Interactive Server Side Data Mining and DecisionSite … · Rule Induction Pigin MahaCu Rev MahaCu Web Browser ... Middle, Edge), Octant, Quad, ... Workflow Example 4 C D AB 1 5 3

Applied Materials Confidential

Applied Global Services (AGS)

Interactive Server Side Data Miningand DecisionSite Integrated with

Applied Materials PEC

Paul Proctor Spotfire User Conference 2004October 5, 2004



Process Excursion Control (PEC)

Yield Improvement – Reduce Wafer Processing Cost

Reduce the number of experiment iterations required to isolate a root cause

– Increase ThroughputEliminate mismatched tool conditions that restrict product flow

– Improve Target Product QualityCorrelate non-obvious dependent and independent variable relationships that impact the process capability window

Increased Tool Operational Efficiency – Improve tool availability

Receive event, warnings and alarm notifications from each tool in real time in order to provide faster response time

– Decrease Mean Time to RepairCorrelate specific time stamped sensor information for root cause identificationSensor data visualization for fab-fab, tool-tool, chamber-chamber contrasting



Technology Features - Data Available for Analysis

XXXXXXXXXLot

XXXXXXXXXWafer

XXXXXXReticle

XXXXDie

SWRDefectEtestInline Metr-ology

BitmapSbinSorteDiagMESGranu-larity



Data Mining Architecture

PEC Technology

MES

Probe

InlineMetrology

ElectricalTest

Fab Wide Data Mining Server

eDiagnostics& Data Mining

Web GUI


Web GUI

Web Server

eDiagnostics TechnologyAMAT TSP

Tool 1AMAT TSP

Tool n

TSP eDiagnosticsServer

sBB sBB


Web GUI

DataStorage

100 – 150 Points @ 1Hz

Defect

Qual Wafer Data



EDM Data Mining Overview

Export for internal analysis -DS, SAS, JMP, .xls, .txt.

Intranet Web Delivery

Data Gather Data Prep

Customer- Dbase Extract- EI- Raw Test

Data (KLArf)- SAS, Excel, etc

AMAT- Format all

raw data files- Build Data

Squares- Data Clean

CustomerGeneratedDataCubes

Direct Linkto Customer

Database

DataBrainTMDataCubeAnalysis Visualization

Data MiningSOMRule InductionPiginMahaCuRev MahaCu

Web Browser-Charts-Report Creation

(.ppt Export)-Data Available(.txt Export)

Bin (Probe)

Parametric

Tools (MES)

Defect

Equipment Interface



PEC Service Process FlowAutomated Correlation Analysis using EDM；Enterprise Data Mining

– Sort/ Probe: Die level functional test data (Bin) on All Production Lots– WAT/ E-Test: Parametric data on Test Structure and Test Wafers (Sight Level, Die Level)– Defect/ In-line Metrology: KLARF Daa, Bitmap, EQC/ WQC Data…etc– MES: Lot History and Lot Level Summary Data (In-line Metrology…etc)– eDiagnostics: Process Trace data on Tool Sensor Level

Optimization Action Plan Proposal based on Correlation Analysis Result– Determine Rogue Tools and Process Excursions/ Margin Issue– Propose Action Plan to eliminate Yield detractors caused by Tool/ Process/ Layout Design– Prioritize Optimization Action Plans based on Customer ROI impact– Propose and execute eDiagnostics hooking up and process monitoring– Propose DOE and Test Strategies

Optimization Action Plan Excution/ Qualification on Tool Process– Optimization and Maintenance on AMAT Tool/ Process– Propose/ Qualify OAP on Non-AMAT Tool/ Process– Attaching AMAT eDiagnostics capability and Set Up/ Maintenance for Tool Monitoring



Data Mining Analysis Tools

Self Ordered Neural Network Maps (SOM)– Entire data set filtered and fitted with

potential independent variable causes

MahaCu and Pigin Algorithms– Lot and wafer based analysis

SOM Clustering– Wafer level filtering of Probe results

Zonal Analysis– Eight zones capable including Radial

(Center, Middle, Edge), Octant, Quad, Photo shot, etc.

Defect Methodology– Kill Ratio Analysis– Drill down to Tool/Chamber– Drill down to Sensor

IV: P10132 CD:NDIFF,W=0.42,70% ( MT: 20.481 ); (Mean: 0.485, SD: 0.093)

DV: YIELD(Mean: 32.111, SD: 11.939)

LOWHIGH



Zonal Analysis - Wafer Map

8 Zonal configurations are standard, Radial, Octant, Qual, Photo shot, etcAll AMAT techniques can be use to analyze zonal dependences

Radial zone definition



Defect Kill-RatioStep ID: POLS, Fine Bin: 61

• ~3.3% of die affected• Kill-Ratio ~ 0.275 (conservative estimate) •% of dies killed=3.3*0.275=0.9



DV: YIELD(Mean: 32.111, SD: 11.939)

