Data Management and Data Analysis Solutions in ... · Data Management and Data Analysis Solutions...

SeUGI 2002 PARIS Data Analysis / J.L.Grolier

www.Altissemiconductor.com

Data Management and Data AnalysisSolutions in Manufacturing :

10 Years Experienceat ALTIS Semiconductor




l Altis Semiconductor

l Yield Management in Manufacturing

l Data Management System

l Data Analysis Solutions

l Data Mining Success Story

l Conclusion

Agenda



l Altis Semiconductor Creation in July 1999 :

Ø 50/50 Joint Venture between IMD (IBM) & Infineon (SIEMENS)

Altis Semiconductor

l Major Advantages :

Ø 35 years of experience in electronic components (Logic products)

Ø Concentration of expertise on the Corbeil Essonnes site

Ø Component mass-production Know-How (DRAM)



l Technologies :

Ø Minimum pattern Size : 0.35-micron to 0.13-micron

Ø Interconnection : Aluminum, Copper (0.18-micron)

Ø Insulation : “Low-K” Dielectric

Altis Semiconductor

l Product Trends :

Ø System On a Chip Concept : Integration on asingle chip of Logic and Memory devices

Ø Power Consumption : Decrease of the Operative Voltage (3.3V to 1.2V)



Altis Semiconductor

l Markets :

Ø TelecommunicationsØ Consumer ProductØ Mobile Phone

l Customers :

Ø Computer Disk drives,Ø Graphic, Audio, Network Cards,Ø Microprocessors & Microcontrollers,Ø Mobile Phones.



Altis Semiconductor

l Corbeil-Essonnes Site :

Ø Location : Corbeil-Essonnes (40 km south of Paris)

Ø Staffing : Altis : 2000 people Partners : 1000 people

Ø Surfaces :Site : 60 haClean Rooms : 35 000 m2

Ø Clean Rooms Specifications :Class 1 : 0.03 particle / literParis : 350 000 particles / liter

Ø Consumptions :Electrical power : 360 Million kWh / yearUltra Pure water : 4 800 m3 / day








l Conclusion

Agenda



Manufacturing Constraints

l Manufacturing Lots :

Ø 25 Silicon Wafers by lot (200 mm diameter)Ø 100 to 2000 chips per WaferØ 150 to 600 parameters by chip

l Manufacturing Steps :

Ø Multiple Operations : -> Multiple Tools typesØ Critical Operations (Bottleneck) : -> High Number of ToolsØ Stability during Time : -> Tools monitoring

l Manufacturing Line :

Ø High level of Investment (1 Billion $)Ø More than 300 Process Steps by ProductsØ Multiple Technologies and ProductsØ Cycle Time : 1 to 2 months



Manufacturing Constraints

l Quality Controls :

Ø (a)- Physical Measurements : -> Dimension, Thickness, Overlay…Ø (b)- Defect detection : -> ContaminationØ (c)- Logistic Flow of Manufacturing Lots

l Electrical Yield :

Ø Each chip is electrically TestedØ Yield calculation by Manufacturing Lot : Y = # good chips /# good & bad chipsØ Yield Detractors : Y = f ( a , b , c)

Ø High Yield => Reduced Manufacturing CostsØ Rapid yield Ramp-up => New Products Startup



l Known Root Cause Mechanism :Ø Expert Knowledge of :

Ø Critical Process StepsØ Major Electrical failuresØ Similar Mechanism on other Products

Yield Management

l Yield Improvement :Ø Engineering on Known Critical StepsØ Action Plans on Known Yield Detractors

l Unknown Yield Drift :Ø Investigations “in all Directions” :

Ø Trend Charts Analysis (Step by Step)Ø Quality Indicator exhaustive ReviewØ Tool Investigations

Ø Experimentations in Manufacturing LineØ Manufacturing Stop Order !!








