Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Roi-based Data Collection by Alan Weber at Cimetrix
Ähnlich wie Roi-based Data Collection by Alan Weber at Cimetrix (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Roi-based Data Collection by Alan Weber at Cimetrix
- 1. 29th Advanced Process Control Conference Austin, Texas October 9-11, 2017 Alan Weber – Cimetrix Incorporated ROI-based Approach for Evaluating Application Data Collection Use Case Alternatives
- 2. Outline • Key messages • Keeping score with ROI • The standardization paradox • Data collection strategy • Example use case • Factory implementation alternatives
- 3. Key messages • It’s important to keep score… while agreeing on the rules of the game • Standardization does not lead to conformity, but actually enables genuine transformation • Data collection should be viewed as a spectrum of alternatives, not an all-or-nothing proposition • The latest generations of SEMI standards offer many opportunities for engineering cost savings
- 4. The importance of keeping score It’s an integral part of everyday life • Sports • Business • Politics • Science • Health • Finances • Relationships • …
- 5. Keeping score with an ROI model Agree on relative cost and value of key factors Costs • Materials/outside services • Software development • Technology development • Hardware • Licenses • Internal labor • Operations • Engineering • Automation • Information technology • Capital expenses • Equipment • Other Benefits • Product material • Yield • Yield ramp • Scrap reduction • Time • Equipment/fab uptime • Factory cycle time • New Product Introduction time • Cost Reduction • Qual wafers • Hardware • Licenses • Engineering labor • Other
- 6. The standardization paradox Avoiding the conformity trap • How can standardization result in competitive advantage? • If we all fabs ca gather the same data, how can a single fab differentiate itself from the others? • Consider the use of time • We all start with the same amount of time... but success favors those with the discipline to use it wisely • Think of discipline as a sort of “personal entropy reduction” • The same applies to data • It’s not what you collect, but how you use it
- 8. Data collection strategy Start with the goal and work backwards • What are the critical decisions that need to be made? • What insights do we need to make these decisions? • What information do we need to get these insights? • What data must we collect to get the information we want? Source: McKinsey & Company Insights Information Data Decisions
- 9. Data collection alternatives Spec contents, tool capability SEMI Standard Level Functionality Benefit GEM/GEM300 Full support for E40, E87, E90, E94, etc. Baseline: Supplier-specific integration costs; labor-intensive SECS data collection management, tool characterization, software upgrade verification, and fault model development processes EDA Freeze I (1105) EDA basics – early metadata models, DCP-based “data on demand”, multi-client access Self-documenting interface capability; quick and easy to change data collection plans as application needs evolve; factory system architecture flexibility EDA Freeze II (0710) Conditional triggers in trace requests, simple event support, interface discovery; second-generation metadata models Precisely “frame” trace data depending on application requirements; one-click connectivity; cleaner model structures with richer event/parameter content; higher performance EDA Common Metadata (E164) Complete coverage of GEM300 and E157 objects, state machines, events; standard metadata model structure, content, and names Programmatically generate DCPs, configure generic tool applications, characterize equipment behavior; simplify mapping to factory data management systems Factory-Specific EDA Requirements Process-specific parameters for advanced feature extraction for FDC, PHM, VM; mechanism- and component-level command/response signals for fingerprinting, tool matching; etc. Dramatically increase visibility into tool and process behavior; enable advanced “smart factory” monitoring and control applications well beyond current capabilities
- 10. What does E164 specify? Structure and content of equipment metadata • E120/E125 Common Equipment Model usage/content • Nodes and parameters must have meaningful descriptions • Equipment element attributes for all E120 nodes must have meaningful values • All definitions (exceptions, SMs, parameter types, units, SEMI object types) must be referenced • Strict event name enforcement • State Machines • Strict State Machine definitions • Requires E157 State Machines for all process modules • Requires E90 State Machines for all substrate locations • Requires all Parameters, Events and Exceptions defined in Freeze II standards to be present • State and transition names must match GEM300 standards
