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IMPROVEAn ENIAC Manufacturing Science

Program to Support European Semiconductor Industry

François FinckR&D programs Manager

STMicroelectronicsfrancois.finck@st.com

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SC Industry Context• The semiconductor industry is a key contributor to

European economic growth and prosperity

Nevertheless

• The European semiconductor base is shrinking and more and more companies are choosing to outsource device manufacturing to other regions, mainly to Asia.

SEMI white paper 2008

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Competitiveness Enablers• To maintain and improve its competitiveness the

European SC manufacturing must rely on advanced solutions in Manufacturing science

• The development of these solutions – can only be done through cooperation between

industrialists, SMEs, academia and institutes– must take advantage of the existing technology clusters

around the SC manufacturers– requires the support of Europe and National PA's

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ENIAC first project callSub Programme 8

Target Activity 1: Advanced Line Operation (Manufacturing Science)• SP8-1 Objective:

To allow European device makers to increase the productivity and sustainability of the most advanced CMOS and derivative technologies semiconductor fabs

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Two Technical Challenges for the Future

• Scaling down CMOS (Moore Law)• Managing High mix and heterogeneity

(More than Moore)

• To enable the production of high-quality nanoscale devices at reasonable cost

One Objective

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Scaling Down CMOS

What kind of Process Control Systems do we need to develop to be able to manufacture these devices in

high volumes at reduced cost per die?Source: Intel Ireland Public Relations

LG = 10nmLG = 10nm

20nm Length

25 nm

15nm

15nm Length

65nm Node

45nm Node

90nm Node

32nm Node22nm Node

10nm Length

50nm Length

30nm Length

30nm

Courtesy of Intel

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High Mix and Heterogeneity (typ. fab)

• 10 technology types• 4 to 6 generations of each technology type• > 100 products running concurrently through

the manufacturing fab. • 5000 wafers per Week• several hundred reticle changes per week

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High Mix and Heterogeneity • Challenges in Equipment Effectiveness

– Increase of non productive time (gating metrology, recipe qualifications, wait and down time)

– Stagnating equipment reliability, availability and utilization

– Increasing variations by increased number of equipment per process step (and vice-versa)

– Increasing interaction between process steps– Increasing internal tool complexity

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Manufacturing Science Answers

• Solutions to Process Control Issues– Virtual metrology, dynamic control plan, data

mining, data reduction, data / time synchronization

• Improving Equipment Effectiveness– Predictive Maintenance, remote diagnostics, lots

scheduling and resources planning

Manage FDC strategy, collect data, perform analysis at equipment level

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Implementing Manufacturing science solutions to increase equiPment pROductiVity and

fab pErformance

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IMPROVE Master Objectives

• To improve processes reproducibility and quality

• To improve the effectiveness of production equipement

• To shorten cycles time and improve learning curve

=> To IMPROVE European Fab's Competitiveness

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IMPROVE 3 Manufacturing Science R&D

Topics

• Virtual Metrology• Corrective/ Preventive & Predictive

Maintenance• Dynamic Control Plan

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SPC Chart

47504800485049004950500050505100515052005250

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Lot number

Tre

nc

h D

ep

th

Metrology Data

UCL

LCL

Etch STI Planer

Hours/days delay for standard

metrology

SPC

LRC Etch

Wafer

Voltage, power, OES

etc

Metrology

yY

Y = f(X)

Y

X=[X1,X2,……Xn ]

Virtual Metrology Data

Courtesy of Intel

MetrologyImmediate

Computation for Virtual Metrology

Virtual

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Virtual Metrology• Providing measurement on every wafer in

real time• Improving process control from "run to run"

to "wafer to wafer"– Increasing device quality and yield

• Reducing standard metrology steps– Cycle time improvement– Operating costs reduction

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Corrective/ Preventive & Predictive Maintenance

Equipment context data are available in • Manufacturing Execution System (MES)• Computerized Maintenance Management System

(CMMS)• Recipe Management Systems (RMS).

