Smart Manufacturing Requirements for Equipment Capability and Control

Transcription

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

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 Control Connect Collaborate Visualize Analyze Optimize Pilot Factory Operations Equipment Components Equipment Developers Cimetrix Software Standard Model Equipment Model Process Engineering KPIs (metrics) Time to money Yield Productivity Throughput Cycle time High-Volume Factory Ops Capacity Scrap rate EHS

6 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 * EDA Common Metadata standard

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 postprocess 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 vendorspecific 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 wellknown 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