- 11. Standard metadata model benefits First specified by SEMI E164 • Model structure exactly reflects tool hardware organization • Complete description of all potentially useful information in the tool • Always accurate, always available – no additional documentation required • Common point of reference among tool, process, and factory stakeholders • Source of unambiguous identifiers/tags for database [auto] configuration • Enables “plug and play” applications Process Module #1 Gate Valve Data Substrate Location Utilization More Data, Events, Alarms Process Tracking Other Components
- 12. Example use case Multivariate Fault Detection and Classification (FDC) • Description • Multivariate statistics used to develop reduced-dimension equipment fault models for various operating points • Fault models evaluated in real-time to detect process drift and/or impending tool failure • May interdict tool operation in mid-run to prevent/reduce scrap • KPIs affected • Process yield and scrap rate through higher detection sensitivity • Equipment availability resulting from fewer false positives • EDA leverage • Conditional triggers in trace request frame data collection • Metadata model contains context to select proper fault detection algorithms • Multi-client access to develop, evaluate, and update fault models
- 13. Data collection alternatives Fault Detection and Classification (FDC) SEMI Standard Level Functionality Benefit GEM/GEM300 Fault models difficult to change after initial development if data collection requirements change Baseline EDA Freeze I (1105) Easy to change equipment data collection plans as fault models evolve and require new data; Model development environment can be separate from production system Engineering labor reduction; improved fault models and lower false alarm rate EDA Freeze II (0710) Use conditional triggers to precisely “frame” trace data while reducing overall data collection needs; Incorporate sub-fab component/subsystem data into fault models Even better fault models; reduced MTTD (mean time to detect) of fault or process excursion; little or no data post-processing required EDA Common Metadata (E164) Include standard recipe step-level transition events for highly targeted trace data collection; Automate initial equipment characterization process by using metadata model to generate required data collection plans Faster tool characterization and fault model development time Factory-Specific EDA Requirements Incorporate previously unavailable equipment signals in fault models; Update data collection plans and fault models automatically after process and recipe changes; Include recipe setpoints in the equipment metadata models TBD (Not yet applicable)
- 14. ROI factors and FDC false alarm costs Hypothetical megafab • Factor values • Number of tools - 2000 • Hour of tool time - $2200 (average raw and finished wafer value) • Qual wafer cost - $250 • Hour of engineering/tech time - $150 • Cost of false alarms • Tool time to resolve (incl. 0.5 hour metrology) – 5 hours • Qual wafers required – 6 • Engineering/tech time required – 2 hours • Cost per false alarm = 4.5*2200 + 6*250 + 2*150 = $11,700 • False alarm rate – 2 per tool per year • Total false alarm cost = $11,700*2000*2 = $46.80M • Benefit of advanced data collection • Reduction in false alarm rate – 50% • Annual savings = $23.4M
- 15. ROI factors and process excursion costs Hypothetical megafab • Factor values • Wafer value - $10,000 (average cost of WIP) • Hour of engineering/tech time - $150 • Cost of process excursions • Wafers per excursion – 500 • Delta yield per excursion – 3% • Engineering time required to resolve – 160 hours • Cost per excursion = 500*10,000*.03 + 160*150 = $174,000 • Excursion rate – 24 per year • Total excursion cost = $174,000*24 = $4.12M • Benefit of advanced data collection • Reduction in # and severity (yield loss) of process excursions – 25% • Annual savings = $1.72M
- 16. Outline • Key messages • Keeping score with ROI • The standardization paradox • Data collection strategy • Example use case • Factory implementation alternatives
- 17. Factory Architecture A Application-driven multi-client EDA connections CVD Furnace CMP GEM300 EI GEM300 EI Tool Etch MES YMS APC, RMS HTTP Factory Information and Control Systems Process Data Storage HSMS AMHS SOA MES, RMS Other DB EDA Applications (EES, PCS) Application Data HTTP HTTP
- 18. Architecture style Wild West – chaotic
- 19. Factory Architecture B Common GEM/EDA Application Connection CVD Furnace CMP GEM300/ EDA EITool Etch MES YMS APC, RMS Factory Information and Control Systems Process Data Storage HSMS / HTTP AMHS Equipment Integration Servers (New) GEM300/ EDA EI SOA MES, RMS Other DB
- 21. Factory Architecture C Fab-wide EDA infrastructure CVD Furnace CMP EDA EI GEM300 EI GEM300 EI Tool Etch MES YMS APC, RMS HTTP Factory Information and Control Systems Equipment Integration Servers (Enhanced) Process Data Storage HSMS AMHS EDA/GEM Time-Series DB SOA EDA Admin EDA Config Context data MES, RMS Other DB
- 22. 감사합니다 唔該 Merci Danke 多謝 ありがとうございます Gracias Acknowledgements and Thanks • Mark Reath, Member, Technical Staff, GLOBALFOUNDRIES • Decades of APC Conference organizers and participants!