IEEESEMI Advanced Semiconductor Manufacturing Conference PrognosticDiagnostic Health Management

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Prognostic/Diagnostic Health Management (PHM) System for FAB Efficiency Chin Sun chin@globalcybersoft. com 1 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 1

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Outline • Introduction – Industry Trend • • 2 PHM – What? Method Results Conclusion May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 2

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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 3 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 3

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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 4 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 4

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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 5 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 5

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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 6 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 6

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Industry Trend: APC/AEC 2005 Presentation from Adventa APC/AEC 2005 / "Reaping the Benefits of Heuristic Fault Modeling" Presenter: Jared Warren, Adventa Control Technologies 7 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 7

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Industry Trend: Weighted FDC APC/AEC 2005 Presentation from Intel APC/AEC 2005 / Intel Corporation "Weighted Fault Detection and Classification" Presenter: Mao, John 8 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 8

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Industry Trend: Weighted FDC APC/AEC 2005 Presentation from Intel APC/AEC 2005 / Intel Corporation "Weighted Fault Detection and Classification" Presenter: Mao, John 9 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 9

IEEE/SEMI Advanced Semiconductor Manufacturing Conference The Evolution of Quality Control PHMEquip FDC 10 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 10

IEEE/SEMI Advanced Semiconductor Manufacturing Conference CONVENTIONAL e-Diagnostic APPROACH ? Host Equipment Engineers Slow Trouble Shooting Process • 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 11 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 11

IEEE/SEMI Advanced Semiconductor Manufacturing Conference AUTOMATED e-Diagnostic APPROACH Host + PHM-Equipment Engineers Management Enable Real Time Auto-Diagnostic • Reduce or Eliminate potential Human Errors: Automated, Knowledge based Analysis • Feed Forward ↔ Feed Backward Proactive 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 12 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 12

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Infrastructure A global Internetbased collaborative Knowledge Base accumulation and sharing environment PHM DVP Servers NO DTC Scenarios Equip. Engr. A PHM-Equip Client Equipment Manufacturers PHM-Equip Systems PHM Production Servers Verified Rules Transfer Prognostic Rules upload e. g. failing oxygen sensors Knowledge is power, but only when it is shared DTC False Alarm Scenarios Equip. Engr. B PHM-Equip Client Equip. Engr. C PHM-Equip Client u. PHM-Equip will help resolve NO DTC (Diagnositc Troub-shooting Code) problems u. PHM-Equip will help resolve DTC False Alarm problems u. PHM-Equip will accumulate Prognostic Rules from experienced equipment engineers 13 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 13

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT PHM-Equip Process Info Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge KB Info feed forward/ feed backward thru entire process flow PHM-E 1 PHM-E 2 PHM-FAB APC Device Info PHM-BE 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 PHM-Etest PHM-F 1 PHM-F 2 PHM-DDR PHM-BEST KNOWLEDGE BASES Fab equipment sets E-TEST Fab equipment sets Gate Ox 14 Vt implant Litho FAB Front End May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts Process Flow Fab Processes Wafer Sort /Final Test Defect Density Reduction FAB Back End C. Sun Slide 14

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Architecture v Western Electrical (WE) control charts with pattern recognition capability + Multivariate FDC to identify out of control tool parameters v 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 Equip/Tool Data PHM-Equip RT-KM Engine Fault Tool Cause Fault Diagnostic Classification Report Fast Corrective Action RT-KM Rule-Based Root Causes Identification 15 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 15

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Highlighted Features • • 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 16 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 16

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Highlighted Benefits • • • 17 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 24 x 7 Equipment Process Monitoring Capable of Supporting multiple Equipment Reduce engineers’ pressure, increase productivity and efficiency Permanent repository of Knowledge and Expertise May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 17

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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 Actions and Enabling Continuous Improvement 18 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 18

