Data Usage. SEMICON Japan ISMI NGF Briefing and e-manufacturing Workshop December 2, 2008



Similar documents
ISMI Predictive Preventive Maintenance Implementation Guideline

Semiconductor Equipment Security: Virus and Intellectual Property Protection Guidelines Harvey Wohlwend harvey.wohlwend ismi.sematech.

Equipment Modeling in EDA

Engineering Optimization through the qualified use of CMMS and Predictive Software

Welcome & Introduction

Understanding Manufacturing Execution Systems (MES)

Proactive Asset Management with IIoT and Analytics

Approaches for Implementation of Virtual Metrology and Predictive Maintenance into Existing Fab Systems

BIG DATA ANALYTICS: THE TRANSFORMATIVE POWERHOUSE FOR BIOTECH INDUSTRY ADVANCEMENT. David Wiggin October 8, 2013

Operational Business Intelligence in Manufacturing

This paper describes Digital Equipment Corporation Semiconductor Division s

IoT Changes Logistics for the OEM Spare Parts Supply Chain

Is a specialized database application used by the manufacturing industry to reduce production costs by controlling tool usage, inventory, rework and

TestScape. On-line, test data management and root cause analysis system. On-line Visibility. Ease of Use. Modular and Scalable.

How to Improve Tablet PCs and Other Portable Devices with MEMS Timing Technology

HONEYWELL TURBOCHARGING

Lean manufacturing in the age of the Industrial Internet

RFID current applications and potential economic benefits

Scheduler/Dispatcher User Requirements

Improving production and operation systems with RFID. Taking manufacturing to the next level: RFID Work-in-Process solutions from IBM

Dave Bloss - Intel Corporation

Best Practices for Verification, Validation, and Test in Model- Based Design

Chapter 9 Reliability Centered Maintenance

Lean Manufacturing and Six Sigma

Big Data Analytics and Decision Analysis for Manufacturing Intelligence to Empower Industry 3.5

BYD Builds a Smart Factory with the Innovation Control Center and SAP MaxAttention

Ten Critical Questions to Ask a Manufacturing ERP Vendor

INTELLIGENT DEFECT ANALYSIS SOFTWARE

Caterpillar Automatic Code Generation

Revised April (May) 2015

Maximizing return on plant assets

LOOP Technology Limited. vision. inmotion IMPROVE YOUR PRODUCT QUALITY GAIN A DISTINCT COMPETITIVE ADVANTAGE.

Achieve greater efficiency in asset management by managing all your asset types on a single platform.

Improve the product lifecycle

Tailoring your Quality Management System in a Competitive Marketplace Heather Driscoll Infor Presales Business Solutions Consultant

Empirix OneSight for VoIP: Avaya Aura Communication Manager

Semiconductor Equipment Security Guidelines Virus Protection

Does enterprise resource planning software support lean? ERP goes 44 TARGET AME.ORG/TARGET

The Recipe for Sarbanes-Oxley Compliance using Microsoft s SharePoint 2010 platform

Achieve greater efficiency in asset management by managing all your asset types on a single platform.

Ten Critical Questions to Ask a Manufacturing ERP Vendor

Vamshi K. Katukoori Graduate Student: NAME University Of New Orleans. Standardizing Availability Definition

Florida SUPPLY CHAIN MANAGEMENT. Executive Summary

A 10-Minute Guide to Increasing Supply Chain Visibility

Achieve greater efficiency in asset management by managing all your asset types on a single platform.

TPM OVERVIEW. Manufacturing & Administrative Excellence. 1

IBM Sterling Warehouse Management System

MANAGING LINEAR ASSETS Managing Linear Assets has always been a challenge; find out how customers leverage SAP to meet industry requirements.

Solution Recipe: Remote PC Management Made Simple with Intel vpro Technology and Intel Active Management Technology

Utilizing KolibriMFG Software System to Schedule and Control Shop Floor

Diagnosing Hydraulic Problems

White Paper May Seven reports every supply chain executive needs Supply Chain Performance Management with IBM

Alarm Response Management

Equipment Master Standardization

WATER InfRAsTRucTuRE MAnAgEMEnT

Predictive and Prescriptive Analytics An Example: Advanced Sales & Operations Planning

Gian Luca Sacco Marketing Director South & Central Europe. Smarter decisions, better products.

