TechXPOT 1F – Smart Manufacturing

SEMICON Taiwan 2017

September 13, 2017

Alan Weber – Cimetrix Incorporated

Smart Manufacturing Requirements forEquipment Capability and Control

Outline

• What is “Smart Manufacturing?”

• Related SEMI EDA* standards

• Smart factory applications

• Equipment design implications

• Conclusions

TechXPOTSmart Manufacturing

*EDA = Equipment Data Acquisition

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…”

Related SEMI standardsEquipment 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

The equipment model value chain

EquipmentModel

High-Volume Factory Ops

Pilot FactoryOperations

Process Engineering

EquipmentDevelopers

EquipmentComponents

CimetrixSoftware

StandardModel KPIs (metrics)

• Time to money

• Yield

• Productivity

• Throughput

• Cycle time

• Capacity

• Scrap rate

• EHS

Control Connect Collaborate Visualize Analyze Optimize

* 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

Origin of the EDA standardsIndustry 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

Origin of the EDA standardsPerformance 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

Worldwide new activities/projectsInteresting 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

Smart factory applicationsCurrent 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

Smart factory applicationsFuture 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

Equipment design implicationsRevolution 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

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

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