l Conclusion

Agenda



Data Management System

NT CLIENT PC_____________

Emulation

OPER-1

OPER-2

OPER-3

OPER-4

TEST

PROCESSOPERATIONAL DB

________________Defect Inspection

MetrologyLogistic

Electrical Test SP2 FRAME_____________

File ServerChip DB2

MVS HOST______________Lot, Wafer DB2

RISC STATION_____________

CPUMemory



• Servers :p 200 UNIX Servers,p 80 OS/2 Servers,p 20 NT Servers,p 3000 NT/OS2 Client Stations

• Network :p ATM backbone

p 450 Switching Hubs

p 90 % Token-Ring / Ethernet 100

p 80 km of optical fibers

p 10 000 Connectors

• Data Bases :p 40 DB2 on UNIX Servers,p 8 Oracle DBp 150 Lotus Notes Bases

• Applications :p Manufacturing (200)




Enterprise Storage System

• 4 Tbytes (RAID5)• 3 Tbytes usable

Main Servers• RISC Farm

• 8 Gb Memory

BackUp Server• 14 Tbytes (Tapes)









l Conclusion

Agenda



Data Analysis Solutions

l 10 Years Experience with SAS :

Ø In House SAS Programming Competence (Data Analysis Team)

Ø SAS Base/AF on AIX Server (200 End Users)Ø SAS Base on NT (5 Developers)Ø Enterprise miner on NT (10 Key Users)Ø WebAF on NT (5 Developers for Evaluation)

l Data Analysis Solutions :

Ø ReportingØ Data Extraction & RestitutionØ Specific Analysis :

Ø Wafer Map DisplayØ Test Data TransferØ Defect Killing Factor…



Data Reporting

l Data Reporting :

Ø In-House SAS DevelopmentØ User defined ProfilesØ Daily/Weekly SAS jobsØ Java Web BrowserØ ~ 5000 Updated Charts / Day



Data Analysis

l Simplified Access to Data:

Ø Interactive Queries (GUI)Ø Specific Analysis ModulesØ SAS Tables Treatment (GUI)Ø SAS Code accessible & re-usable

l DataView = “Easy to use” System forData Selection and Graphical Display :

Ø In-House SAS DevelopmentØ Graphical User Interface (GUI)Ø Point & click SelectionØ Deployment to ~ 200 Engineers



Data Analysis

l Data Restitution :

Ø Multiple Graphical Capabilities (GUI)Ø Save & Run of Queries & GraphicsØ Semi-Automatic Reporting



Specific Data Analysis

l Automatic Wafer Map Display :

Ø Daily SAS JobsØ User Defined ProfilesØ Web AccessØ Wafer Map Display of major Yield Detractors








l Conclusion

Agenda



Data Mining Definition

-Data Mining is "the nontrivial extraction of implicit previously unknown andpotentially useful information from data.“-G. Piatetsky-Shapiro, W. J. Frawley

ManagerLeader

Miner



Data Mining Approach

STATISTICS---------

(Statistical Models for known data relationships)

SEMICONDUCTORCAR INDUSTRY

PHARMACY

SIGNAL PROCESSING---------

(Signal Analysis with time)

AERONAUTICSPACE INDUSTRY

ELECTRONIC

DATA MINING---------

(Methods for finding hidden patterns in data)

BANKINSURANCE

RETAIL



Data Mining Process

S e l e c t T r a n s f .

E x t ra c t e d I n f o r m a tio n

S e l e c t . D a ta

D a ta W a r e - h o u s e

D e fin e Ta s k

A p p ly R e s u lts

M i n e V i s u a l i z e U n d e r s t a n d

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

Data Transformation- Usable DataSet- Data Filtering- Data Conversion

Results Validation- Expert Validation- Model Deployment- Knowledge Sharing



Positive Factors of this Project

l Data Management System :

Ø Centralized Technical Data Bases (IBM DB2)Ø High level of Data Integrity

l Data Analysis Expertise :

Ø Early Involvement of Expert in this project (Engineering Team)Ø Data Analysis Know How & Motivation

l Data Analysis Solutions :

Ø In House SAS Programming Competence (Data Analysis Team)Ø Efficient Application for Data Extraction (DataView Application)Ø SAS Enterprise Miner

l Data Mining Effort Distribution :

