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Outline

Prognostic/Diagnostic Health Management (PHM) System for FAB Efficiency Chin Sun chin @globalcybersoft.com. Outline. Introduction Industry Trend PHM – What? Method Results Conclusion. Industry Trend: APC/AEC 2005 Presentation from Samsung.

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Outline

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  1. Prognostic/Diagnostic Health Management (PHM) SystemforFAB EfficiencyChin Sunchin@globalcybersoft.com ASMC 2006 – Boston, Massachusetts C. Sun Slide 1

  2. Outline • Introduction • Industry Trend • PHM – What? • Method • Results • Conclusion ASMC 2006 – Boston, Massachusetts C. Sun Slide 2

  3. Industry Trend: APC/AEC 2005 Presentation from Samsung APC/AEC 2005 / Samsung Electronics Co., Ltd.  "An Application of Multivariate Statistics in Detecting Equipment Changes" Presenter: Lee, Seungjun ASMC 2006 – Boston, Massachusetts C. Sun Slide 3

  4. Industry Trend: APC/AEC 2005 Presentation from Samsung APC/AEC 2005 / Samsung Electronics Co., Ltd.  "An Application of Multivariate Statistics in Detecting Equipment Changes" Presenter: Lee, Seungjun ASMC 2006 – Boston, Massachusetts C. Sun Slide 4

  5. Industry Trend: APC/AEC 2005 Presentation from Helix Tech. APC/AEC 2005 / Helix Technology Corporation  "Predictive Capability Enabled by a Deterministic Method of Analysis or Real World Vacuum System e-Diagnostics" Presenter: Gaudet, Peter ASMC 2006 – Boston, Massachusetts C. Sun Slide 5

  6. Industry Trend: APC/AEC 2005 Presentation from Helix Tech. APC/AEC 2005 / Helix Technology Corporation  "Predictive Capability Enabled by a Deterministic Method of Analysis or Real World Vacuum System e-Diagnostics" Presenter: Gaudet, Peter ASMC 2006 – Boston, Massachusetts C. Sun Slide 6

  7. Industry Trend: APC/AEC 2005 Presentation from Adventa APC/AEC 2005 / "Reaping the Benefits of Heuristic Fault Modeling" Presenter: Jared Warren, Adventa Control Technologies ASMC 2006 – Boston, Massachusetts C. Sun Slide 7

  8. Industry Trend: Weighted FDC APC/AEC 2005 Presentation from Intel APC/AEC 2005 / Intel Corporation  "Weighted Fault Detection and Classification" Presenter: Mao, John ASMC 2006 – Boston, Massachusetts C. Sun Slide 8

  9. Industry Trend: Weighted FDC APC/AEC 2005 Presentation from Intel APC/AEC 2005 / Intel Corporation  "Weighted Fault Detection and Classification" Presenter: Mao, John ASMC 2006 – Boston, Massachusetts C. Sun Slide 9

  10. The Evolution of Quality Control PHM-Equip FDC ASMC 2006 – Boston, Massachusetts C. Sun Slide 10

  11. CONVENTIONAL e-Diagnostic APPROACH ? • Opportunities of Human Errors: Labor intensive and Time consuming • Passive Approach: No knowledge sharing or self learning, lacking of predictive capability • Inconsistency: Analysis results are human dependent • Cost of Resources: Delay Time-to-Corrective Actions, Long training time for new engineers Equipment Engineers Host Slow Trouble Shooting Process ASMC 2006 – Boston, Massachusetts C. Sun Slide 11

  12. AUTOMATED e-Diagnostic APPROACH • Reduce or Eliminate potential Human Errors: Automated, Knowledge based Analysis • Feed Forward ↔ Feed BackwardProactive Approach: Enable Knowledge Sharing, Self Correction, and providing Predictive Capability • Consistency: Analysis results are based on Data and Knowledge • Saving Resources: Fast Time-to-Corrective Actions, Shorten training time for new engineers Management Host + PHM-Equip Equipment Engineers Enable Real Time Auto-Diagnostic ASMC 2006 – Boston, Massachusetts C. Sun Slide 12

