health dashboard view
• Interpretation
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
• The RF generator is the lowest level as the RF generator is the field replaceable unit (FRU), what you change as a unit when the failure occurs
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Health dashboard view
• Interpretation
Production planning
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
• The RF generator is the lowest level as the RF generator is the field replaceable unit (FRU), what you change as a unit when the failure occurs
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Health dashboard view
• Interpretation
Production planning
Maintenance planning
TOOL 0.7
EFEM 0.9 PM1 0.9 PM3 0.65PM2 0.4
PUMP 0.7 VACCTRL
0.8RF 0.3GASBOX
0.9
GEN 0.3 MATCH 0.7
ESC 0.8
• The tool is performing OK with health at 70%
• The EFEM and PM1 are in great shape – health at 90%
• The PM3 is starting to perform worse with health at 65%
• The PM2 is failing with health at 40%
• Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement
• The RF generator is the lowest level as the RF generator is the field replaceable unit (FRU), what you change as a unit when the failure occurs
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• In 2006–2009, the PHM project scope was to define the PHM concept for the semiconductor industry by member company and supplier community consensus using:
– Member company working group & supplier forum– Workshops – Output was documentation – Vision, Implementation Guideline, White Paper, Data
Requirements, Benefits Modeling
ISMI PHM project
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ISMI PHM project
• In 2006–2009, the PHM project scope was to define the PHM concept for the semiconductor industry by member company and supplier community consensus using:
– Member company working group & supplier forum– Workshops – Output was documentation – Vision, Implementation Guideline, White Paper, Data
Requirements, Benefits Modeling• In 2009–2010, the project managed 2 pilots, each executed by a team made
up of a top-10 OEM/supplier {tool expertise} plus an ISMI member company {volume factory data} plus a university researcher {advanced mathematics}. The pilots concluded in 2010.
– The pilot teams each have 2 primary tasks:• Develop and demonstrate a hardware hierarchical tool health dashboard that reports
in real time the health of the tool/module/subsystem/component• Develop and demonstrate a prediction capability for specific maintenance events
– Output includes pilot reports, benchmarking reports, some software, and some IP
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Timeline of Project History
2007 2008 2009 2010 2011
Vision Document
Equipment ImplementationGuideline
PPM DataRequirements
PPMImplementationGuideline
PPM Costand CycleTime
PPM ResearchWhite Paper
Pilot 1 & 2Demo Reports
OEMBenchmarkReport
Pilot 1Final Report
Pilot 2Final Report
More Pilots
Public
Confidential
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Pilot 1• Partners:
– Lam Research– Global Foundries– University of Cincinnati
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Pilot 1• Partners:
– Lam Research– Global Foundries– University of Cincinnati
• Data:– 4 LAM 2300® Kiyo® Conductor Etch Product platforms x 2 chambers = 8
chambers– 8 months of production data from fab >3TB– Maintenance records and metrology (etch rate, CD, particles) data
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Pilot 1• Partners:
– Lam Research– Global Foundries– University of Cincinnati
• Data:– 4 LAM 2300® Kiyo® Conductor Etch Product platforms x 2 chambers = 8
chambers– 8 months of production data from fab >3TB– Maintenance records and metrology (etch rate, CD, particles) data
• Build model to predict time to failure (RUL) for 3 costly, low frequency key maintenance events
– ESC failure– Vacuum leak {injector seal, window seal, chamber iso valve}– Wet Clean {chamber hardware element wear is root cause}
• Build hardware hierarchical view of tool health dashboard for process chamber and associated hardware
• A variety of models considered for each prediction: PLS, SOM, etc.
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Pilot 1- Health dashboard for tool 11. A collection of the health assessment results using only data directly from the component2. The health value is a distance measurement from arbitrary baseline (the start of the collected data)3. The dashboard can be used to detect and diagnose behavior shifts in the different equipment components4. Further refinement can be accomplished when health values are assigned to events – bad or good behavior
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Pilot 1 - ESC prediction1. The ESC health metric is based on a model that uses FDA-selected data features2. The ESC health metric tracking is wet clean shift-adjusted3. The ESC lifetime is assumed from the maximum historical ESC health value without failure, as “proof of life”4. The technique can be further refined when failure data is available (failure data)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
2
4
6
8
10
12
Normalized Time Units as a percentage of expected life
Adj
uste
d H
ealth
Val
ue
Adjusted for WC Health Value for Tool 6
0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 1.020.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Normalized Time Units as a percentage of expected life
Pre
dict
ed T
ime
Uni
ts L
eft
Tool 6 Predicted Life Left vs. Time (Worst as Threshold)
RUL = 0.082
Remaining Life Prediction MeanRemaining Life Lower BoundRemaining Life Upper Bound
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Pilot 2• Partners:
– Novellus Systems– Micron– University of Texas, Dr Dragan Djurdjanovic et al.
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Pilot 2• Partners:
– Novellus Systems– Micron– University of Texas, Dr Dragan Djurdjanovic et al.
• Data:– 1 Vector® PECVD TEOS tool platform– ~9 months of production data from member company fab– Maintenance records and metrology data (thickness per wafer,
particles, qualifications (thickness and RI)
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Pilot 2• Partners:
– Novellus Systems– Micron– University of Texas, Dr Dragan Djurdjanovic et al.
