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

1. TechXPOT 1F – Smart Manufacturing
SEMICON Taiwan 2017
September 13, 2017
Alan Weber – Cimetrix Incorporated
Smart Manufacturing Requirements for
Equipment Capability and Control

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. 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. 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. 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. * 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. 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. 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. 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. 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. 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. 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. 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. 감사합니다
唔該
Merci
Danke
多謝
ありがとうございます
Thank you