  13. PHM-Equip Infrastructure Equipment Manufacturers PHM-Equip Systems A global Internet-based collaborative Knowledge Base accumulation and sharing environment PHM DVP Servers PHM Production Servers Verified Rules Transfer Knowledge is power, but only when it is shared NO DTC Scenarios Prognostic Rules upload e.g. failing oxygen sensors DTC False Alarm Scenarios Equip. Engr. A PHM-Equip Client Equip. Engr. B PHM-Equip Client Equip. Engr. C PHM-Equip Client • PHM-Equip will help resolve NO DTC (Diagnositc Troub-shooting Code) problems • PHM-Equip will help resolve DTC False Alarm problems • PHM-Equip will accumulate Prognostic Rules from experienced equipment engineers ASMC 2006 – Boston, Massachusetts C. Sun Slide 13

  14. PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT PHM-Equip Process Info PHM-FAB Device Info PHM-BE Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge Yield/Product Engineers populate PHM-BE with feedback rules based on previous analysis Process Engineers populate PHM-FAB with APC rules based on their knowledge APC KB Info feed forward/feed backward thru entire process flow PHM-E1 PHM-E2 PHM-F1 PHM-F2 PHM-DDR PHM-BEST PHM-Etest KNOWLEDGE BASES Fab equipment sets E-TEST Wafer Sort /Final Test Fab equipment sets Defect Density Reduction Process Flow Gate Ox Vt implant Litho Fab Processes FAB Front End ASMC 2006 – Boston, Massachusetts C. Sun Slide 14 FAB Back End

  15. PHM-Equip Architecture • Western Electrical (WE) control charts with pattern recognition capability + Multivariate FDC to identify out of control tool parameters • Advanced Real Time-Knowledge Management (RT-KM) Rule-based methodology automatically determine when an equipment fault occurs, what caused it, and how to correct it Multivariate Mahalanobis Distance Fault Detection Engine PHM-Equip RT-KM Engine Fault Classification Tool Diagnostic Report Equip/Tool Data Fault Cause Fast Corrective Action RT-KM Rule-Based Root Causes Identification ASMC 2006 – Boston, Massachusetts C. Sun Slide 15

  16. Shorten time to analyze data to validate decisions Automatic Equipment diagnostics Increase Engineers’ productivity and efficiency. Resolve equipment malfunction problems faster Use Knowledge system as a continuous learning tool Integrated Knowledge base/Database optimized for Ediag data Fast, simple access to diagnostic report Facilitates collaboration among different FAB equipment engineers Versatile and interactive Rule development tool Worksheet based Easy to use Rules are specific for Equipment Diagnostic analysis Highlighted Features ASMC 2006 – Boston, Massachusetts C. Sun Slide 16

  17. Highlighted Benefits • Improved Time To Market and Reduce the waste of manpower • Enable effective Knowledge Sharing • Utilize Engineering Knowledge in FDC to have more accurate detection • Enable Real-time feedback, Continuous Improvement • Eliminate False Alarms • Reduce scrapped/low performance wafers • Enable 24x7 Equipment Process Monitoring • Capable of Supporting multiple Equipment • Reduce engineers’ pressure, increase productivity and efficiency • Permanent repository of Knowledge and Expertise ASMC 2006 – Boston, Massachusetts C. Sun Slide 17

  18. PHM-Equip Solutions • Real time feedback of Equipment & Process Status • Automatically Identify equipment malfunctions/Process misprocessing • Real time feedback of Diagnostic/Prognostic reports • Knowledge retained in database, never lost Fast Time-to-Corrective Actionsand Enabling Continuous Improvement ASMC 2006 – Boston, Massachusetts C. Sun Slide 18

  19. Conclusions • Knowledge Based Methodology • On-line, Real time • Auto diagnostics/prognostics • Permanent repository for knowledge • 24 X 7Equipment monitoring • Enable Global e-Diagnostic In Summary:PHM-Equip provides an innovative methodology for Equipment Control. PHM-Equipenables continuous improvement in the day-to-day Operation of Equipment. As the results, PHM-Equip presents numerous possibilities to improve the Overall Equipment Efficiency (OEE) ASMC 2006 – Boston, Massachusetts C. Sun Slide 19

  20. Method: Mahalanobis Distance ASMC 2006 – Boston, Massachusetts C. Sun Slide 20

  21. Method: Mahalanobis Distance ASMC 2006 – Boston, Massachusetts C. Sun Slide 21

  22. Method: Mahalanobis-Taguchi System ASMC 2006 – Boston, Massachusetts C. Sun Slide 22

  23. Method: Mahalanobis-Taguchi SystemA Multidimensional diagnosis system ASMC 2006 – Boston, Massachusetts C. Sun Slide 23

