Prognostics and Health Management (PHM) in Semiconductor Manufacturing

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Adele Ashlyn Sharp
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Accelerating Manufacturing Productivity Prognostics and Health

Symposium, June 22 211 David Stark david.stark@ismi.sematech.

International SEMATECH Manufacturing Initiative, ISMI, Advanced

1 Accelerating Manufacturing Productivity Prognostics and Health Management (PHM) in Semiconductor Manufacturing SEMATECH Japan Symposium, June David Stark Copyright 29 SEMATECH, 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.

2 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 Future

3 Problem statement IC factory ROI can be improved. 3

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 4

5 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

6 What is PHM? Prognostics & Health Management (PHM) The discipline that links studies of failure mechanisms to system lifecycle management. 6

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. 7

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 nonpredictive 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

9 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 nonpredictive 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

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. 1

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! 11

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. 12

13 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

14 PHM data to information to action Select tools Equipment 14

15 PHM data to information to action Select tools Equipment Raw Data - Features 15

16 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features 16

17 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features PCA, Fisher - Feature Importance 17

18 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features PCA, Fisher - Feature Importance Health Models - Actionable Info 18

19 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features PCA, Fisher - Feature Importance &Maintenance Record Health Models - Actionable Info 19

20 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features PCA, Fisher - Feature Importance &Maintenance Record Prediction Models - Planning Info Health Models - Actionable Info 2

21 PHM data to information to action Select tools &Metrology Data Equipment Raw Data - Features PCA, Fisher - Feature Importance &Maintenance Record $$$ Optimization - WIP & Maint WIP PM Prediction Models - Planning Info Health Models - Actionable Info 21

22 PHM data to information to action Select tools &Metrology Data Scheduler WIP, PM Equipment Raw Data - Features PCA, Fisher - Feature Importance &Maintenance Record Resources $$$ Optimization - WIP & Maint WIP PM Prediction Models - Planning Info Health Models - Actionable Info 22

23 PHM data to information to action Select tools &Metrology Data Scheduler WIP, PM Equipment Raw Data - Features PCA, Fisher - Feature Importance &Maintenance Record Resources $$$ Optimization - WIP & Maint WIP PM Prediction Models - Planning Info Health Models - Actionable Info 23

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26 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 GEN.3 MATCH.7 26

27 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation GEN.3 MATCH.7 27

28 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% GEN.3 MATCH.7 28

29 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% GEN.3 MATCH.7 29

30 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% GEN.3 MATCH.7 3

31 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% GEN.3 MATCH.7 31

32 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% GEN.3 MATCH.7 32

33 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% GEN.3 MATCH.7 33

34 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 34

35 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 35

36 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement 36

37 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 Drill down on PM2 shows the problem is in the RF subsystem, specifically the RF generator needs replacement 37

38 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 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

39 Health dashboard view TOOL.7 EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 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

40 Health dashboard view TOOL.7 Production planning EFEM.9 PM1.9 PM2.4 PM3.65 GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 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 4

41 Health dashboard view TOOL.7 Production planning EFEM.9 PM1.9 PM2.4 PM3.65 Maintenance planning GAS BOX.9 PUMP.7 VAC RF.3 CTRL.8 ESC.8 Interpretation The tool is performing OK with health at 7% The EFEM and PM1 are in great shape health at 9% The PM3 is starting to perform worse with health at 65% The PM2 is failing with health at 4% GEN.3 MATCH.7 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

42 ISMI PHM project In 26 29, 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 42

43 ISMI PHM project In 26 29, 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 29 21, the project managed 2 pilots, each executed by a team made up of a top-1 OEM/supplier {tool expertise} plus an ISMI member company {volume factory data} plus a university researcher {advanced mathematics}. The pilots concluded in 21. 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

44 Timeline of Project History Vision Document Equipment Implementation Guideline PPM Research White Paper PPM Implementation Guideline PPM Cost and Cycle Time Pilot 1 & 2 Demo Reports Pilot 1 Final Report Pilot 2 Final Report More Pilots PPM Data Requirements OEM Benchmark Report Public Confidential 44

45 Pilot 1 Partners: Lam Research Global Foundries University of Cincinnati 45

46 Pilot 1 Partners: Lam Research Global Foundries University of Cincinnati Data: 4 LAM 23 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

47 Pilot 1 Partners: Lam Research Global Foundries University of Cincinnati Data: 4 LAM 23 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

48 Pilot 1- Health dashboard for tool 1 1. A collection of the health assessment results using only data directly from the component 2. 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 components 4. Further refinement can be accomplished when health values are assigned to events bad or good behavior 48

49 Pilot 1 - ESC prediction 1. The ESC health metric is based on a model that uses FDA-selected data features 2. The ESC health metric tracking is wet clean shift-adjusted 3. 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) 12 1 Adjusted for WC Health Value for Tool Tool 6 Predicted Life Left vs. Time (Worst as Threshold) Remaining Life Prediction Mean Remaining Life Lower Bound Remaining Life Upper Bound Adjusted Health Value Predicted Time Units Left RUL = Normalized Time Units as a percentage of expected life Normalized Time Units as a percentage of expected life 49

50 Pilot 2 Partners: Novellus Systems Micron University of Texas, Dr Dragan Djurdjanovic et al. 5

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) 51

52 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 HMM

Modeled recipedependent degradation rates Model applied

9 ka A 1.5kA A 5A A.73.93.97.27.95.7.96.3.98.5.93.4.94.2.97.7 1.

