Smarter Manufacturing through Equipment Data-Driven Application Design

Smarter Manufacturing through
Equipment Data-Driven Application Design
Alan Weber
Mark Reath
Vice President, New Product Innovations, Cimetrix Incorporated
Senior Member Technical Staff, GLOBALFOUNDRIES
Smart Manufacturing Forum

Mr. Alan Weber
Vice President, New Product Innovations
Cimetrix Incorporated
Education:
• Bachelor’s and Master’s degrees in
Electrical Engineering, Rice University
Experience:
• Semiconductor design automation
• Equipment and factory control system
architectures
• Advanced Process Control (APC) and other
key manufacturing applications
• SEMI Information and Control standards,
especially GEM300 and EDA/Interface A
• Semiconductor manufacturing technology
Presentation Topic: Smarter Manufacturing through
Equipment Data-Driven Application Design

Alan Weber is currently the Vice President, New Product
Innovations for Cimetrix Incorporated. Previously he
served on the Board of Directors for eight years before
joining the company as a full-time employee in 2011.
Alan has been a part of the semiconductor and
manufacturing automation industries for over 40 years.
He holds bachelor’s and master’s degrees in Electrical
Engineering from Rice University.
Speaker Bio

Topic Abstract (1/2)
• Many Smart Manufacturing presentations focus on the connectivity
requirements for the number and diversity of devices that need to
communicate over “Industrial Internet of Things” to achieve the
collaborative decision-making behavior called for in the vision of
Industry 4.0.
• From this perspective, a lot of attention is devoted to the platforms,
protocols, and “plumbing” needed to support these devices, without
discussing the motivations and meaning of their interactions…
thereby leaving much of real problem domain unaddressed.
• In a complex manufacturing environment, most of the information
about the current status and near-term outlook of the factory is
embedded in the equipment itself, so the need to build a “virtual copy
of the physical world” (from Industry 4.0 Wikipedia) is very real.

Topic Abstract (2/2)
• The latest generations of SEMI Standards (GEM and EDA) define
explicit, device-resident metadata models of all the parameters,
events, and alarms that may be produced, so applications can be
programmatically configured to communicate with them with little or
no custom software.
• The challenge that remains is how to use that information to improve
operational performance… in other words, deciding what
manufacturing applications to build.
• In this presentation, the author will relate a number of specific
manufacturing objectives to the applications required to achieve
them and show how the standards-based equipment models directly
support their respective algorithms.

Outline
• What is “Smart Manufacturing?”
• Related SEMI Standards support
• Equipment model value chain
• Advanced smart factory applications
• Conclusions

What is “Smart Manufacturing?”
Again, 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 Standards support
Equipment Data Acquisition (EDA) suite
• Key features
– Ability to 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
Get the data you want…
when and where you need it

Equipment model value chain
Fundamental concept for application integration
EDA Model
(SEMI E164)
High-Volume
Factory Ops
Pilot
Factory
Operations
Process
Engineering
Equipment
Developers
Equipment
Components
Cimetrix
Software
Standardized
Equipment
Models
KPIs (metrics)
• Time to money
• Yield
• Productivity
• Throughput
• Cycle time
• Capacity
• Scrap rate
• EHS
Control Connect Collaborate Visualize Analyze Optimize

Advanced smart factory applications
Current leading edge
• Real-time throughput monitoring
• Precision FDC feature extraction
• Fleet matching and management
• Specialty sensor data access
• eOCAP execution support
• Sub-fab data integration/analysis
• Product and material traceability
Wide range of engineering/operations coverage

Real-time throughput monitoring
Application summary
• Problem statement
– Monitor bottleneck (e.g., litho) tool throughput performance to know
when it drifts away from “normal” for whatever reason
– This is important because any loss of throughput ripples throughout
the line
• Solution components
– Monitor events and calculate process time “on the fly”
– Evaluate context to compare “equivalent” runs; flag outliers
• EDA leverage
– Standard material movement and recipe execution events
• Key ROI factors
– Cycle time

SEMI E90 state machines and model content

E157 state machine, model content, and results

E40 and E94 required context information
High-level
Equipment
structure
JobManager
Module
ControlJob
CarrierInputSpec
attribute
ProcessJob
PRMtlNameList
attribute

Precision FDC feature extraction
Application summary
– Multivariate statistics used to develop reduced-dimension
equipment fault models for equipment operating points
– Fault model accuracy depends on calculating “features”
using trace data collected during key recipe steps
– Multivariate analysis tools
– Context evaluation for grouping fault models into
equivalence classes (“threads”)
• EDA leverage
– Conditional triggers, context data in metadata model,
multi-client access for effective model development
• Key ROI factors
– Delta yield (25% fewer excursions), lower false alarm rate
(50%), rapid excursion detection (50% MTTD, severity
reduction), scrap, equipment uptime, engineering
efficiency

Fleet matching and management
Application summary
– Maintain large sets of similar equipment at same operating point
to maximize lot scheduling flexibility (i.e., no “dedicated” tools)
– Tools drift apart over time, especially when manual adjustments
are made
– Capture equipment configuration and status information
– Track behavior of key equipment mechanisms, independent of
process recipe
• EDA leverage
– Metadata model content at sensor/actuator command level
– Access vector of important equipment constants
• Key ROI factors
– Cycle time (dispatching flexibility), equipment uptime, yield ramp

Specialty sensor data access
Application summary
– Reduce effort required to parse complex sensor data on sensor-
specific local file systems and merge it with the EDA-sourced
equipment data for use in advanced process control applications
– Sensors include OES, RGA, pyrometers, NDIR, Mass spec, high-
frequency RF, QCM, …
– Format conversion, data compression, new EDA metadata types
and interface modules
• EDA leverage
– Multi-client capability, model-based interface definitions, powerful
DCP structure
• Key ROI factors
– Tool availability, test wafer usage, engineering effort

Full Equipment Model
(from process equipment)
Partial Equipment Model
(from sensor integration platform)
Minimal
Equipment
Structure
High-level
Equipment
structure
Process
ChamberProcess
Chambers
Internal
Sensors
External
Sensors
Internal/external sensors see same environment

Process mode-specific data collection
Recipe Operating Mode
Data Collection
Frequency (KHz)
Window of Interest
Duration (ms)
Plasma strike 1 – 10 1000
Wafer temperature ramp 0.1 – 10 5000
Plater hot entry current 1 – 10 1000
ALD process cycle 0.2 – 2 100
Fault Mode
Data Collection
Frequency (KHz)
N/A
Plasma micro-arcing 250
Plasma macro-arcing 1
Plater power supply fault 250
EUV droplet generation 250

Residual Gas Analysis:
CVD Clean Endpoint

RGA application details, results
CVD clean endpoint, wafer repeatability
Excellent wafer-to-wafer repeatability

RGA application details, results
CVD clean endpoint, tool comparison
Multiple wafers, identical tool hardware and clean recipe

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

Smarter Manufacturing through Equipment Data-Driven Application Design

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