Prognostic/Diagnostic Health Management (PHM) System for FAB Efficiency
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1 Prognostic/Diagnostic Health Management (PHM) System for FAB Efficiency Chin Sun 1 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 1
2 Outline Introduction Industry Trend PHM What? Method Results Conclusion 2 May 22-24, 2006 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 3 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 3
4 Industry Trend: APC/AEC 2005 Presentation from Samsung 4 APC/AEC 2005 / Samsung Electronics Co., Ltd. "An Application of Multivariate Statistics in Detecting Equipment Changes" Presenter: Lee, Seungjun May 22-24, 2006 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 5 May 22-24, 2006 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 6 May 22-24, 2006 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 7 May 22-24, 2006 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 8 May 22-24, 2006 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 9 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 9
10 The Evolution of Quality Control PHM- Equip FDC 10 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 10
11 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
12 AUTOMATED e-diagnostic APPROACH Host + PHM-Equip 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
13 PHM-Equip Infrastructure A global Internetbased collaborative Knowledge Base accumulation and sharing environment NO DTC Scenarios PHM DVP Servers Equipment Manufacturers PHM-Equip Systems Verified Rules Transfer Prognostic Rules upload e.g. failing oxygen sensors DTC False Alarm Scenarios PHM Production Servers Knowledge is power, but only when it is shared 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 13 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 13
14 PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT Process Info Device Info PHM-Equip PHM-FAB PHM-BE KB Info feed forward/ feed backward thru entire process flow Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge APC PHM-E1 PHM-E2 PHM-F1 PHM-F2 KNOWLEDGE BASES Process Engineers populate PHM-FAB with APC rules based on their knowledge Yield/Product Engineers populate PHM-BE with feedback rules based on previous analysis PHM-Etest PHM-DDR PHM-BEST 14 Gate Ox Vt implant FAB Front End Fab equipment sets Fab equipment sets Litho Process Flow Fab Processes E-TEST May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 14 Defect Density Reduction FAB Back End Wafer Sort /Final Test
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 Equip/Tool RT-KM Engine Data Fault Fault Cause Fault Classification Tool Tool Diagnostic Report Report Fast Corrective Action RT-KM Rule-Based Root Causes Identification 15 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 15
16 Method: Mahalanobis Distance 16 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 16
17 Method: Mahalanobis Distance 17 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 17
18 Method: Mahalanobis-Taguchi System 18 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 18
19 Method: Mahalanobis-Taguchi System A Multidimensional diagnosis system 19 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 19
20 Method: Mahalanobis-Taguchi System A Multidimensional diagnosis system Where S i = 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. 20 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 20
21 PHM-Equip Examples: Data Source 21 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 21
22 PHM-Equip Examples: Data Source (OES) Optical Emission Spectroscopy wavelength monitored 250 nm nm nm nm nm nm nm nm 22 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 22
23 Results: Fault Detection Step 1: Define the Problem 23 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 23
24 Results: Fault Detection Step 2: Define Control/Response Variables (OES) Optical Emission Spectroscopy wavelength monitored 250 nm nm nm nm nm nm nm nm (MD) Mahalanobis Distance 24 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 24
25 Results: Fault Detection Step 3: Construct the Full Model MTS Measurement Scale Note: The measurement scale is constructed by training datasets 25 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 25
26 Results: Fault Detection Step 4: Validate the ability of measurement scale Note: the capability of measurement scale is demonstrated by test datasets. 26 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 26
27 Results: Fault Classification Method: Distinguish the signal pattern shift of each variable between the test dataset and the model 27 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 27
28 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. 28 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 28
29 Create Diagnostic Rule from pattern signature 29 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 29
30 PHM Fault Detection and Classification Summary 30 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 30
31 Promote Predictive Maintenance Example of Prognostic Rule for oxygen sensor 31 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 31
32 PHM-Equip Example: Diagnostic Results Normal process patterns 32 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 32
33 PHM-Equip Example: Diagnostic Results Progressive degrading Operating patterns can be used to generate prognostic pattern recgonition rules 33 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 33
34 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 34 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 34
35 PHM-INT, PHM-Equipment, PHM-FAB & PHM-BE PHM-INT Process Info Device Info PHM-Equip PHM-FAB PHM-BE KB Info feed forward/ feed backward thru entire process flow Equipment Engineers populate PHM-Equip with equipment rules based on their knowledge APC PHM-E1 PHM-E2 PHM-F1 PHM-F2 KNOWLEDGE BASES Process Engineers populate PHM-FAB with APC rules based on their knowledge Yield/Product Engineers populate PHM-BE with feedback rules based on previous analysis PHM-Etest PHM-DDR PHM-BEST 35 Gate Ox Vt implant FAB Front End Fab equipment sets Fab equipment sets Litho Process Flow Fab Processes E-TEST May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 35 Defect Density Reduction FAB Back End Wafer Sort /Final Test
36 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 36 May 22-24, 2006 ASMC 2006 Boston, Massachusetts C. Sun Slide 36
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