LOW

SOM ProblemDefinition

Complex AnalysisCorrelation To

In-Line Metrology

MES Process ToolCorrelations for EachMetrology Parameter

Chamber Sensor TraceAnalysis to Source

Complex Analysis Automated Mechanism



EDM helps to narrow down the root cause findings

Workflow Example

4

DC

A B

1

5

3

E

2

LLBLLA

F

Service Interface

PEC/eDiagnostics ServerTDV, DCM, EMA, TPR, EDM eDiagnostics System

Interface (eDSI)Tool

Dedicated Network

Service Interface

Tool Sensor Data

Mining Analysis

• Real Time Data Viewer

• Historical Data

• Data Export to Excel

• Wafer based, Lot based and Time based sensors charting

• Multiple sensors per chart with wafer to wafer, lot to lot, and tool to tool overlay

• Wafer and lot level statistics

• Lot and wafer based search capability

4

DC

A B

1

5

3

E

2

LLBLLA

F

Process ToolTool Data Viewer (TDV)

Comparison analysis using data mining:

• Tool+Chamber vs. Sensor• Sensor vs. Metrology or Etest data• Yield vs Tool+Chamber



Tool/Chamber MatchingFingerprinting Report: Overview• Displays each sensor and corresponding recipe step which describes differences between chambers, recipes, or other categorization

Fingerprinting Report: Analysis• Group wafers by chamber, recipe, or other categorization• Statistical analysis of sensor data for all specified wafers to determine differences

Fingerprinting Report: Visualization• Integrated full Sensor Investigation visualization

functionality for analyzed data



Practical Example of Datamining

Analysis Case Study 1



Case Study 1

Module engineer wants to perform data-mining on in-line CD metrology and the corresponding tool sensor data to determine if any correlations exist.

Module Engineer is looking for an early warning predictor in the eD data so that a proactive intervention can be performed on the etcher “before” the product CD drifts to far.

TMN would be used to monitor and alarm if control limit(s) are violated on predictor sensor(s)

Overall Project goal was to improve CD CpK as well as optimize and extend the PM window with confidence.



Etch CD Analysis Workflow using Tool Sensor DataDefine wafer sensor data set for analysis, including:Date Range, Tools, Recipes, Chambers, Lots, and/or Slots



Etch CD Analysis Workflow using Tool Sensor Data

Define name for job and include external data for analysis, in this case provide CD data



Load in-line CD Metrology Data into the Fingerprinting Data-mining Analysis Engine

Use wafer list to create CD data for measured wafers

Simple Tab delimited ASCII table format



Select analysis to be performed and include external data to be used in analysis



Etch CD Analysis Workflow using Tool Sensor Data• Perform analysis run to automatically generate list of items which correlates with categories• Examine result report to determine which categories correlate with CD, e.g., chamber, tool, recipe• Examine results to find sensor values collected, corresponding with recipe step which correlates with CD

Automatic drill down analysis of sensor data to determine sensors most likely to contribute to CD variation



Selecting Average value for Throttle Valve Step shows significant correlation with CD data (values are grayed out)

Further analysis will be performed to optimize the PM cyclein order to significantly reduce CD variation



Compare Sensor Traces of High and Low Wafers

Higher AvgThrottle Valve Step

Lower AvgThrottle Valve Step



Practical Data-Mining Applications Summary

Corrective Maintenance and Optimization Work Flows– Chamber-Chamber Performance Comparison– Process Chamber PM Trending Scenario– Statistical Excursion Investigation Scenario– Tool Down Signature and Root Cause Matching– Up Tool Verses Down Tool Comparison– Down Tool Comparison with Normal Operation Signature

Reference– Performance Analysis across a lot

Fingerprinting Data-Mining Methods– Automated and manual methods– Ability to import metrology and defectivity data sets for

automated fingerprinting and analysis



Applied EDM/PEC &

Spotfire DecisionSite



Datamining Challenges

Many Statistically Valid findings are flagged by the EDM Analysis Engine

A rapid way of assessing the validity and impact of each of the findings is necessary

Rapid Visualization Tools are the best approach to perform the Findings Assessment Step

Applied has utilized JMP, EXCEL and other general visualization packages

Spotfire DecisionSite interface is being integrated into our EDM technology to allow seamless and fast assessment of findings



Data Mining Summary Results



Example Data Mining Finding



Transfer to Spotfire for Visualization



Data Mining Findings Pareto



Data Mining Findings by Score



Review EDM Results for Finding



EDM Data Cube imported to DS



High Bin Fail Lots



Wafer Map Gallery



ET vs. ET



N-Tool Count by Lot



Active Tools at each Stage ID



Documents

Interactive Server Side Data Mining and DecisionSite … · Rule Induction Pigin MahaCu Rev MahaCu Web Browser ... Middle, Edge), Octant, Quad, ... Workflow Example 4 C D AB 1 5 3