Connectivity Challenges of Smart Manufacturing

Connectivity Challenges of Smart Manufacturing

Addressing Connectivity Challenges of Disparate Data Sources in Smart Manufacturing Smart Manufacturing Pavilion Meet the Experts Stage Alan Weber September 19, 2019 Outline Problem statement Gigafactory context Smart Manufacturing data sources Unifying concepts Characteristics Challenges Example solution architectures Questions

Problem statement Background Data collection is the principal enabling technology for maintaining a Smart Factorys digital twin The diversity of data sources in Smart Manufacturing environments is growing, not shrinking Goals Access important information in these data sources with minimal custom software Leverage existing data collection infrastructure to seamlessly integrate data from all sources Gigafactory context In every minute of every day EDA services collect millions

of parameters GEM messages coordinate hundreds of transactions GEM300 events track thousands of activities Copyright 2019 Cimetrix. All Rights Reserved. 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 Outline Problem statement Gigafactory context Smart Manufacturing data sources Unifying concepts Characteristics Challenges Example solution architectures Questions Semiconductor Smart Manufacturing Data sources (current sampling) SEMI Standards-compliant interfaces

EDA/Interface A SECS/GEM/GEM300 Custom interfaces Purpose-specific (EUV data collection) External sensors (RGA, OES, ) Equipment log files (many examples) OPC-enabled equipment/subsystems OPC Classic DA (Data Acquisition) OPC UA (Unified Architecture) IIoT Issues with SEMI EDA data sources Equipment models are vendor specific Models may or may not reflect equipment

structure, depending on when they were defined Models may not provide sufficient visibility into equipment behavior Interface performance may not match expectations if control system was not redesigned All of this is improving rapidly as fabs take a more prescriptive approach to their automation specs Valid Entries: Comply Comply by (Date) Partially Comply (Notes) Do no comply N/A EDA adoption status Cumulative # of production tools installed

All are now requiring latest SEMI Standards for equipment modeling and data acquisition Principal Motivation: Flexibility Data collection plan Changing requirements Multi-client architecture We are here ! 9 Unifying concepts/relationships Generalizable from SEMI EDA standards Manufacturing Stakeholders and their KPIs

Data Collection Plans Equipment Data Consumers Data Equipment Model Unifying concepts Metadata model and data collection plan (DCP)

Shared equipment model benefits Driven by factory requirements Structure exactly reflects equipment hardware organization Provides complete description of all useful information in the equipment Always accurate and available no additional documentation required* Common point of reference across all factory and supplier stakeholders Source of unambiguous information for database configuration Reduces integration engineering * As long as it can be queried from the equipment Other Components

Process Module #1 Gate Valve Data Substrate Location Utilization More Data, Events, Alarms Process Tracking Issues with SEMI GEM data sources Equipment model is not explicit GEM/GEM300 will persist as the principal

command and control interface Data collection mechanisms are fixed and limited Event reporting Trace reporting Variable status queries Equipment model must be derived from lists of variables, events, constants, state machines, etc. The recent SEMI E172 standard (SECS Equipment Data Dictionary) offers a partial solution But must be specified if it is to be delivered SEDD SECS Equipment Data Dictionary Schema and examples .

GEM equipment model structure Embedded in E172 (SEDD)elements Issues with OPC UA data sources OPC UA is an architecture, not a standard Set of architectural specifications for creating integrated factory systems Systems include manufacturing equipment, applications, data repositories, and the other components that interconnect them all Interoperability of the system components depends on their respective suppliers having chosen compatible and similarly scoped profiles Because of the breadth of OPC UAs applicability and variety of implementation options, this itself is a significant system engineering task

Interoperability of OPC UA components Requires compatible mappings and profiles Figure 1 The OPC UA Stack Overview (from Volume 6) Figure 1 Profile Conformance Unit Test Cases (from Volume 7) Issues with external sensors Implementations are factory specific Typical challenges. 1. Finding a sensor that works 2. Sampling/process synchronization 3. Dealing with multiple timestamps 4. Scaling and units conversion 5. Applying factory naming convention

