The key messages of this presentation include Gigafactory and Smart Manufacturing context, Big Data and Data collection strategy as well as the importance of Equipment Models

1. Systema ExpertDay
Dresden, Germany 24 January 2019
Alan Weber – Cimetrix Incorporated
Standards-based Multi-Source Data Collection in
the Smart Manufacturing Era

2.  Key messages
 Gigafactory and Smart Manufacturing context
 Data collection strategy
 SEMI Standards evolution
 Importance of equipment models
 Plug and play application integration
 Conclusions
Outline

3.  Equipment models are the key technology for aligning data
from multiple sources
 Semantics of SEMI E120 (Common Equipment Model) and
E125 Equipment Self-Discovery) can be used across
multiple representations/protocols
 SECS/GEM
 EDA (Equipment Data Acquisition)
 OPC-UA
 Log files
 Modeling principles and guidelines from SEMI E164 (EDA
Common Metadata) likewise apply
 SEMI E134 (Data Collection Management) concepts are
also very useful (plans, reports)
Key messages

4. Gigafactory context
In every minute of every day…
4
GEM messages
coordinate hundreds
of transactions…
GEM300 events
track thousands of
activities…
EDA services
collect millions
of parameters…
Copyright © 2018 Cimetrix. All Rights Reserved.

5.  “… 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…”
What is “Smart Manufacturing?”
From Industry 4.0 Wikipedia…
5

6.  Discoverable
 Autonomous
 Model-based
 Secure
 Self-monitoring
 Easy to use
 Compact
 Standards-based
Components of a Smart Factory
Attributes of all these connected “things”
6
Imagine the collaborative behavior that could emerge !

7.  What are the critical decisions that need to
be made?
 What insights do we need to make these
decisions?
 What information do we need to get these
insights?
 What data must we collect to get the
information we want?
 What sources can provide that data?
Data collection strategy
Start with the goal and work backwards
Source: McKinsey & Company (and Cimetrix)
Insights
Information
Data
Decisions
Sources

8. KPIs, stakeholders, applications, …
Importance of the equipment model
8
Key
Performance
Indicators
(KPIs)
Factory
Stakeholders
Manufacturing
Applications
Data
Equipment
Model

9. The equipment model value chain
Equipment
Model
High-Volume
Factory Ops
Pilot Factory
Operations
Process
Engineering
Equipment
Developers
Equipment
Components
Cimetrix
Software
Standard
E164 Model
KPIs (metrics)
• Time to money
• Yield
• Productivity
• Throughput
• Cycle time
• Capacity
• Scrap rate
• EHS

10.  Process engineers
 Develop and maintain robust manufacturing processes that achieve device operational
specifications (speed, power, size)
 Equipment engineers
 Fleet matching and management to minimize or eliminate the need to dedicate certain
equipment sets for critical process steps and thereby simplify the overall factory scheduling
process
 Maintenance engineers
 Minimize equipment downtime, MTTR (mean time to repair), and test wafer usage required
to bring equipment back to production-ready state
 Industrial engineers
 Monitor equipment and factory throughput in real-time, identify opportunities to eliminate
wait time waste in individual equipment types as well as the overall factory, and address
bottlenecks as they shift and emerge
 Facilities engineers
 Collect and integrate sub-fab data from pumps, chillers, exhaust systems, and other
complex subsystems into the production data management infrastructure for use by a
growing range of analysis applications
Smart Factory stakeholders
and their priorities (engineering)
10

11.  Production control staff
 Determine the material release schedule and manage the factory scheduling/dispatching
systems to accommodate changes in customer orders and/or factory status;
 Application and infrastructure developers
 Define overall factory system design and implementation roadmap to achieve and maintain
competitive advantage, and provide applications that achieve productivity, quality, and
reliability objectives of the industrial, process, and equipment engineers
 Automation and integration specialists
 Connect equipment to factory system infrastructure and related applications and verify
compliance to automation purchase specifications
 Procurement/Supplier Relations
 Manage the overall purchasing process to support factory customers while achieving capital
budget targets
 Factory management
 Responsible to executive management for competitive manufacturing results
Smart Factory stakeholders
and their priorities (operations, other)
11

