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Smart Manufacturing Requirements for Equipment Capability and Control

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TechXPOT 1F – Smart Manufacturing
SEMICON Taiwan 2017
September 13, 2017

Published in: Technology
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Smart Manufacturing Requirements for Equipment Capability and Control

  1. 1. TechXPOT 1F – Smart Manufacturing SEMICON Taiwan 2017 September 13, 2017 Alan Weber – Cimetrix Incorporated Smart Manufacturing Requirements for Equipment Capability and Control
  2. 2. Outline • What is “Smart Manufacturing?” • Related SEMI EDA* standards • Smart factory applications • Equipment design implications • Conclusions TechXPOT Smart Manufacturing *EDA = Equipment Data Acquisition
  3. 3. What is “Smart Manufacturing?” From Industry 4.0 Wikipedia… • “… cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. • Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time…”
  4. 4. Related SEMI standards Equipment Data Acquisition (EDA) suite • Key features • Query equipment for its metadata model • Multiple independent client applications • Powerful Data Collection Plan (DCP) structure • Support for “data on demand” • Performance monitoring and notification features • Web-based communications technologies • Seamless integration to “smart factory” applications Get the data you want… when and where you need it
  5. 5. The equipment model value chain Equipment Model High-Volume Factory Ops Pilot Factory Operations Process Engineering Equipment Developers Equipment Components Cimetrix Software Standard Model KPIs (metrics) • Time to money • Yield • Productivity • Throughput • Cycle time • Capacity • Scrap rate • EHS Control Connect Collaborate Visualize Analyze Optimize
  6. 6. * EDA Common Metadata standard Why is E164* so important? Common metadata results in… • Consistent implementations of GEM300 • Commonality across equipment types • Automation of many data collection processes • Less work to interpret collected data • Enables true “plug and play” applications • Major increases in engineering efficiency E164 is to EDA what GEM was to SECS-II
  7. 7. Origin of the EDA standards Industry motivation (circa 2001) • Needed flexible approach for collecting and distributing high-density real-time equipment and process data • Fault detection algorithms were evolving from lot-level post- process application to within-process diagnosis and tool interdiction capabilities • Run-to-run control applications moving from lot level to wafer level • Only alternatives were custom interfaces or vendor- specific data collection systems (i.e., expensive) • EDA provided standard approach across tool types supporting a common client/host data collection system
  8. 8. Origin of the EDA standards Performance expectations • GEM-based data collection limitations • Maximum trace data frequency typically 1 Hz • Collection event aligned with substrate movement and recipe start/stop • OK for material tracking, OEE reports, and lot-level FDC and R2R control • GEM interface fixed or “locked down” to avoid tool performance problems • Process engineers needed more/better data on their terms • At least 10 Hz frequency at recipe step boundaries • 100 Hz frequency for critical, rapidly changing parameters • Precise data “framing” for advanced predictive algorithms • Dynamic sampling in response to changing process conditions • Define new data collection plans (within limits) without additional sign-off
  9. 9. Worldwide new activities/projects Interesting EDA use cases • Key industry initiative support • Smart Manufacturing, Industry 4.0 • ROI-driven factory application development • Specific yield, revenue, productivity benefits • FDC, WTW, eOCAP, Queue time reduction,… • Sub-system integration • Cymer laser analysis/”smart data” feed • Edwards sub-fab component gateway • External specialty sensors (OES, RGA,…) • Multi-source data aggregation • “Big data” analysis feeds
  10. 10. Smart factory applications Current leading edge • Real-time throughput monitoring • Precision FDC feature extraction • Specialty sensor data access • Fleet matching and management • eOCAP execution support • Sub-fab data integration/analysis • Product and material traceability Covers wide range of engineering/operations careabouts
  11. 11. Smart factory applications Future possibilities • Recipe-driven DCP generation • Automated tool characterization • Equipment mechanism fingerprinting • Specialty sensor data repository sampling • Post-PM tool auto-requalification • Wafer-less process requalification • Process-specific control strategies • Disparate data source aggregation Even broader impact on manufacturing KPIs
  12. 12. Equipment design implications Revolution in equipment control… • Understand distinction between equipment- and process-induced failure modes • Support sensor-specific sampling frequencies • Provide built-in DCPs and control algorithms for well- known failure modes • Support full visibility into important tool behavior in equipment metadata model • Implement first principles-based control where feasible • Provide “sockets” for proprietary sensor integration • Establish clear equipment data ownership boundaries
  13. 13. Conclusions • The latest generation of SEMI EDA standards directly supports Smart Manufacturing initiatives • Robust equipment models are the key to advanced application support and manufacturing KPI improvement • Equipment suppliers have an essential role to play in implementing these standards • Equipment purchase specifications must go beyond the current standards in the areas of performance and visibility
  14. 14. 감사합니다 唔該 Merci Danke 多謝 ありがとうございます Thank you

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