adaptive test technology · •statistical bin limits/yield limits (sbl/syl) other dpm reduction...

Teradyne Confidential Teradyne Confidential

Adaptive Test Technology

[email protected]


Contents

• What is Adaptive Test?

• Importance of Adaptive Test

• Applications of Adaptive Test : Examples

• Requirements

Teradyne Confidential

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?


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.



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



Contents




• Requirements


• 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


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


Contents




• Requirements


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


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


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


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


Wafer 1 Wafer 2

Upper Limit Lower Limit

LL LL UL UL DPAT Limits

Outlier Outlier

Example: Dynamic PAT is an Outlier Algorithm


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


Example: Static PAT

AEC - Q001


Example: Dynamic PAT

AEC - Q001


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


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


• 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


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?


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


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


• 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)


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


Example: Dynamic Yield Recovery


• Good die in a bad neighborhood

• Near Neighbor Residual Analysis (NNR)

• Statistical Bin Limits/Yield Limits (SBL/SYL)

Other DPM Reduction Techniques


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


Contents




• Requirements


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


• 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


Thank you!

adaptive test technology · •statistical bin limits/yield limits (sbl/syl) other dpm reduction...

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