12. Evolution of equipment models
Referenced in industry standards
Natural language analogy…
 SECS-I vocabulary (data items)
 SECS-II grammar (streams/functions)
 GEM sentences (capabilities)
 GEM300 conversations (scenarios)
 EDA improv theatre (dynamic DCPs)
 E164 improv theatre with a point (common model)
 OPC-UA spontaneous flash mob (IIoT, …)

13. EDA Equipment Metadata Model
Static structure and dynamic behavior

14.  Model structure exactly reflects tool
hardware organization
 Complete description of all potentially
useful information in the tool
 Always accurate, always available – no
additional documentation required
 Common point of reference among tool,
process, and factory stakeholders
 Source of unambiguous identifiers/tags
for database [auto] configuration
 Enables “plug and play” applications
Standard metadata model benefits
First defined by SEMI E164
Process Module #1
Gate Valve Data
Substrate Location
Utilization
More Data,
Events, Alarms
Process Tracking
Other
Components

15. SEDD – SECS Equipment Data Dictionary
Schema and examples
….

16. OPC-UA (Unified Architecture)
Information Model examples

17.  What is it?
 Application integration concept
 Equipment directly supports application capability
without additional programming or configuration
 Applications can make assumptions about semantics of
tool data (i.e., about what things mean)
 Who benefits and how?
 Factory customers greatly reduce application
development/integration cost and time to market
because of commonality across equipment types
Thoughts on “plug and play”
In the context of Smart Manufacturing

18. Plug and play application integration
Example tool data required (1)
Application Tool Data Required
OEE (Overall Equipment Effectiveness) Transition events and status codes sufficient to classify equipment states for all time periods
Intra-tool material (substrate) tracking Material tracking events; material location state indicators and state change events
Process execution tracking
Start/stop events for all processing modules; recipe step indicators and step change events for
all processing modules that support multi-step recipes
WTW (wait time waste) analysis
(aka Product Time Measurement)
The combination of events required for the intra-tool material flow and process execution
tracking applications (see above) and context data required to classify material states for all
time periods (see the SEMI E168 Product Time Management standard for a deeper explanation)
Time-based PM
(Preventative Maintenance)
Run timers at the FRU (field replaceable unit) level
Usage-based PM
Usage parameters and accumulators appropriate for each FRU, such as time-in-state, execution
cycles, fluid flow rates, consumables flow rates, power consumption, etc.
Condition-based PM Meaningful “health indicators” for each FRU
FDC (Fault Detection and Classification)
Equipment/process parameters required by specific fault models and associated context
information (this is difficult to do completely because most FDC models are “trained” with
knowledge of “good” and “bad” runs, which is not known to the OEM a priori)

19. Plug and play application integration
Example tool data required (2)
Application Tool Data Required
Equipment configuration monitoring
Vector of important equipment constants with expected values and acceptable ranges; may
need to support multiple sets, if the values are setup-dependent. Designed to catch human
error resulting from operator manual adjustments.
Component fingerprinting
Performance parameters for key equipment mechanisms, including command/response signals
at the sensor/actuator level
Static job scheduling Setup and execution times per product/recipe combination and current setup information
Real-time job dispatching
Estimate of current job completion time; estimate of completion time for all material queued at
the equipment
Factory cycle time optimization Material buffer contents, job queue information
Real-time dashboard Equipment/component production status indicators
Equipment failure analysis Meaningful alarm/fault codes and recent history/statistics
Remote recipe selection
Recipe directory with meaningful header information (version #, checksum, etc.) to support
reliable selection
Recipe verification Checksum calculation spec to compare equipment-resident recipe with host version
Run-to-run control Identification of recipe adjustable parameters and commands to remotely update them

20. Conclusions
• Models help components of a Smart Manufacturing
system understand one another
• Models have been at the core of SEMI's
Information and Control standards for decades
• The sophistication of these models is now sufficient
to support “plug and play” application integration
• Industry standard models can greatly reduce
factory application development/integration cost
• Models will play an increasingly important role as
the number and variety of components multiply

21.  감사합니다
 唔該
 Merci
 Danke
 多謝
 ありがとうございます
 Gracias
Acknowledgements and Thanks
 Systema Expert Day organizers and participants !