decisionsite improves analysis ... - spotfire.co.kr

10/20/2004S.Rugh 1

® 2004 Spotfire User’s Conference Everywhere You Are SM

Title

DecisionSite Improves Analysis Capabilities of a

Commercial Semiconductor Yield Database

Stephen RughYield Enhancement Section Manager

ATMEL Corporation

[email protected]

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Outline

BackgroundProblemInitial GoalsAnalysis ApproachCase Study BenefitsMoving Forward

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Background

ATMEL: Mid-sized manufacturer of semiconductors; primarily non-volatile memories, microcontrollers, RF, and ASIC’s.Colorado Springs Facility

Large 6” wafer fabRun rates near 15,000 wfrs per weekHundreds of productsDozens of process flowsApproximately 2000 employees

Major European Facilities8” fab in France8” fab in England

My focus: Yield enhancement at the Colorado Springs facility.

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Background (continued)

Had a fairly good Oracle-based databasePurchased in the late 1990’sDatabase included wafer level and lot level data

Wafer probe (Yield and bin data)Parametric “Etest” (Vt’s, sheet rho’s, etc.)Inline engineering data (TOX’s, CD’s, etc.)WIP (tool name and move-out times)

Commercially available as a comprehensive semiconductor yield improvement toolSupplied and supported by a large industry vendor

But…

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ProblemExisting analysis tool was poor

Data extract size restrictionsLimitations on the # of productsLimitations on the # of parameters

Couldn’t look at all data for a manufacturing lot in one analysis!Hard-coded queries

WIP queries not formatted correctly for our environmentNo way to modify the queriesVendor not interested in releasing ATMEL specific version

Extract queries slowSome too slow to be useful

Analysis capabilities also slow and cumbersomeParticularly when dealing with equipment correlationsHard to quickly look through a lot of data

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Initial GoalsNOT replace the entire database

Key internal software already written Based on existing schema Extensively used

Same database application/schema used at 2 other major sitesToo painful and costly to start over from scratch

Needed solution that would work for everyone

Solve all of the existing query and analysis issuesGet to a point where all data for a given wafer or lot could be analyzed against all data for possible correlations.

Automate the analysis approachLink into additional databases

Especially those with tool related dataEstablish better links between sort wafer maps and inline defectwafer maps

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Spotfire Query Approach

Link into main engineering database and develop good, generic queries that could be used by all

Lot List: Start by retrieving a lot list based on various test, product, and date conditionsProbe Data: Use lot list table to get data and add as new columnsParametric Data: Use lot list in the existing table to get data, pivot, and add as new columnsInline Data: Use lot list in the existing table to get data, pivot, and add as new columnsWIP Data: Use lot list in the existing table to get data, reformat it, join in tool descriptions, pivot, and add as new columns

Final Result: Large correlation table with one row per manufacturing lot number

Typically includes 2000-3000 columns of dataAll available data for those lots

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WIP Query Problem SolvedLegacy WIP tracking system – used Spotfire tool to help format the data

Multiple steps within a given manufacturing operationExample: 3 steps at operation 100

1. Clean step (tool name = C***)2. Furnace step (tool name = F***)3. Inspect Step (tool name = I***)

Needed to pull each Op_Tool combo as a unique column in order to make the ANOVA analysis meaningful

Op100 correlation not usefulInformation Interaction Designer allowed enough flexibility to create new concatenated WIP columns called:

Op100_COp100_FOp100_I

Now able to run an ANOVA of yield vs. each of these steps

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WIP Query Problem Solved (continued)Additional tool definition table created in a separate database

For each tool, included:Model Type (MXP , 4420XL, L9400, etc.)Location

Created queries to automatically join in this data to look for differences not only between specific tool ID’s, but also by tool type (model) and location.

Lot Equip at Op100_C

Type at Op100_C

Location at Op100_C

Move-Out Date at Op100_C

L1043 C01 MercuryA Fab 1 7/4/04 19:00L1044 C01 MercuryA Fab 1 7/3/04 8:00L1045 C02 MercuryB Fab 1 7/6/04 23:41L1046 C01 MercuryA Fab 1 7/3/04 8:33L1047 C04 MercuryA Fab 2 7/5/04 1:00L1048 C01 MercuryA Fab 1 7/5/04 14:46L1049 C03 MercuryA Fab 2 7/6/04 4:21L1050 C02 MercuryB Fab 1 7/3/04 21:08L1051 C01 MercuryA Fab 1 7/4/04 19:08

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Spotfire Analysis ApproachOnce the large correlation table is built, use the Column Relationships tool (version 7.3) to run:

Linear RegressionOverall yield vs. all probe fail bins, parametric data, and inline engineering data. Sort results based on R-square value.Quickly look at most important correlations.Fine tune as needed: Parametric value vs. inline, etc.