Scheduled Maintenance

(Over Enginnering)

Corrective Maintenance

(Unpredictable)

EquipmentAvailable to

produce parts

Assist

Present

R2RSPC

FDC

RMSCMMS

MES

Condition Datas

PT…

S.Hubac & al ASMC Conference (Jull 2010)

Equipment Condition Data are there... But use of this information must be... IMPROVEd

Ava

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Equipment process data are available in specific control applications:• Fault Detections & Classification: FDC• Statistical Process Control: SPC• Regulation loop: R2R• Failure and Maintenance history: CMMS

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Corrective/ Preventive & Predictive Maintenance Evolution

Ava

ila

bil

ity

Tar

ge

t

Scheduled

Corrective

EquipmentAvailable to

produce parts

Assist

PredictiveScheduled Maintenance

(Over Enginnering)

Corrective Maintenance

(Unpredictable)

EquipmentAvailable to

produce parts

Assist

Present

Addressing root causes to increase Equipment availability & reliability

R2RSPC

FDC

RMSCMMS

MES

Condition Datas

PT

…

Target

S.Hubac & al ASMC Conference (Jull 2010)

Efficient use of Condition data containing Failure modes, Effect & Detection will allow:

to understand Root Cause(s) on Preventive / Corrective Maintenance which leads to over engineering and/or unscheduled down time.

to consider Prediction by modeling the link between Failure Modes and Detection of Cause(s) & Effect .

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Moving from Reactive to Predictive Equipment Operations

• Reducing unscheduled equipment downtime• Increasing equipment reliability• Reducing number of scrapped wafers• Improving diagnostic and recovery time

thus reducing variability

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Control Plan

Risk Modeling

Target Control Plan

Dynamic Control Plan

Risk Model

Control Rules, Sampling & Limits

Real Time Decisionlot / tool

Failures History and Modeling

Yield lossesEng. KnowledgePhysics

Meas. TechnicsMeas. quality indexCost indexProduction Plan

WIPPrioritiesTool Health

FactorLot dispatching

MetrologyRun to Run FDCVirtual MetrologyWafer to wafer FDC

Dynamic

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Dynamically optimizing the Control Plan with respect to the

real time risk analysis

• Reduction of unnecessary control steps• Reinforcing the control on critical steps• Using Equipment Health Factor to optimize

lot dispatching

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Improve Development Process

– SC Manufacturers• To define problem, provide

data, specify and assess solutions

– Academics• To work on physical and

statistical models– Solution Providers

• To prototype hardware and software tools for development assessment

Data Acquisition

Modeling

Prototypes

Assessment

An extensive vertical collaboration

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IMPROVE skills

– SC Manufacturers

– Academics

– Solution Providers

PhysicalModeling

Diffusion

More Moore

200/300mmLines

More thanMoore

Etch ImplantPhotoLitho

APC Framework

Data Analysis

Simulation Software

Sensors

Non linear Stats.

NeuralNetworks

BayesianNetworks

RiskAnalysis

An extensive horizontal collaboration

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Key figures3600 Men Months over 3 years100 full-time researchersJan 2009 to December 2011

35 Partners over 6 countries

IMPROVE resources

SC manuf.53%

Solutions providers

21%

Univ./lab/Institutes26%

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IMPROVE Consortium• 6 major European SC manufacturers

– LFoundry– INTEL– INFINEON– Austriamicrosystems– Numonyx– ST

• 2 Institutes– Fraunhofer G.– LETI

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IMPROVE Consortium• 10 Solutions Providers

– France:PDF Solutions, Probayes– Germany: Camline, ISYST, InReCon– Ireland: LAM Research, Lexas Research– Italy: Techno Fittings, LAM Research– Portugal:Critical Manufacturing

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IMPROVE Consortium• 12 Academic Labs

– France:EMSE-CMPGC, GSCOP, LTM CNRS– Germany: Augsburg University, FAPS (Erlangen)– Ireland: DCU (Dublin)– Italy: UNIPV, UNIMI, UNIPD, CNR E, CNR IMM – Austria:FH-WN (Wiener Neustadt)

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A novel approach using combination of technologies to estimate wafer’s physical dimensions and electrical performance

An Example of Cooperation

Numonyx

Intel

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Benefits of the Cooperation for Europe

• The IMPROVE project will be a key enabler for 2 main competitive advantages

1. To directly contribute to the competitiveness of the semiconductor fabrication in Europe with the developped solutions• Better process and equipment control at lower cost• Better productivity of equipment• Better cycle time

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Benefits of the Cooperation for Europe

2. To contribute to the creation and reinforcement of a European ecosystem in the semiconductor manufacturing area• Building of a continuous collaboration network in

Manufacturing Science among European actors

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IMPROVing the Eco-system

SCManufacturers

Labs &Academics

SolutionProvidersLong term reinforced

competitiveness for all actors

New Technologies Introduction

New Problems

Expertise Development & Recognition

New Concepts to Implement

Enriched Portofolio,New Markets

More Effective Production Lines

New Tools

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Thank you for your attention

More information available on IMPROVE public web site

www.eniac-improve.eu

IMPROVE project is funded by ENIAC Joint Undertaking and the National Public Authorities of Austria, France, Germany, Ireland, Italy and Portugal