1 IMPROVE An ENIAC Manufacturing Science Program to Support European Semiconductor Industry François Finck R&D programs Manager STMicroelectronics

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 call Sub 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) To enable the production of high-quality nanoscale devices at reasonable cost One Objective

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

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 wafers per Week several hundred reticle changes per week

8 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

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 Lot number Trench Depth Metrology Data UCL LCL Etch STI Planer Hours/days delay for standard metrology SPC LRC Etch Wafer Voltage, power, OES etc Metrology y Y Y = f(X) Y X=[X 1,X 2,……X n ] Virtual Metrology Data Courtesy of Intel Metrology Immediate Computation for Virtual Metrology Virtual

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

16 Corrective/ Preventive & Predictive Maintenance Evolution Availability Target  Scheduled Corrective Equipment Available to produce parts Assist Predictive Scheduled Maintenance (Over Enginnering) Corrective Maintenance (Unpredictable) Equipment Available to produce parts Assist Present Addressing root causes to increase Equipment availability & reliability R2R SPC FDC RMS CMMS 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.

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

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 – 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

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

22 Key figures 3600 Men Months over 3 years  100 full-time researchers  Jan 2009 to December 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 A novel approach using combination of technologies to estimate wafer’s physical dimensions and electrical performance An Example of Cooperation

27 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

28 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

29 IMPROVing the Eco-system SC Manufacturers Labs & Academics Solution Providers Long 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

30 Thank you for your attention More information available on IMPROVE public web site IMPROVE project is funded by ENIAC Joint Undertaking and the National Public Authorities of Austria, France, Germany, Ireland, Italy and Portugal