Data Management and Data Analysis Solutions in Manufacturing : 10 Years Experience at ALTIS Semiconductor

Transcription

1 Data Management and Data Analysis Solutions in Manufacturing : 10 Years Experience at ALTIS Semiconductor

2 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

3 Altis Semiconductor Altis Semiconductor Creation in July 1999 : 50/50 Joint Venture between IMD (IBM) & Infineon (SIEMENS) Major Advantages : 35 years of experience in electronic components (Logic products) Concentration of expertise on the Corbeil Essonnes site Component mass-production Know-How (DRAM)

4 Altis Semiconductor Technologies : Minimum pattern Size : 0.35-micron to 0.13-micron Interconnection : Aluminum, Copper (0.18-micron) Insulation : Low-K Dielectric Product Trends : System On a Chip Concept : Integration on a single chip of Logic and Memory devices Power Consumption : Decrease of the Operative Voltage (3.3V to 1.2V)

5 Altis Semiconductor Markets : Telecommunications Consumer Product Mobile Phone Customers : Computer Disk drives, Graphic, Audio, Network Cards, Microprocessors & Microcontrollers, Mobile Phones.

6 Altis Semiconductor Corbeil-Essonnes Site : Location : Corbeil-Essonnes (40 km south of Paris) Staffing : Altis : 2000 people Partners : 1000 people Surfaces : Site : 60 ha Clean Rooms : m2 Clean Rooms Specifications : Class 1 : 0.03 particle / liter Paris : particles / liter Consumptions : Electrical power : 360 Million kwh / year Ultra Pure water : m3 / day

7 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

8 Manufacturing Constraints Manufacturing Line : High level of Investment (1 Billion $) More than 300 Process Steps by Products Multiple Technologies and Products Cycle Time : 1 to 2 months Manufacturing Steps : Multiple Operations : -> Multiple Tools types Critical Operations (Bottleneck) : -> High Number of Tools Stability during Time : -> Tools monitoring Manufacturing Lots : 25 Silicon Wafers by lot (200 mm diameter) 100 to 2000 chips per Wafer 150 to 600 parameters by chip

9 Manufacturing Constraints Quality Controls : (a)- Physical Measurements : -> Dimension, Thickness, Overlay (b)- Defect detection : -> Contamination (c)- Logistic Flow of Manufacturing Lots Electrical Yield : Each chip is electrically Tested Yield calculation by Manufacturing Lot : Y = # good chips /# good & bad chips Yield Detractors : Y = f ( a, b, c) High Yield => Reduced Manufacturing Costs Rapid yield Ramp-up => New Products Startup

10 Yield Management Known Root Cause Mechanism : Expert Knowledge of : Critical Process Steps Major Electrical failures Similar Mechanism on other Products Yield Improvement : Engineering on Known Critical Steps Action Plans on Known Yield Detractors Unknown Yield Drift : Investigations in all Directions : Trend Charts Analysis (Step by Step) Quality Indicator exhaustive Review Tool Investigations Experimentations in Manufacturing Line Manufacturing Stop Order!!

11 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

12 Data Management System PROCESS OPER-1 OPER-2 OPERATIONAL DB Defect Inspection Metrology Logistic Electrical Test SP2 FRAME File Server Chip DB2 MVS HOST Lot, Wafer DB2 OPER-3 OPER-4 NT CLIENT PC Emulation RISC STATION CPU Memory TEST

13 Data Management System Servers : 200 UNIX Servers, 80 OS/2 Servers, 20 NT Servers, 3000 NT/OS2 Client Stations Network : ATM backbone 450 Switching Hubs 90 % Token-Ring / Ethernet km of optical fibers Connectors Data Bases : 40 DB2 on UNIX Servers, 8 Oracle DB 150 Lotus Notes Bases Applications : Manufacturing (200)

14 Data Management System Enterprise Storage System 4 Tbytes (RAID5) 3 Tbytes usable Main Servers RISC Farm 8 Gb Memory BackUp Server 14 Tbytes (Tapes)

15 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

16 Data Analysis Solutions 10 Years Experience with SAS : In House SAS Programming Competence (Data Analysis Team) SAS Base/AF on AIX Server (200 End Users) SAS Base on NT (5 Developers) Enterprise miner on NT (10 Key Users) WebAF on NT (5 Developers for Evaluation) Data Analysis Solutions : Reporting Data Extraction & Restitution Specific Analysis : Wafer Map Display Test Data Transfer Defect Killing Factor

17 Data Reporting Data Reporting : In-House SAS Development User defined Profiles Daily/Weekly SAS jobs Java Web Browser ~ 5000 Updated Charts / Day

18 Data Analysis DataView = Easy to use System for Data Selection and Graphical Display : In-House SAS Development Graphical User Interface (GUI) Point & click Selection Deployment to ~ 200 Engineers Simplified Access to Data: Interactive Queries (GUI) Specific Analysis Modules SAS Tables Treatment (GUI) SAS Code accessible & re-usable

19 Data Analysis Data Restitution : Multiple Graphical Capabilities (GUI) Save & Run of Queries & Graphics Semi-Automatic Reporting

20 Specific Data Analysis Automatic Wafer Map Display : Daily SAS Jobs User Defined Profiles Web Access Wafer Map Display of major Yield Detractors

