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Contents
• What is Adaptive Test?
• Importance of Adaptive Test
• Applications of Adaptive Test : Examples
• Requirements
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Adaptive Test :
A broad term used to describe various statistical methods for
improving semiconductor test by adjusting the test plan based
upon feedback from the test environment.
What is Adaptive Test?
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Traditional Adaptive Test :
•Performed manually
•Study historical production results
•Adjust test limits or test flows with
permanent changes to test
program to improve yield, reduce
time, etc.
•Monitor subsequent production
for new opportunities.
Today’s Adaptive Test :
•Means the ability to perform these
actions on a continuous and
automatic basis
•Real-time adaptive test means
the ability to perform these
adjustments on-line during
production testing.
•Statistical analysis used to adjust
limits or flows.
What is Adaptive Test?
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What is Adaptive Test?
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Feed-forward Data Data from previous test steps, inline test,
historical test results, Off-line analysis
results, Production/Supply Chain data
Real-time analysis Yield, fallout/bins,
parametric data,
Production results
Post-Test Analysis results (outliers), dispositioning,
product flows, target applications, future
test optimization
TEST
What is Adaptive Test?
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Contents
• What is Adaptive Test?
• Importance of Adaptive Test
• Applications of Adaptive Test : Examples
• Requirements
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• New methodologies for optimizing – Yield – Cost of Test – Quality and Reliability – New product introduction time
• Consumer markets driving need for lowest test cost
• Automotive and medical electronics are driving requirements for
zero defects
Importance of Adaptive Test
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Improve Production Yield
Speed Time to Entitled Yield
1-4%
Increase Production Yield
Optimize Overall Equipment Efficiency (OEE)
Speed Time to Actionable Data
10-20%
Optimize Overall Equipment Efficiency
Increase Product Reliability
Through Outlier Detection
20-50%
Increase Product Reliability
Increase Product & Testing Quality
Reduce Customer Returns
50-75%
Improve Product & Testing Quality Advanced Adaptive Test for TTR and/or
Improve Capital Utilization
10-30%
Reduce Test Times
MEASURABLE RESULTS
*OptimalTest사 자료
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Contents
• What is Adaptive Test?
• Importance of Adaptive Test
• Applications of Adaptive Test : Examples
• Requirements
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Old practice:
– Assume that a passing part is a good part
Adaptive Test Solution:
– Long standing quality practices observe that if a measure that is supposed to be a variable does not show any change, or shows monotonically increasing or decreasing changes over a short set of data points, then something is wrong with the testing
– An instrument stuck at a value within the pass-fail limits can PASS a faulty device
– On-line data monitoring can catch this problem
Example: Quality Assurance
Reference WECO Rules: Western Electric Company Quality
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To achieve maximum test coverage more tests need to be added to a test program.
Yet, as yield approaches 100%, more and more individual tests will approach a zero failure rate
– If yield were perfect, testing would not be necessary
Statistical sampling is an automated method to determine when to add or to skip specific tests and thus reduce test time without impacting quality.
Parametric tests can be measured for being under “statistical control” and probabilities for failure can be computed on the fly during lot processing.
Example: Parametric Test Time Reduction
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Example: Reducing Parametric Test Time
Run TestN and collect new value
Add value to current distribution
Analyze the statistical goodness
of TestN
Run standard test
Run extended test(s)
Skip this test
LL UL
LL UL
LL UL
Real-time decision for next TestN
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Parts Average Testing (PAT):
– A part that tests within its spec limits but behaves differently than the
majority of parts in its batch has a significantly higher probability of field
failure
Outliers detection and rejection can be used to
– Reduce field failures
– Reduce burn-in costs
Challenge
– How can outliers be detected on the fly
– How can outliers be automatically rejected
– Without generating unnecessary yield loss
Example: Outlier Detection & Rejection
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Wafer 1 Wafer 2
Upper Limit Lower Limit
LL LL UL UL DPAT Limits
Outlier Outlier
Example: Dynamic PAT is an Outlier Algorithm
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Example: Dynamic PAT is an Outlier Algorithm
Upper Static PAT Limit
Upper Design Limit
Lot 1 Lot 2
Dynamic
PAT Limits X
X
X
X
X
X
PAT
Outliers
Lot 3 Lot 4
Lower Design Limit
Lower Static PAT Limit
For each critical
parametric test
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Example: Static PAT
AEC - Q001
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Example: Dynamic PAT
AEC - Q001
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Historically, product engineers may manually tighten design
specs over time as yields improve
Continuous improvement covers a wide range of methods for
automatically doing this
A requirement of a continuous improvement system is a
database capable of storing and comparing trends over a long
period of time or units
Example: Continuous Feedback/Improvement
Test
Program Continuous
learning DBMS
Adaptive Test
Interface
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The ability to predict the behavior of testing based upon certain measures in earlier process steps and adaptively increase of decrease testing to reduce test time or improve quality
Example: Feed-forward Applications
Test Plan
Probe
Feed-forward
DBMS
Fab processing Wafer Probe Packaged Test
Test Plan
Final
Feed-forward
DBMS
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• Sensor device needs up to 8 insertions to test
– TEMP – HOT, COLD, ROOM
– Pressure
– Vibration
– Etc.