  24. Method: Mahalanobis-Taguchi SystemA Multidimensional diagnosis system Where Si= standard deviations of i – th variable, C-1 = the inverse of correlation matrix, k = number of variables, n = number of observations, T = transpose of the standard vector. ASMC 2006 – Boston, Massachusetts C. Sun Slide 24

  25. PHM-Equip Examples: Data Source ASMC 2006 – Boston, Massachusetts C. Sun Slide 25

  26. PHM-Equip Examples: Data Source (OES) Optical Emission Spectroscopy wavelength monitored • 250 nm • 261.8 nm • 266.6 nm • 272.2 nm • 278.3 nm • 284.6 nm • 288.25 nm • ….. • 791.5 nm ASMC 2006 – Boston, Massachusetts C. Sun Slide 26

  27. Results: Fault Detection • Step 1: Define the Problem ASMC 2006 – Boston, Massachusetts C. Sun Slide 27

  28. Results: Fault Detection • Step 2: Define Control/Response Variables (OES) Optical Emission Spectroscopy wavelength monitored • 250 nm • 261.8 nm • 266.6 nm • 272.2 nm • 278.3 nm • 284.6 nm • 288.25 nm • ….. • 791.5 nm (MD) Mahalanobis Distance ASMC 2006 – Boston, Massachusetts C. Sun Slide 28

  29. Results: Fault Detection • Step 3: Construct the “Full Model MTS Measurement Scale” Note: The measurement scale is constructed by training datasets ASMC 2006 – Boston, Massachusetts C. Sun Slide 29

  30. Results: Fault Detection • Step 4: Validate the ability of measurement scale • Note: the capability of measurement scale is demonstrated by test datasets. ASMC 2006 – Boston, Massachusetts C. Sun Slide 30

  31. Results: Fault Classification Method: Distinguish the signal pattern shift of each variable between the test dataset and the model ASMC 2006 – Boston, Massachusetts C. Sun Slide 31

  32. Results: Fault Classification Results: Test wafer 2 and test wafer 18 have the same four machine state variables associated with the RF-12 system fault. ASMC 2006 – Boston, Massachusetts C. Sun Slide 32

  33. Create Diagnostic Rule from pattern signature ASMC 2006 – Boston, Massachusetts C. Sun Slide 33

  34. PHM Fault Detection and Classification Real Time FDC Monitor Window ASMC 2006 – Boston, Massachusetts C. Sun Slide 34

  35. PHM Fault Detection and Classification Report Root cause of equipment malfunction and PIDs associated with faults ASMC 2006 – Boston, Massachusetts C. Sun Slide 35

  36. PHM Fault Detection and Classification Summary ASMC 2006 – Boston, Massachusetts C. Sun Slide 36

  37. Promote Predictive MaintenanceExample of Prognostic Rule for oxygen sensor ASMC 2006 – Boston, Massachusetts C. Sun Slide 37

  38. PHM-Equip Example: Diagnostic Results Normal process patterns ASMC 2006 – Boston, Massachusetts C. Sun Slide 38

  39. PHM-Equip Example: Diagnostic Results Progressive degrading Operating patterns can be used to generate prognostic pattern recgonition rules ASMC 2006 – Boston, Massachusetts C. Sun Slide 39

  40. State-based Warning System 4. Do not commence processing 1. Normal 2. Predictive Monitoring started 3. Recommand preventive maintenance (PM) in 48 hr Monitoring started 5. Stop Processing ASMC 2006 – Boston, Massachusetts C. Sun Slide 40

  41. PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT PHM-Equip Process Info PHM-FAB Device Info PHM-BE Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge Yield/Product Engineers populate PHM-BE with feedback rules based on previous analysis Process Engineers populate PHM-FAB with APC rules based on their knowledge APC KB Info feed forward/feed backward thru entire process flow PHM-E1 PHM-E2 PHM-F1 PHM-F2 PHM-DDR PHM-BEST PHM-Etest KNOWLEDGE BASES Fab equipment sets E-TEST Wafer Sort /Final Test Fab equipment sets Defect Density Reduction Process Flow Gate Ox Vt implant Litho Fab Processes FAB Front End ASMC 2006 – Boston, Massachusetts C. Sun Slide 41 FAB Back End

  42. PHM VALUE PROPOSITION • Provide Predictive Equipment Maintenance & Diagnostics • Correct Problems before failure occurs • Real time process/tool/equipment health feedback • Pinpoints miss processing/equipment malfunction steps • Diagnostic report feeds backward • Diagnostic report feeds forward • Knowledge reusable, never lost ASMC 2006 – Boston, Massachusetts C. Sun Slide 42

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