53 Pilot 2 HMM degradation model & anomaly detection HMM (4-) state transition matrices for 3 different recipes 1 Modeled recipedependent degradation rates Model applied to production data anomalies detected as high error 5.9 ka A 1.5kA A 5A A health kA 1.5kA 5A 5 cycles

54 Pilot 2 Match matrix prediction Previous Run6Cycle Previous Run6Best Match Previous Run7Cycle Previous Run7Best Match Current Run Cycle Previous Run1Cycle Previous Run1Best Match Current Run Cycle Current Run Cycle Current Run Cycle Current Run Cycle Previous Run2Cycle Previous Run2Best Match Current Run Cycle Current Run Cycle Compare current set of runs to all past sets of runs (in feature space) to derive similarity indices (plot on X-Y graph) Current Run Cycle 54

55 Pilot 2 Match matrix prediction Previous Run6Cycle Previous Run6Best Match Previous Run7Cycle Previous Run7Best Match Current Run Cycle Previous Run1Cycle Previous Run1Best Match Current Run Cycle Current Run Cycle Current Run Cycle Current Run Cycle Previous Run2Cycle Previous Run2Best Match Current Run Cycle Current Run Cycle Compare current set of runs to all past sets of runs (in feature space) to derive similarity indices (plot on X-Y graph). Calculate possible linear combinations of Feature Vectors (FV) for m steps ahead. 1 i w i k k t w FV[ I k ( m)] Load Capacitor Overshoot High (V) Predicted FVs Average Prediction Actual Future Data Point Normal Data Top Plate Temp Mean (C) Current Run Cycle 55

56 Pilot 2 Match matrix prediction Previous Run6Cycle Previous Run6Best Match Previous Run7Cycle Previous Run7Best Match Current Run Cycle Previous Run1Cycle Previous Run1Best Match Current Run Cycle Current Run Cycle Current Run Cycle Current Run Cycle Previous Run2Cycle Previous Run2Best Match Current Run Cycle Current Run Cycle Current Run Cycle Compare current set of runs to all past sets of runs (in feature space) to derive similarity indices (plot on X-Y graph). Calculate possible linear combinations of Feature Vectors (FV) for m steps ahead. i 1 w i k k t w FV[ I k ( m)] Load Capacitor Overshoot High (V) Top Plate Temp Mean (C) Approximate the distribution of feature vectors using mixtures of multivariate Gaussian distribution at each step. Predicted FVs Average Prediction Actual Future Data Point Normal Data Load Capacitor Overshoot High (Scaled) Normal Prediction Current Run Actual Top Plate Temp Mean (Scaled) 56

57 Pilot 2 Match matrix prediction Previous Run6Cycle Previous Run6Best Match Previous Run7Cycle Previous Run7Best Match Mean Squared Error Current Run Cycle Previous Run1Cycle Previous Run1Best Match Current Run Cycle Current Run Cycle Current Run Cycle Current Run Cycle Previous Run2Cycle Previous Run2Best Match Current Run Cycle Current Run Cycle Current Run Cycle Compare current set of runs to all past sets of runs (in feature space) to derive similarity indices (plot on X-Y graph). Match Matrix w/precoat Match Matrix ARMA Calculate possible linear combinations of Feature Vectors (FV) for m steps ahead. i 1 w i k k t w FV[ I k ( m)] Obtain error (distance between the peak of the GMM and the actual FV) for each prediction step ahead. Load Capacitor Overshoot High (V) Top Plate Temp Mean (C) Approximate the distribution of feature vectors using mixtures of multivariate Gaussian distribution at each step. Load Capacitor Overshoot High (Scaled) Predicted FVs Average Prediction Actual Future Data Point Normal Data Normal Prediction Current Run Actual Prediction Step Top Plate Temp Mean (Scaled) 57

58 PHM 211 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

59 PHM 211 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 Broaden the pilot scope to include more priority tools/tool types and drive real-time integration 59

60 PHM 211 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 Broaden the pilot scope to include more priority tools/tool types and drive real-time integration Pilot common component model development in 211 6

61 PHM 211 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 Broaden the pilot scope to include more priority tools/tool types and drive real-time integration Pilot common component model development in 211 Drive integration upward into the factory decision-making; algorithm-driven joint WIP and maintenance decisions 61

62 PHM 211 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 Broaden the pilot scope to include more priority tools/tool types and drive real-time integration Pilot common component model development in 211 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

63 ISMI PHM resources on ISMI s public website 63

64 ISMI PHM resources on ISMI s public website Declassified information 4 publications with general guidelines Vision 27 Equipment Implementation 28 Integrated Implementation 29 Data Requirements 29 Intelligent Maintenance Class materials 21 64

65 ISMI PHM resources on ISMI s public website Declassified information 4 publications with general guidelines Vision 27 Equipment Implementation 28 Integrated Implementation 29 Data Requirements 29 Intelligent Maintenance Class materials 21 Presentations (2) and posters (6) from 21 ISMI Symposium 65

66 PHM Questions and Answers 66

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