Ø 50% Data SelectionØ 20% Data MiningØ 30% Results Validation



LOT-Y

EQ-B

EQ-F

EQ-B

TEST

50%

LOT-Z

EQ-C

EQ-G

TEST

60%

OPER-1

OPER-2

OPER-3

OPER-4

TEST

PROCESS

EQ-D

LOT-X

EQ-A

EQ-E

EQ-A

TEST

70%

Tree Decision Methodology

“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%



LOT-Y

EQ-B

EQ-F

EQ-B

TEST

50%

LOT-Z

EQ-C

EQ-G

TEST

60%

OPER-1

OPER-2

OPER-3

OPER-4

TEST

PROCESS

EQ-D

LOT-X

EQ-A

EQ-E

EQ-H

TEST

70%

Tree Decision Methodology

DATA DISCRIMINATION

-> Y(B) < Y(C) < Y(A)

-> Single Tool not Relevant

-> Y(E) = Y(F) = Y(G)

-> Y(B) <<< Y(H)

1-Yield Comparison Tool toTool within each Operation2- Selection of the mostDiscriminated Operation

1st Node : OPER-4,EQ-B Low Yield,EQ-H High Yield



Tree Decision Results : 1st Node

Wet Operation :

- Known problem already detected- Capacity Bottleneck- Technical fixes in implementationphase.



Anneal Operation :

- Unknown problem !!- Unknown mechanism from Experts!!- High Yield Impact !!- Stop Order of EQ-A & EQ-B

Tree Decision Results : 2nd Node



Yield Improvement

l 25% of lots impacted by EQ-A & EQ-B at Anneall 2% Yield Improvementl ROI : SAS Solution paid after 1 month of Yield Recovery



Cleaning PB Anneal PB

Other Examples



Tree Decision Reporting

l TreeView = “Tree Decision Reporting :

Ø In-House SAS Development (SAS/WebAF)Ø User Interface for Parameters (Applet)Ø Weekly SAS JobsØ Web Html & ActiveX outputs








l Conclusion

Agenda



Conclusion

l ”Data Mining Success Story at Altis Semiconductor” :

Ø Clear Efficiency of Tree Decision for Yield ImprovementØ Fully Applicable to Semiconductor Manufacturing (Rules easily understood)Ø Fast, Exhaustive & Systematic Analysis of all possible Root CausesØ Significant Return On Investment

l Data Mining will not replace all Standard Analysis Techniques :

Ø Complementary Data Analysis SolutionØ Centralized Technical Data Bases are mandatoryØ Significant Efforts for Data Extraction & TransformationØ Expensive Data Analysis Solution.

l The Human Expertise is key for Data mining :

Ø Definitions of the relevant parameters for analysisØ Validation of the Root Causes & of the Corrective ActionsØ Learning and Deployment of the Knowledge Discovery found in Database (KDD)



Conclusion

l Data Mining Evolution :

Ø Transposition of other Data Mining Methods to Semiconductor :Ø Data Clustering, Association AnalysisØ Development of Prediction models

Ø Partnership

Ø New Domains of Application :Ø Process Control (APC), Maintenance DataØ Manufacturing & Lot Logistic, Planning & Finance

Ø Data Extraction Development & Deployment Efforts

l Data Analysis Evolution :

Ø In-House SAS Development :Ø High Reactivity to Customer needsØ Good Alternative to the lack of Commercial Offers 5 Years ago

Ø Development & Maintenance Efforts

Ø Today Commercial Alternatives :Ø Complete Packages available for Semiconductor Data Analysis …

Ø ROI to be calculated



Thanks

Ø S.Delabrière (Altis Semiconductor)Ø A.Tran (Altis Semiconductor)Ø A.Chauvet (Altis Semiconductor)Ø E.Perrin (IFITEP)Ø Y.Dupret (ESEO)Ø R.Cheek (IBM USA)

Ø F.Lemoing (SAS)Ø F.Nicorosi (SAS)Ø Y.Pechine (SAS)Ø H.Taalba (SAS)

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