NIWeek 2016 - "Breaking Data Silos"

Breaking Data Silos

Michael Schuldenfrei, CTO

NI Week 2016 - Test Leadership Forum

© Optimal+ 2016, All Rights Reserved

Optimal+ in the Semiconductor Industry

Over 90%Foundry &

OSAT coverage

YieldUp to 2%

Quality50% less escapes

Efficiency Up to 20%

35B+Chips (in 2015)

TTRUp to 30%

And others ….

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© Optimal+ 2016, All Rights Reserved 3

Optimal+ Solution Architecture (Semi)

FinanceInventory

Billing Supply

Procurement

Manage-ment

CLIENT APPLICATIONS• Analytics•Queries• Rules• Simulations

APPLICATIONSERVERS

PROXY SERVER

E-TEST DATA LOG

OPERATIONS CLIENT

Test Floors Fabless / IDM Headquarters

Factory A

Factory BFactory C

Alerts & Linked Reports

Guidance & Requests

WAFER SORT TESTER

FINAL TESTTESTER

SLT TESTER

MES

OPTIMAL+ DATABASE(Cloud or On-Premise)

One Point of Truth between Engineering, Operations, Planners, Finance and Management

Data – Dramatically Enhances Yield, Efficiency & Quality

4© Optimal+ 2016, All Rights Reserved

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Yield Reclamation

Site-to-site variances, equipment issues, etc.



Efficiency

In this example the tester is retesting 97% of bad dice (blind retest) with only 1 die gain


Quality – Escape Prevention

Other examples:Good die/device with“out of spec” test resultsFailing tests in good partsIncorrect number of testsFreeze detectionParametric trendsProcess capability (CPk)

Example: Probe mark trackingThe algorithm tracks probe marks per each die at wafer sort and compares with a specified value


Quality – Outlier Detection

Wafer Geography – die near the edge of the wafer are generally less reliable than those in the center of the wafer

Die Neighborhood – die that are surrounded by large numbers of failing die on the wafer

Parametric Outliers – die with individual test results that are statistically significantly different than the rest of the population

Multivariate Outliers – die where combinations of test results are statistically different than others

Geographic Outliers (colored blue)

Parametric Outliers

Data Silos and Why they Matter

9© Optimal+ 2016, All Rights Reserved


Data Silos in Semiconductor Test

E-Test WS FT 1Burn-

inFT 2

Manufacturing Flow

Devices typically go through multiple test steps…

SLT

Subcon 2SubconFoundry



E-Test WS FT 1Burn-

inFT 2

Manufacturing Flow

At multiple locations…

SLT

Subcon 2Subcon 1Foundry



E-Test WS FT 1Burn-

inFT 2

Manufacturing Flow

No data is typically shared between the testing locations…

SLT




E-Test WS FT 1Burn-

inFT 2

Manufacturing Flow

Or even within a location

SLT




E-Test WS FT 1Burn-

inFT 2

Manufacturing Flow

But what if we can BREAK DOWN THE SILOS?

SLT

Connect data from across multiple operations for:

• Offline analysis

• Wafer map reconstruction

• RMA investigation

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Cross Operation Analysis


DB WAT WS1 WS2 Assy. FT1 Burn-in FT2 SLT

Implementations:Within the same test area (e.g. WS, FT, etc.)Between test areas (e.g. from WAT to WS to FT)Within a single subconBetween multiple subcons (hub and spoke)Real-time (test program integration)Offline bin-switching

Example scenarios:Outlier Detection – drift analysisPairing – cherry-picking for power & speed combinationsTest program tuningSLT / Burn-in reduction

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Data Feed Forward – Make it Actionable




Detect Drift between Two Operations

Tester

1. ECID Data

2. FT1 Measurements

Test Program running

FT2 operationReal-time data!

No test time impact!

Database


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The Challenge:

Burn-in is very expensive – runs for up to 120 hours on large numbers of chips

Burn-in is traditionally a required step for critical components (e.g. medical, automotive)

So by confidently predicting which parts WON’T fail burn-in,

we can reduce the number of tested parts and significantly cut

costs!