The Turning of JMP Software into a Semiconductor Analysis Software Product:

Industrial Automation. A Manufacturing Revolution in Automotive and Industrial Equipment

The Semiconductor Industry: Out in Front, but Lagging Behind Tom Mariano Published September, 2014

ARTICLE IN PRESS. LeanThinking. journalhomepage: Driving Value in The Upstream Chain Management Through Lean Principles

Epicor. Service Management

Lawrence S. Farey. 193 S.W. Seminole Drive Aloha, OR 97006

ni.com/vision NI Vision

Maximizing Equipment Uptime

Operations & Maintenance 101 Maintenance Strategies and Work Practices to Reduce Costs

INVENSYS OPERATIONS MANAGEMENT: AUTOMATING THE FUTURE OF A SUSTAINABLE SAN FRANCISCO. Invensys Operations Management

Lean Principles by Jerry Kilpatrick

About Redtail Telematics

RACK AND CONTAINER TRACKING SOLUTION

Hessel Visser NCOI Les 6 A P 373. Operations Management, 7E: Chapter 14 en15

System Integration. System Integration. Global Manufacturing

Pneumatic Proportional Valve Selection Made Simple

IBM RFID for Supply Chain and Logistics: Reusable Asset Tracking solution

Master Protection Extended Powertrain Warranty Program. Master Maintenance And Full Maintenance Programs

gimm Global Integrated Manufacturing Manager. Solution for industrial management of productive and logistic factory processes

Perimeter Security System

Digital Controllers ACTUATOR COMPATABILITY

INTELLIGENT DEFECT ANALYSIS, FRAMEWORK FOR INTEGRATED DATA MANAGEMENT

FITMAN Future Internet Enablers for the Sensing Enterprise: A FIWARE Approach & Industrial Trialing

TPM at the heart of Lean - March 2005 Art Smalley

Maintenance Plan. Many of the individual components of a comprehensive maintenance program are listed below, along with brief descriptions.

Program & Portfolio! Management using! Kanban! Copyright 2013 Davisbase Consulting. Limited Display License Provided to ASPE

BPMN Process Design for Complex Product Development and Production

Contamination Transport from Wafer to Lens

Data Science & Big Data Practice

Purchasing Final. Ch.3 The Legal Aspects of Purchasing

Diesel Rotary UPS reliability and process availability

AIR CONDITIONING LIFE EXTENDER ACX10 FIELD INSTALLATION AND PERFORMANCE EVALUATION

Transcription:

Data Usage Accelerating Manufacturing Productivity SEMICON Japan ISMI NGF Briefing and e-manufacturing Workshop December 2, 2008 David Stark David.Stark@ismi.sematech.org 512-356-3278 Copyright 2008 SEMATECH, Inc. SEMATECH, and the SEMATECH logo are registered servicemarks of SEMATECH, Inc. International SEMATECH Manufacturing Initiative, ISMI, Advanced Materials Research Center and AMRC are servicemarks of SEMATECH, Inc. All other servicemarks and trademarks are the property of their respective owners.

Agenda Two projects that require high quality data from equipment: Predictive Preventive Maintenance (PPM) Enhanced Equipment Quality Assurance (EEQA) What, Why, Who, When,

Agenda - PPM What is Predictive Preventive Maintenance (PPM)? Why develop and implement PPM? cost and productivity impact Who is already doing PPM (other industries)? will perform PPM in the semiconductor industry? equipment supplier and device maker responsibilities When will PPM development and deployment occur? Resources

What is PPM? PPM is an application that uses mathematical algorithms applied to tool and process data to sense tool component failure and tool performance degradation before they occur Simple example: Your vehicle Change the oil and filter every 3 months or 3000 miles is a time-based or usebased scheduled maintenance PPM applied to oil and filter change may monitor oil viscosity, pressure drop across the filter, oil clarity, engine resistance at idle, etc., and report a countdown until oil and/or filter change is required

What is PPM? PPM for semiconductor factories will apply mathematical models to equipment and factory data to predict tool failure and then act on those predictions to optimize factory productivity PPM will be developed for key equipment where Technically possible, and Adequate business value exists This is NOT every tool Equipment side PPM will monitor tool and predict failure Factory side PPM will make business rule based decisions about scheduling PM, parts, personnel, and WIP

Why Develop and Implement PPM? Cost and productivity impacts Reduced unscheduled equipment downtime, since impending failure is sensed and advance warning is issued Avoid sudden disruption to work in progress (WIP) flow through the factory [reroute WIP] Avoid complete loss [scrap] or rework cost for in-process WIP Reduced time to diagnose and repair since PPM identifies failure part or subsystem [lower maintenance cost] Equipment engineer can review the correct maintenance procedure Needed parts identified in advance and pulled from inventory or JIT Increased net uptime/availability