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Conclusions • • • Knowledge Based Methodology On-line, Real time Auto diagnostics/prognostics Permanent repository for knowledge 24 X 7 Equipment monitoring Enable Global e-Diagnostic In Summary: PHM-Equip provides an innovative methodology for Equipment Control. PHM-Equip enables 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) 19 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 19

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Method: Mahalanobis Distance 20 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 20

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Method: Mahalanobis Distance 21 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 21

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Method: Mahalanobis-Taguchi System 22 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 22

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Method: Mahalanobis-Taguchi System A Multidimensional diagnosis system 23 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 23

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Method: Mahalanobis-Taguchi System A 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. 24 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 24

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Examples: Data Source 25 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 25

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Examples: Data Source (OES) Optical Emission Spectroscopy wavelength monitored u 250 nm u 261. 8 nm u 266. 6 nm u 272. 2 nm u 278. 3 nm u 284. 6 nm u 288. 25 nm u…. . u 791. 5 nm 26 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 26

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Results: Fault Detection • Step 1: Define the Problem 27 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 27

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Results: Fault Detection • Step 2: Define Control/Response Variables (OES) Optical Emission Spectroscopy wavelength monitored u 250 nm u 261. 8 nm u 266. 6 nm u 272. 2 nm u 278. 3 nm u 284. 6 nm u 288. 25 nm u…. . u 791. 5 nm 28 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts (MD) Mahalanobis Distance C. Sun Slide 28

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Results: Fault Detection • Step 3: Construct the “Full Model MTS Measurement Scale” Note: The measurement scale is constructed by training datasets 29 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 29

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Results: Fault Detection • Step 4: Validate the ability of measurement scale Note: the capability of measurement scale is demonstrated by test datasets. 30 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 30

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Results: Fault Classification Method: Distinguish the signal pattern shift of each variable between the test dataset and the model 31 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 31

IEEE/SEMI Advanced Semiconductor Manufacturing Conference 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. 32 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 32

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Create Diagnostic Rule from pattern signature 33 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 33

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM Fault Detection and Classification Real Time FDC Monitor Window 34 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 34

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM Fault Detection and Classification Report Root cause of equipment malfunction and PIDs associated with faults 35 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 35

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM Fault Detection and Classification Summary 36 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 36

IEEE/SEMI Advanced Semiconductor Manufacturing Conference Promote Predictive Maintenance Example of Prognostic Rule for oxygen sensor 37 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 37

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Example: Diagnostic Results Normal process patterns 38 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 38

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-Equip Example: Diagnostic Results Progressive degrading Operating patterns can be used to generate prognostic pattern recgonition rules 39 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 39

IEEE/SEMI Advanced Semiconductor Manufacturing Conference State-based Warning System 4. Do not commence processing 1. Normal 40 May 22 -24, 2006 2. Predictive Monitoring started ASMC 2006 – Boston, Massachusetts 3. Recommand preventive maintenance (PM) in 48 hr Monitoring started C. Sun Slide 40 5. Stop Processing

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT PHM-Equip Process Info Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge KB Info feed forward/ feed backward thru entire process flow PHM-E 1 PHM-E 2 PHM-FAB APC Device Info PHM-BE 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 PHM-Etest PHM-F 1 PHM-F 2 PHM-DDR PHM-BEST KNOWLEDGE BASES Fab equipment sets E-TEST Fab equipment sets Gate Ox 41 Vt implant Litho FAB Front End May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts Process Flow Fab Processes Wafer Sort /Final Test Defect Density Reduction FAB Back End C. Sun Slide 41

IEEE/SEMI Advanced Semiconductor Manufacturing Conference PHM VALUE PROPOSITION v Provide Predictive Equipment Maintenance & Diagnostics v Correct Problems before failure occurs v Real time process/tool/equipment health feedback v Pinpoints miss processing/equipment malfunction steps v Diagnostic report feeds backward v Diagnostic report feeds forward v Knowledge reusable, never lost 42 May 22 -24, 2006 ASMC 2006 – Boston, Massachusetts C. Sun Slide 42