ANOVAOverall yield (or fail bin or parametric value) vs. equipment used at each manufacturing step.

• Tool entity ID, tool type, and locationMark records to create a High-Low column and look at all electrical data vs. the High-Low categorization.

Chi-SquareHigh-Low categorization vs. equipment used at each manufacturing step.

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Case StudyYield Trend

Lot #A2363 A2370 A7179 A7199 A8241 A8258 A8700 A8736

30

40

50

60

70

80

What’s causing these lower yielding lots?

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® 2004 Spotfire User’s Conference Everywhere You Are SM Initial QueryUse Information Library to get starting lot list. - Select from list of generic queries.

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Create Data Table Create the data table:

- Run relevant queries- Add data as new columns

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® 2004 Spotfire User’s Conference Everywhere You Are SMYield vs. Bins

Use Column Relationships tool (Linear Regression) to:- Plot all fail bins against Overall Yield- Sort by descending RSq value- Bin 15 identified as a problem

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® 2004 Spotfire User’s Conference Everywhere You Are SMLR Setup Use Linear Regression to plot all Etest and Inline data against Bin 15.

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® 2004 Spotfire User’s Conference Everywhere You Are SM Bin 15 vs. Etest

Use Linear Regression table to look at- Top Etest/Parametric correlations- Sorted by descending RSq value- VTQ correlation identified

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Bin 15 vs.

Inline

Use Linear Regression table to look at- Top Inline data correlations- Sorted by descending RSq value- BN+ CD correlation identified

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Bin 15 vs.

Equipment

Run an ANOVA calculation:- Bin 15 vs. all equipment- Top correlation to BN+ photo aligner (stepper)

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BN+ Aligner vs.

Inline

Re-run a final ANOVA to look at:- BN+ Aligner vs. all inline data- CD and registration differences noted

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BenefitsAll data now available in one table and can be:

Saved and re-analyzed at later timeShared with others

WIP (Tool) extracts now meaningfulQuery flexibility was key

Analysis fast and simple: Linear Regression, ANOVA, Chi-SqEasy to connect into and extract data from multiple, unrelated databasesAble to duplicate Information Interaction Model to link into databases with same schema at two other major ATMEL fabs.

Required only minor editsOne source now available for all key data

Easy to save and share data via PowerPoint or Word.Short learning curve for basic user

Developing good initial queries hardest

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Additional Benefits

Allows for human-based data-mining!Can analyze all of the data without knowing where to lookQuick and easy to sift between the important and non-importantCan look at this vs. that in seconds

Allows for better understanding of key process variables.

Example:Which CD’s most important?Where are there tool differences?Which inline parameters most affect leakage?

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Moving ForwardNumber of users:

25 total users (licensed and trial evaluation)Two other locations now evaluating

Didn’t solve everything on our initial wish list, but:Almost all issues related to internal database schema issues. For example:

Wafer map coordinate system issues (not X, Y, and bin) Need to join lot based data with tool based data, but no common fields

Wish list provided to SpotfireIncludes:

Ability to save and share the Correlation Relationship tablesBeefed up statistical visualizationsImproved trend chart capabilitiesMore automation

Bottom Line: No other tool as flexible or as good for quickly looking for relationships amongst a large number of variables.

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Business Benefit Summary

Business DriverCurrently used to aid in semiconductor wafer fab yield enhancement activities

UsersWafer fab yield enhancement engineers and process engineers

Application AreaAll fab related manufacturing data, test data,and tool data From multiple sources

Large Oracle databases, various SQL databases, & external Excel files

ROIHas led to improved yields

Can now quickly identify tool differencesEasier to identify optimal process targets

Has led to improved organizational productivityOverall analysis much faster and more complete

decisionsite improves analysis ... - spotfire.co.kr

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