Why Develop and Implement PPM? Cost and productivity impacts Reduced cost of parts, since lifetime of scheduled parts, replaced under time-based PM policies may be extended Reduced variability of tool availability decreases cycle time (modeling shows 5-10% cycle time reduction for zero variability in MTBF and MTTR in reference model)

Who is already doing PPM? (in other industries) PPM is famous in Automotive industry in the modern automobile, especially the engine Toyota in the car/truck assembly line Aeronautics General Electric aircraft engines Defense and Space programs Advanced fighter jet NASA and International Space Station Heavy Industry John Deere, Komatsu in earth moving equipment Power plant generators

Who will perform PPM in IC industry? Equipment suppliers will develop PPM in the tool envelope most complete data resident knowledge of tool and component design installed base field performance Device makers can add a layer to the equipment model for recipe mix, metrology, or yield data will develop the factory-side applications that use the equipment PPM application signals to make decisions about maintenance scheduling, messaging, WIP routing, and parts

Equipment PPM Equipment Data PCA Visualization Prediction Information

ISMI PPM Interaction with Equipment Suppliers Equipment Supplier 1 MC MC MC MC MC WG ISMI 1:1 1:1 1:1 1:1 1:1 Equipment Supplier 2 Equipment Supplier 3 Equipment Supplier 4 Focused engagement in H2-2008 Development Demo - Pilot Generic guidelines to full community 1:1 Equipment Supplier 5 Equipment Supplier n

Documentation Pilots Demos Development When Will PPM Development and Deployment Occur? Contracting

PPM Resources from ISMI ISMI Consensus Preventive and Predictive Maintenance Vision Guideline http://ismi.sematech.org/docubase/abstracts/4819ceng.htm ISMI Predictive and Preventive Maintenance Equipment Implementation Guideline http://ismi.sematech.org/docubase/abstracts/4934aeng.htm

PPM Questions and Answers

Agenda Enhanced Equipment Quality Assurance (EEQA) What is EEQA? Why develop and implement EEQA? cost and productivity impacts Who will perform EEQA? equipment supplier and device maker responsibilities When will EEQA development and deployment occur? Resources

What is EEQA? EEQA is an element of the Equipment Engineering System (EES) that has been proposed by JEITA in collaboration with International SEMATECH and SELETE since 2000 EEQA is using Equipment Engineering Data to enhance the ability to establish and validate the equipment s performance The foundation is the Equipment Engineering Data Data that adequately describes the functional performance of the equipment Should utilize Interface A and the metadata structure

Data Hierarchy EQPT Specific tool ID serial # MODULE 1..n Transfer, Process 1, Process 2, SUBSYSTEM 1..n Gas, Vacuum, RF, Chuck, COMPONENT 1..n MFC1..n, shutoff valve 1..n This dataset, the Interface A metadata, includes all atomic data, static and dynamic, for the subject tool

What about data for EEQA? EEQA requires IDM access to all the Interface A atomic metadata, AND Functional performance data Time dependant hardware performance Example: wafer handling wafer move performance distributions, pump down time distributions, others Trace data transformation to information Example: MFC, Power Supply, RF Match, Pumping Ramp time, Overshoot amplitude, Settling time, Integrated performance to setpoint (mean, stdev), Shutoff time, transients/noise Effective process time In a Standardized Way, so multiple applications can Uniformly Access the data

Why Develop and Implement EEQA? Cost and productivity impacts to device maker All tool data consuming applications will access the structured dataset for their purpose EEQA functional performance value-added data available from the equipment in a standardized way Decreases cost to develop applications Increases effectiveness of applications [PPM, ECM, APC,VM,...] Equipment Supplier can use EEQA concepts for Improved quality in-house and up the supply chain Improved field support capability Installation validation

Who will Perform EEQA? Equipment suppliers most complete data resident knowledge of tool and component design installed base field performance Device makers will develop the VMfactory-side applications that use the equipment EEQA data ECM APC PPM Graphic from the JEITA documents

When will EEQA Development and Deployment occur? Develop functional performance data from metadata and establish ability to collect the value added data using the Interface A port (through 2009)

Resources JEITA 300mm Prime Guidelines www.jeita-smtj.com/pdf/300p_glv2_phase2.pdf Requirement on Enhanced Equipment Quality Assurance upon Equipment Installation (EEQA) http://jeitasmtc.elisasp.net/pdf/request%20on%20eeqa%20english%20 Translation%20V1.0.pdf

EEQA Questions and Answers