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SC Industry Context

IMPROVE An ENIAC Manufacturing Science Program to Support European Semiconductor Industry François Finck R&D programs Manager STMicroelectronics francois.finck@st.com. SC Industry Context. The semiconductor industry is a key contributor to European economic growth and prosperity Nevertheless

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SC Industry Context

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  1. IMPROVEAn ENIAC Manufacturing Science Program to Support European Semiconductor IndustryFrançois FinckR&D programs ManagerSTMicroelectronicsfrancois.finck@st.com

  2. 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

  3. 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

  4. 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

  5. Two Technical Challenges for the Future • Scaling down CMOS (Moore Law) • Managing High mix and heterogeneity (More than Moore) One Objective • To enable the production of high-quality nanoscale devices at reasonable cost

  6. 30nm 90nm Node Scaling Down CMOS 65nm Node 45nm Node 32nm Node 22nm Node 50nm Length 30nm Length Courtesy of Intel 20nm Length 15nm Length 10nm Length 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

  7. 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

  8. High Mix and Heterogeneity • Challengesin 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

  9. 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

  10. Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance

  11. 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

  12. IMPROVE 3 Manufacturing Science R&D Topics • Virtual Metrology • Corrective/ Preventive & Predictive Maintenance • Dynamic Control Plan

  13. SPC Chart Metrology Virtual Metrology Data Virtual Metrology Data 5250 UCL Immediate Computation for Virtual Metrology 5200 y Y 5150 5100 Trench Depth 5050 Y = f(X) 5000 4950 Y 4900 X=[X1,X2,……Xn ] 4850 4800 LCL 4750 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Lot number SPC Hours/days delay for standard metrology Voltage, power, OES etc Metrology Courtesy of Intel Wafer LRC Etch Planer STI Etch

  14. 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

  15. Corrective/ Preventive & Predictive Maintenance Equipment context dataare available in Manufacturing Execution System (MES) Computerized Maintenance Management System (CMMS) Recipe Management Systems (RMS). Scheduled Maintenance (Over Enginnering) High level of unexpected events Condition Datas Corrective Maintenance (Unpredictable) MES … FDC • 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 PT SPC Assist R2R RMS Equipment Available to produce parts Availability to be improved CMMS Equipment Condition Data are there... But use of this information must be... IMPROVEd Present S.Hubac & al ASMC Conference (Jull 2010)

  16. Corrective/ Preventive & Predictive Maintenance Evolution • Addressingroot causes to increase Equipment availability & reliability Predictive Scheduled Maintenance (Over Enginnering) Scheduled Corrective Efficient use of Condition data containing Failure modes, Effect & Detection will allow: Assist Condition Datas Corrective Maintenance (Unpredictable) Equipment Available to produce parts … MES FDC Availability Target PT SPC Assist R2R RMS Equipment Available to produce parts • 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 . CMMS Present Target S.Hubac & al ASMC Conference (Jull 2010)

  17. 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

  18. Failures History and Modeling • Yield losses • Eng. Knowledge • Physics Dynamic Control Plan Real Time Decisionlot / tool Risk Modeling Dynamic Control Plan • WIP • Priorities • Tool Health Factor • Lot dispatching Risk Model Control Rules, Sampling & Limits • Meas. Technics • Meas. quality index • Cost index • Production Plan Target Control Plan • Metrology • Run to Run FDC • Virtual Metrology • Wafer to wafer FDC

  19. 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

  20. Improve Development Process An extensive vertical collaboration • 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 Assessment Data Acquisition Modeling Prototypes

  21. IMPROVE skills An extensive horizontal collaboration • SC Manufacturers • Academics • Solution Providers 200/300mm Lines More Moore More than Moore Implant Photo Litho Diffusion Etch Risk Analysis Non linear Stats. Neural Networks Bayesian Networks Physical Modeling Data Analysis APC Framework Simulation Software Sensors

  22. Key figures • 3600 Men Months over 3 years • 100 full-time researchers • Jan 2009 to December 2011 35 Partners over 6 countries

  23. IMPROVE Consortium • 6 major European SC manufacturers • LFoundry • INTEL • INFINEON • Austriamicrosystems • Numonyx • ST • 2 Institutes • Fraunhofer G. • LETI

  24. 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

  25. 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)

  26. An Example of Cooperation Numonyx Intel A novel approach using combination of technologies to estimate wafer’s physical dimensions and electrical performance

  27. Benefits of the Cooperation for Europe • The IMPROVE project will be a key enabler for 2 main competitive advantages • 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

  28. Benefits of the Cooperation for Europe • 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

  29. IMPROVing the Eco-system New Tools More Effective Production Lines Enriched Portofolio, New Markets SC Manufacturers New Technologies Introduction Solution Providers Long term reinforced competitiveness for all actors New Concepts to Implement New Problems Expertise Development & Recognition Labs & Academics

  30. 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

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