Accelerating Manufacturing Productivity

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Prognostics and Health Management (PHM)in Semiconductor Manufacturing

SEMATECH Japan Symposium, June 22 2011

David Starkdavid.stark@ismi.sematech.org512-373-5116

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+

2

Problem statement

IC factory ROI can be improved.

3

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

4

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

5

What is PHM?

Prognostics & Health Management (PHM) — The discipline that links studies of failure mechanisms to system lifecycle management.

6

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.

7

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).

8

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.

9

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.

10

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!

11

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.

12

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.

13

PHM data to information to action

Equipment

Select tools

14

PHM data to information to action

Equipment Raw Data- Features

Select tools

15

PHM data to information to action

Equipment Raw Data- Features

&Metrology Data

Select tools

16

PHM data to information to action

Equipment Raw Data- Features PCA, Fisher

- Feature Importance

&Metrology Data

Select tools

17

PHM data to information to action

Equipment Raw Data- Features PCA, Fisher

- Feature Importance

Health Models- Actionable Info

&Metrology Data

Select tools

18

PHM data to information to action

Equipment Raw Data- Features PCA, Fisher

- Feature Importance

Health Models- Actionable Info

&Metrology Data

&Maintenance Record

Select tools

19

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

20

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

21

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

22

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

23

24

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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

26

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

27

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%

28

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%

29

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%

30

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%

31

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%

32

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%

33

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%

34

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%

35

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

36

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

37

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

38

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

39

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

40

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

41

• 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

42

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

43

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

44

Pilot 1• Partners:

– Lam Research– Global Foundries– University of Cincinnati

45

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

46

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.

47

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

48

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

49

Pilot 2• Partners:

– Novellus Systems– Micron– University of Texas, Dr Dragan Djurdjanovic et al.

50

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)

51

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

52

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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Pilot 2 Match matrix prediction

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Pilot 2 Match matrix prediction

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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

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Prediction Step

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quar

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rror

Match Matrix w/PrecoatMatch MatrixARMA

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

58

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

59

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

60

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

61

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…

62

ISMI PHM resources on ISMI’spublic website

63

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

64

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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