21 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

22 Data Mining Definition -Data Mining is "the nontrivial extraction of implicit previously unknown and potentially useful information from data. -G. Piatetsky-Shapiro, W. J. Frawley Leader Manager Miner

23 Data Mining Approach SIGNAL PROCESSING (Signal Analysis with time) STATISTICS (Statistical Models for known data relationships) DATA MINING (Methods for finding hidden patterns in data) AERONAUTIC SPACE INDUSTRY ELECTRONIC SEMICONDUCTOR CAR INDUSTRY PHARMACY BANK INSURANCE RETAIL

24 Data Mining Process WFT Yield Analysis - Yield Distribution - Yield Vintage - Root Causes & Impact Data Selection - Massive Queries - Multiple DataBases - Key parameters Data Mining - Method Selection - Pattern Discovery - Modeling Apply Results Extracted Information Define Task Data Warehouse Select. Data Select Transf. Mine Visualize Understand Data Transformation - Usable DataSet - Data Filtering - Data Conversion Results Validation - Expert Validation - Model Deployment - Knowledge Sharing

25 Positive Factors of this Project Data Mining Effort Distribution : 50% Data Selection 20% Data Mining 30% Results Validation Data Management System : Centralized Technical Data Bases (IBM DB2) High level of Data Integrity Data Analysis Solutions : In House SAS Programming Competence (Data Analysis Team) Efficient Application for Data Extraction (DataView Application) SAS Enterprise Miner Data Analysis Expertise : Early Involvement of Expert in this project (Engineering Team) Data Analysis Know How & Motivation

26 Tree Decision Methodology PROCESS LOT-X LOT-Y LOT-Z OPER-1 EQ-A EQ-B EQ-C OPER-2 EQ-D OPER-3 OPER-4 EQ-E EQ-A EQ-F EQ-B EQ-G Post-Mortem Analysis : - EQ-B is guilty? - 0 x EQ-B : Lot-X =70% - 1 x EQ-B : Lot-Z =60% - 2 x EQ-B : Lot-Y =50% TEST TEST TEST TEST 70% 50% 60%

27 Tree Decision Methodology PROCESS LOT-X LOT-Y LOT-Z DATA DISCRIMINATION OPER-1 EQ-A EQ-B EQ-C -> Y(B) < Y(C) < Y(A) OPER-2 EQ-D -> Single Tool not Relevant OPER-3 EQ-E EQ-F EQ-G -> Y(E) = Y(F) = Y(G) OPER-4 EQ-H EQ-B -> Y(B) <<< Y(H) TEST TEST TEST TEST 1-Yield Comparison Tool to Tool within each Operation 2- Selection of the most Discriminated Operation 70% 50% 60% 1st Node : OPER-4, EQ-B Low Yield, EQ-H High Yield

28 Tree Decision Results : 1st Node Wet Operation : - Known problem already detected - Capacity Bottleneck - Technical fixes in implementation phase.

29 Tree Decision Results : 2nd Node Anneal Operation : - Unknown problem!! - Unknown mechanism from Experts!! - High Yield Impact!! - Stop Order of EQ-A & EQ-B

30 Yield Improvement 25% of lots impacted by EQ-A & EQ-B at Anneal 2% Yield Improvement ROI : SAS Solution paid after 1 month of Yield Recovery

31 Other Examples Cleaning PB Anneal PB

32 Tree Decision Reporting TreeView = Tree Decision Reporting : In-House SAS Development (SAS/WebAF) User Interface for Parameters (Applet) Weekly SAS Jobs Web Html & ActiveX outputs

33 Agenda Altis Semiconductor Yield Management in Manufacturing Data Management System Data Analysis Solutions Data Mining Success Story Conclusion

34 Conclusion Data Mining Success Story at Altis Semiconductor : Clear Efficiency of Tree Decision for Yield Improvement Fully Applicable to Semiconductor Manufacturing (Rules easily understood) Fast, Exhaustive & Systematic Analysis of all possible Root Causes Significant Return On Investment Data Mining will not replace all Standard Analysis Techniques : Complementary Data Analysis Solution Centralized Technical Data Bases are mandatory Significant Efforts for Data Extraction & Transformation Expensive Data Analysis Solution. The Human Expertise is key for Data mining : Definitions of the relevant parameters for analysis Validation of the Root Causes & of the Corrective Actions Learning and Deployment of the Knowledge Discovery found in Database (KDD)

35 Conclusion Data Mining Evolution : Transposition of other Data Mining Methods to Semiconductor : Data Clustering, Association Analysis Development of Prediction models Partnership New Domains of Application : Process Control (APC), Maintenance Data Manufacturing & Lot Logistic, Planning & Finance Data Extraction Development & Deployment Efforts Data Analysis Evolution : In-House SAS Development : High Reactivity to Customer needs Good Alternative to the lack of Commercial Offers 5 Years ago Development & Maintenance Efforts Today Commercial Alternatives : Complete Packages available for Semiconductor Data Analysis ROI to be calculated

36 Thanks S.Delabrière (Altis Semiconductor) A.Tran (Altis Semiconductor) A.Chauvet (Altis Semiconductor) E.Perrin (IFITEP) Y.Dupret (ESEO) R.Cheek (IBM USA) F.Lemoing (SAS) F.Nicorosi (SAS) Y.Pechine (SAS) H.Taalba (SAS)