• Allow PE to analyze relationship of test results at STAGE n+1 to
results measured at STAGE-n and skip testing at n+1 when
analysis shows it is unnecessary
– Example: Test-n never fails at COLD if it passes HOT
• Define rules for each stage to allow skipping test(s)
• This is a good feed-forward application (see next page)
Example: Reduce Cost of Multi-insertion Test
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Example: Feed-forward Analysis (Univariate)
Test-n @ HOT
e.g. Condition-2
Test-n @ COLD
e.g. Condition-1
USLHOT
LSLHOT
USLCOLD LSLCOLD
Failures at Condition-1
(COLD) are not tested at
subsequent stages
Fails at Condition-2 (HOT)
that passed Condtion-1
Observations:
High correlation is expected
Analyze over large part count
Can total test time be reduced?
Test-n passes at both
conditions
Challenge: Can functional test times be reduced for multi-
insertion testing using dynamic parametric correlation?
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Example: Feed-forward Analysis (Univariate)
Test-n @ HOT
e.g. Condition-2
Test-n @ COLD
e.g. Condition-1
USLHOT
LSLHOT
USLCOLD
Reduce Test Time:
Using off-line analysis
Identify revised limits on
conditon-1 that predict
When condtion-2 must be
tested or may be skipped
Revised Limits
Test-n results in this
range at C-1 must be
tested at C-2
Test-n results in this
range at C-1 may
skip testing at C-2
LSLCOLD
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Example: Dynamic Multivariate Regression
• Use multiple parametric results to predict yield of specific test(s) or total device yield
• Combines historical correlations with on-line measurements
• May also be used for dynamic prediction of retest effectiveness
• Improves quality & test time concurrently
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• Correlation between functional tests and specific parametric measures produces a precise guide for the probability of failure in a time consuming pattern
• Determine correlation between pass/fail for functional test TF vs. multiple parametric measures P1 & P2
– E.g. Freq & Vdd
• Define rule that describes ranges of 99.99% PASS and/or 99.99% FAIL
• Result:
– Skip testing TF only when P1 & P2
results are in GREEN zone
– Always test TF when P1 & P2 in RED zone
• Repeat for as many functional tests as possible
Example: Reducing Functional Test Time
Failed Passed
VDD
Freq (period) ns
F(P1,P2)
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Old practice:
Release two separate test
programs
– One for calibration – “Long
version”
– One for fast production – “Short
version”
– Ask operator run calibration
program at a certain time, e.g.
every 100 parts, or half way
through the lot, etc.
Adaptive Test Solution:
– Release single program version
with streamlined and extended
calibration routines
– Use adaptive test to monitor
specific conditions and
automatically run the extended
portions when specified
conditions are met (change the
flow)
Example: Automatic Calibration
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Example: Dynamic Yield Recovery
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• Good die in a bad neighborhood
• Near Neighbor Residual Analysis (NNR)
• Statistical Bin Limits/Yield Limits (SBL/SYL)
Other DPM Reduction Techniques
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Advanced Methods for Improving Quality
Efficiency Very little good
material is screen
out with the good
material
Good material is
also screened out
with the poorer
material
Eff
ec
tive
nes
s
Very good detecting
poorer material
Weak at
detecting
poorer
material
Courtesy of Clare Hakeman, Freescale Technology Forum, July 2006
Static PAT BMY
SBL
GDBN
Dynamic PAT
Principal Component
Analysis
Regression
NN Residual
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Contents
• What is Adaptive Test?
• Importance of Adaptive Test
• Applications of Adaptive Test : Examples
• Requirements
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Infrastructure Required for Adaptive Test
Real-time Engine
1. Adaptive Engine Real-time data capture
Real-time decision making
No impact to ATE resources
Available on all ATE brands
On-Line DBMS
2. Learning Engine Off-line Data Analysis
Secure interface to ATE
Continuous feedback
User algorithms
ATE
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• Algorithms must be effective at their mission of saving test time,
or improving quality, etc.
• Meets industry quality and audit trail requirements
• System cannot interfere with ATE performance
• System must be easy to deploy
• Useful at both probe and final test
• Supported on wide base of ATE and test subcontractors
Critical Success Factors for Adaptive Test
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Thank you!
