Breaking Data Silos
Michael Schuldenfrei, CTO
NI Week 2016 - Test Leadership Forum
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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+ 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
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Yield Reclamation
Site-to-site variances, equipment issues, etc.
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Efficiency
In this example the tester is retesting 97% of bad dice (blind retest) with only 1 die gain
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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
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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
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Data Silos in Semiconductor Test
E-Test WS FT 1Burn-
inFT 2
Manufacturing Flow
Devices typically go through multiple test steps…
SLT
Subcon 2SubconFoundry
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Data Silos in Semiconductor Test
E-Test WS FT 1Burn-
inFT 2
Manufacturing Flow
At multiple locations…
SLT
Subcon 2Subcon 1Foundry
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Data Silos in Semiconductor Test
E-Test WS FT 1Burn-
inFT 2
Manufacturing Flow
No data is typically shared between the testing locations…
SLT
Subcon 2Subcon 1Foundry
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Data Silos in Semiconductor Test
E-Test WS FT 1Burn-
inFT 2
Manufacturing Flow
Or even within a location
SLT
Subcon 2Subcon 1Foundry
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Data Silos in Semiconductor Test
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
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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
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DB WAT WS1 WS2 Assy. FT1 Burn-in FT2 SLT
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Detect Drift between Two Operations
Tester
1. ECID Data
2. FT1 Measurements
Test Program running
FT2 operationReal-time data!
No test time impact!
Database
DB WAT WS1 WS2 Assy. FT1 Burn-in FT2 SLT
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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
DB WAT WS1 WS2 Assy. FT1 Burn-in FT2 SLT
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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
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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
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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
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“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?
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Distributed Supply Chain
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DFF/DFB between Industries
Electronics OEM
Chip Supplier 1
Chip Supplier 2
Chip Supplier 3
Is this possible???
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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
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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
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Pairing between Devices
Distributed Supply Chain
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DFF/DFB between Industries
Other Supplier
The simple case – an OEM which also makes chips
Distributed Supply Chain
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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?
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Cisco & Optimal+
Distributed Supply Chain
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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?
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Summary
Thank You!
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