Example: Reducing Burn-in




Wafer Geography – die near the edge of the wafer are generally less reliable than those in the center of the wafer

Die Neighborhood – die that are surrounded by large numbers of failing die on the wafer

Parametric Outliers – die with individual test results that are statistically significantly different than the rest of the population

Multivariate Outliers – die where combinations of test results are statistically different than others

Low Yielding Wafers – die on wafers with unusually poor yield

How to distill these into a single criteria for burn-in? Quality Index!

Determining Quality – Multiple Factors

Geographic Outliers (colored blue)

Parametric Outliers


A numeric value representing the perceived quality of a part based on:

• Wafer geography (e.g. edge vs. center)

• Outlier detection rule inputs (e.g. GDBN, Z-PAT, D-PAT, etc.)

• Number of iterations to PASS

• Overall lot/wafer yield

• Equipment health during test

• Parametric test results from multiple operations

• Etc…

Quality Index can be used in many applications

• Burn-in reduction

• Smart binning and pairing

• Outlier detection

• “Virtual Operations” to re-bin parts

• And many more…

Quality Index

Quality Index

Lot/Wafer Yield etc.

Quality Rule

Inputs

Wafer Geography


Going Beyond Semiconductors

TestReworkGenealogy

IC & Multi Chip

…

1

N

3

2

Boards Systems In Use ReturnsRework

Test & Process data

Use Data

Performance data

Reliability Data

Strong trends are driving the need for:

Stretching the Performance EnvelopeSystem complexity, Performance margins

Superior Quality No-Fail, DPPB, Mission-critical

In-use ConfigurabilityFunctionality-on-demand,..

Brand Protection

Connected & Autonomous

Cars

Smart Wearables

Internet of Things

Security

Electronic Systems-in-

Package (3D IC’s)

Industry 4.0


“The New World”

Requires new collaboration paradigm across Product-Life-Cycle

Today OEMs and OCMs work in silos without sharing data• Main reasons: no convenient way to share data, concerns about

conflict of interests

• Minimal sharing that does occur is typically in reaction to significant quality issues

At the same time there is significant pressure on both OEM and OCM to:• Shorten time to market

• Shorten time to quality

• Improve quality

• Lower cost (e.g. improve yield, reduce test cost)

• Improve productivity (e.g. shorten issue resolution time)

Lacking end-to-end data sharing mechanisms

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What Problems need to be Solved?


Distributed Supply Chain


DFF/DFB between Industries

Electronics OEM

Chip Supplier 1

Chip Supplier 2

Chip Supplier 3

Is this possible???


Lower RMA Costs• Board-to-Chip correlations• Fast Root Cause analysis• On-line RMA Prevention Rules• Reduced NTF rates

Improved Quality and Time-to-Quality• Reduced time to reach board level DPPM goals• On-line Quality link between chips and boards• Escape Prevention and Outlier Detection Rules• Enhanced Functional Safety (ISO 26262)

More Efficient Test Processes – Adaptive Test• Test “suspect” parts more• Test “perfect” parts less

Better System Performance• Avoid in-Spec Chips with marginal performance at Board• Smart Pairing – Select the right chips to the right system board

What Data Sharing can Achieve


Advanced Applications

Genealogy Information (raw data) Reconstructed

wafer map of Board Yield

Board Bin Analysis by

ComponentsRelationships between

component tests(colored by board pass/fail)

The chart here shows correlation between 2 tests from different components, grouped by Board Performance

The left graphs are grouped by Board Performance Group, while the right graph shows per Board Performance Test value


Pairing between Devices



DFF/DFB between Industries

Other Supplier

The simple case – an OEM which also makes chips



DFF/DFB across the Supply Chain

Electronics OEM

Chip Supplier 1

Chip Supplier 1

Chip Supplier 1

But what about between an electronics OEM and its suppliers?


Cisco & Optimal+



Data Feed Backwards – Possible Today!

OEM

OCM 1

OCM 2

OCM 3

By enabling just board level data to chip suppliers quality levels can be dramatically enhanced

Boarddata

Breaking down test silos has tremendous benefits for• Quality, Efficiency, Yield

Data Feed Forward, Quality Index already a reality for major chip manufacturers within their supply chain

Data Feed Backwards from electronics to semi is coming soon…

How are YOU breaking down data silos?


Summary

Thank You!

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NIWeek 2016 - "Breaking Data Silos"

Engineering

Transcript of NIWeek 2016 - "Breaking Data Silos"