• Data:– 1 Vector® PECVD TEOS tool platform– ~9 months of production data from member company fab– Maintenance records and metrology data (thickness per wafer,
particles, qualifications (thickness and RI)• Build model to predict failure for chamber performance shifts
between observable and non-observable states (Hidden Markov & Match Matrix models)
• Build hardware hierarchical view of tool health dashboard for process chamber and associated hardware
• Build maintenance and WIP schedule optimizer from equipment HMM & MM output and reward/penalty functions
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Pilot 2 HMM degradation model & anomaly detection
10000.070.9300
00.050.950000.270.73
9.5 kAA
10000.060.9400
00.040.960000.070.93
kA5.1A
10000.030.9700
00.020.980000.030.97
500AA
HMM (4-) state transition matrices
for 3 different recipes
0 50 100 1500
0.2
0.4
0.6
0.8
1
5.9kA1.5kA500A
Modeled recipe-dependent
degradation rates
cycles
heal
th
Model applied to production dataanomalies detected as high error
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Current Run Cycle
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to derive similarity indices (plot on X-Y
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Pilot 2 Match matrix prediction
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Compare current set of runs to all past sets of runs (in feature space)
to derive similarity indices (plot on X-Y
graph).)]([1 mIFVw
wkt
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Calculate possible linear combinations of Feature
Vectors (FV) for m steps ahead.
76 77 78 79 80 81
0.3
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Top Plate Temp Mean (C)
Load
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acito
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(V)
Predicted FVsAverage PredictionActual Future Data PointNormal Data
Pilot 2 Match matrix prediction
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Compare current set of runs to all past sets of runs (in feature space)
to derive similarity indices (plot on X-Y
graph).)]([1 mIFVw
wkt
kk
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Calculate possible linear combinations of Feature
Vectors (FV) for m steps ahead.
Top Plate Temp Mean (Scaled)
Load
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acito
r Ove
rsho
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(Sca
led)
-4 -3 -2 -1 0 1 2 3 4-4
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76 77 78 79 80 81
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acito
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Approximate the distribution of feature vectors using mixtures
of multivariate Gaussian distribution at each step.
Pilot 2 Match matrix prediction
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Compare current set of runs to all past sets of runs (in feature space)
to derive similarity indices (plot on X-Y
graph).)]([1 mIFVw
wkt
kk
ii
Calculate possible linear combinations of Feature
Vectors (FV) for m steps ahead.
Top Plate Temp Mean (Scaled)
Load
Cap
acito
r Ove
rsho
ot H
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(Sca
led)
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Approximate the distribution of feature vectors using mixtures
of multivariate Gaussian distribution at each step.
Obtain error (distance between the peak of the GMM and
the actual FV) for each prediction step
ahead.
0 5 10 15 20 25 30 350
1
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Prediction Step
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quar
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Pilot 2 Match matrix prediction
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PHM 2011 and beyond
• ISMI will continue to strategically and tactically engage with the equipment supplier community to ensure PHM readiness {primarily data availability}
– Private engagements and public presence
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PHM 2011 and beyond
• ISMI will continue to strategically and tactically engage with the equipment supplier community to ensure PHM readiness {primarily data availability}
– Private engagements and public presence
• Pilots on high value tools/key suppliers 2011–2012+– Broaden the pilot scope to include more priority tools/tool types and
drive real-time integration
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PHM 2011 and beyond
• ISMI will continue to strategically and tactically engage with the equipment supplier community to ensure PHM readiness {primarily data availability}
– Private engagements and public presence
• Pilots on high value tools/key suppliers 2011–2012+– Broaden the pilot scope to include more priority tools/tool types and
drive real-time integration
• Pilot common component model development in 2011
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PHM 2011 and beyond
• ISMI will continue to strategically and tactically engage with the equipment supplier community to ensure PHM readiness {primarily data availability}
– Private engagements and public presence
• Pilots on high value tools/key suppliers 2011–2012+– Broaden the pilot scope to include more priority tools/tool types and
drive real-time integration
• Pilot common component model development in 2011
• Drive integration upward into the factory decision-making; algorithm-driven joint WIP and maintenance decisions
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PHM 2011 and beyond
• ISMI will continue to strategically and tactically engage with the equipment supplier community to ensure PHM readiness {primarily data availability}
– Private engagements and public presence
• Pilots on high value tools/key suppliers 2011–2012+– Broaden the pilot scope to include more priority tools/tool types and
drive real-time integration
• Pilot common component model development in 2011
• Drive integration upward into the factory decision-making; algorithm-driven joint WIP and maintenance decisions
• Integrate data-driven tool monitoring and control systems– FDC, APC, PHM, R2R, YMS…
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ISMI PHM resources on ISMI’spublic website
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ISMI PHM resources on ISMI’spublic website
• Declassified information– 4 publications with general guidelines
• Vision 2007• Equipment Implementation 2008• Integrated Implementation 2009• Data Requirements 2009
– Intelligent Maintenance Class materials 2010
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ISMI PHM resources on ISMI’spublic website
• Declassified information– 4 publications with general guidelines
• Vision 2007• Equipment Implementation 2008• Integrated Implementation 2009• Data Requirements 2009
– Intelligent Maintenance Class materials 2010– Presentations (2) and posters (6) from 2010 ISMI Symposium
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PHM Questions and Answers
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