Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Smart Manufacturing Requirements for Equipment Capability and Control


Published on

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

Published in: Technology
  • Be the first to comment

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