6. Associating context and sensor data 7. Ensuring statistical validity 8. Aligning results in process database Outline Problem statement Gigafactory context Smart Manufacturing data sources Unifying concepts Characteristics Challenges Example solution architectures Questions Example solution architecture EDA-based sensor integration

FICS / MES To factory-level systems Process-specific applications GEM Factory-level EDA Client Apps (DOE, FDC, PHM, ) EDA Client EDA HTTP

Process Engineering Database HTTP HTTP OEM Tool EDA Client Pump I/F Smart Data Model

Public Data Raw Data Metadata Model TP EDA Server Context data Synchronization data DCIM DCIM*

Custom or EtherCAT Proprietary Applications Sensor/Actuator Gateway TCP/IP Device Drivers Synchronization signals TP DP

A S1 S2 S3 * DCIM = Data Collection Interface Module Issues with equipment log files Their formats are custom designs Optimized for ease of creation NOT consumption

Type of information included varies Mixture of events, parameters, alarms Mixture of critical data and just in case stuff Parameter values often stored in native, binary form Format may change throughout the log Not just a simple set of identical records Multiple sections, headers, record layouts, even files Issues with equipment log files They disappear over time Usually circular file system Fixed limits for file sizes and number When limits are reached, oldest files are overwritten Retention period may vary with activity And available storage space

Issues with equipment log files They reside on the tool Part of the local file/directory system Access methods dictated by platform technology Special permissions may be required to keep from invalidating tool warranty They depend on the tools clock So the timestamps are almost always wrong May be able to correct reports if offset from factory reference clock is tracked continuously Example solution architecture Equipment Log File Processing XML/Text Model

Editor Data Source Models (1 per tool type) Data Source Model Validator elastic Filebeat Log Files Equipment Control Platform

Data Collection Plan SEMI E134 .wsdl (schema) elastic logstash EDA Server Log File Processor Isolates Custom content DataSourceModel

.xsd (schema) EDA NewData Reports Trace data Event data Context data Factory EDA Client Software Process Data

Repository (Historian) Key system components Data Source Model (DSM) Foundation for entire system architecture Could be derived from EDA equipment metadata model Identifies type/name of the system that generated log file E.g., process equipment, supplier, tool type, model name, etc. Maps contents of custom log file into standard tool data reports using unique keys for items of interest Keys are assigned in the log file parser Keys appear in correct equipment structural context in the DSM, resulting in proper sourceId (location) and parameterName

Includes optional elements for Data type declaration: necessary for subsequent report processing Units conversion: raw binary to scaled engineering units Event augmentation: generate enumerated state values Key system components Data source parser This is where most of the NRE (non-recurring engineering effort) will be spent Some of this will be custom code, but it is also possible to use commercial ETL (extraction, transformation, and loading) software in many cases Example: elastic Filebeat and Logstash products Perhaps elasticsearch and Kibana for centralized storage and visualization as well The back end of each parser is a standard EDA-compliant data

report generator Output format for all sources is the same This is NOT rocket science just tedious Implementation process For each vendor/equipment type Analyze format and content of log file Identify data items of interest Dont have to collect everything in the log file Develop custom parser, assign keys to items of interest Create Data Source Model with keys in hierarchical context May also derive it from EDA equipment metadata model Include scaling, units, and new state value elements as needed Validate with DSM validation utility

Add new event states and other parameters to client applications and data collection infrastructure Acknowledgements and Thanks SEMI staff and standards volunteers for decades of support ! Danke Merci Grazie Gracias Visit us at www.cimetrix.com www.cimetrix.com/cn

www.cimetrix.com/tw www.cimetrix.com/kr www.cimetrix.com/jp Outline Problem statement Gigafactory context Smart Manufacturing data sources Unifying concepts Characteristics Challenges Example solution architectures Questions

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