integrated equipment data collection and management

13
16 March 2018 Cimetrix Incorporated Integrated Equipment Data Collection and Management for Smart Manufacturing

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Page 1: Integrated Equipment Data Collection and Management

16 March 2018

Cimetrix Incorporated

Integrated Equipment Data

Collection and Management for

Smart Manufacturing

Page 2: Integrated Equipment Data Collection and Management

Outline

• Key messages

• Smart Manufacturing context

• Factory stakeholders

• Equipment model value chain

• SEMI Standards support

• Integrated data management

• Application examples

Page 3: Integrated Equipment Data Collection and Management

Key messages

• Stakeholder needs drive all requirements

• Equipment models are key technology • Content determines system capability

• Management tools determine user experience

• Service-oriented architectures enable smooth technology evolution through decoupling

• Current SEMI Standards provide direct support

• Consistent user experience is vital for stakeholder satisfaction and system adoption

• Chinese semiconductor industry is in perfect position to leverage all the above

Page 4: Integrated Equipment Data Collection and Management

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…”

Page 5: Integrated Equipment Data Collection and Management

Factory stakeholders KPIs, applications, equipment models…

Key Performance Indicators

(KPIs)

Factory Stakeholders

Manufacturing Applications

Data

Equipment Model

Page 6: Integrated Equipment Data Collection and Management

Equipment model value chain Fundamental concept for application integration

Control Connect Collaborate Visualize Analyze Optimize

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

Page 7: Integrated Equipment Data Collection and Management

SEMI Standards support Why is E164* so important?

• Consistent implementations of GEM300

• Commonality across equipment types

• Automation of 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

Page 8: Integrated Equipment Data Collection and Management

Equipment data management Integrated production system architecture

GEM/EDA Gateway

HSMS / HTTP

Equipment Gateways

GEM/EDA Gateway

SOA

MES

YMS

APC, RMS

Factory Information and Control Systems

Process Data

Storage

AMHS

EDA/GEM Time-Series

DB

Equipment Data Mgmt

Support

Data Mgmt Config

MES, RMS,

Other DB CMP

Furnace

Tool Etch

CVD

New Mfg Applications

Page 9: Integrated Equipment Data Collection and Management

Equipment data management Major system components

• Model Manager

• Plan Manager

• Equipment Gateway

• Data Mapper

• Data Router

• Data Repository

• Data Scope

• Synchronization Engine

• Performance Monitor

• Administrator Toolkit

Page 10: Integrated Equipment Data Collection and Management

New manufacturing 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

• Automated equipment characterization

Wide range of stakeholder coverage

Page 11: Integrated Equipment Data Collection and Management

Application example Real-time throughput monitoring

• 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

• Equipment model leverage

• Standard material movement and recipe execution events

• Context available at event occurrence

• Key ROI factors

• Cycle time, productivity excursion MTTD (50% reduction), equipment throughput improvement (3-5%)

Page 12: Integrated Equipment Data Collection and Management

Real-time throughput monitoring SEMI E90 state machines and model

Page 13: Integrated Equipment Data Collection and Management

•謝謝

•감사합니다

• Merci

• Danke

•多謝

•ありがとうございます

• Gracias

Thank you