Submicron Verification Challenges Uri Gruenbaum. Presentation Flow Chart Intro @speed Problems Case...
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Transcript of Submicron Verification Challenges Uri Gruenbaum. Presentation Flow Chart Intro @speed Problems Case...
SubmicronSubmicronVerificationVerificationChallengesChallenges
Uri Gruenbaum
Presentation Flow ChartPresentation Flow Chart
Intro
@speed
Problems
Case study
Intro
Presentation ProgressPresentation Progress
Intro
@speed
Problems
Case study
IntroIntro
What’s going on? What’s going on?
IC features continue to shrink.IC features continue to shrink. Fabrication processes under 130 nmFabrication processes under 130 nm Different size generate different Different size generate different
faults. faults. The faults Distribution is different The faults Distribution is different Good old Stuck-at pattern isn't Good Good old Stuck-at pattern isn't Good
enough.enough.
IntroIntro
@Speed
CompetitionCase study
IntroIntro
In 180 nm we had:In 180 nm we had: Stack at patternStack at pattern Standard memory BIST Standard memory BIST (Built in self test)(Built in self test) IIddqddq
You combine all of them and you get You combine all of them and you get a coverage of ~100%a coverage of ~100%
IntroIntro
@speedproblems
Case study
IntroIntro
In 130 nm and less we have:In 130 nm and less we have: Higher frequencies. Higher frequencies. Different physical Different physical
properties. properties. Much more timing defectsMuch more timing defects
IntroIntro
@speedproblems
Case study
IntroIntroIntroIntro
@speed
problems
Did you know?Did you know? Research from LSI Logic and Intel Research from LSI Logic and Intel
shows that the population of timing shows that the population of timing defects for nanometer designs is defects for nanometer designs is ~2%~2%
Case study
IntroIntroIntroIntro
@speed
problems
Example:Example: 130 nm fabrication process 130 nm fabrication process Yield average of 70%Yield average of 70% Static fault testing coverage of 100%Static fault testing coverage of 100% 2% left unchecked…2% left unchecked… 50% of them on average are ok50% of them on average are ok We will have a defect rate of 0.7%We will have a defect rate of 0.7% DPM of 7000DPM of 7000
Unacceptable
Case study
Presentation ProgressPresentation Progress
Intro
@speed
Problems
Case study
@speed @speed
Background Background Been available for many yearsBeen available for many years A timing defect test patternA timing defect test pattern Used so far to test very high Used so far to test very high
speed devices & very accurate speed devices & very accurate goals goals
Intro
@speed@speed
problems
Case study
@speed-before scan @speed-before scan Intro
@speed@speed
CompetitionOur SolutionWhat next?
clock
@speed- scan @speed- scan insertion insertion
Intro
@speed@speed
CompetitionOur SolutionWhat next?
clock
SE
SI
SO
@speed-shift phase @speed-shift phase Intro
@speed@speed
problems
clock
SE = 1
SI
SO
clock
SI
1 10
1 10 101
Case study
@speed-shift phase @speed-shift phase Intro
@speed@speed
problems
0 11
clock
SE = 1
SI
SO
clock
SI
1 10
Case study
@speed-capture @speed-capture Intro
@speed@speed
problems
0 11
clock
SE = 0
SI
SO
clock
SI
1 10
A B C
A B C
Case study
@speed-capture @speed-capture Intro
@speed@speed
problems
B CA
clock
SE = 0
SI
SO
clock
SI
1 10
A B C
Case study
@speed-shift phase @speed-shift phase Intro
@speed@speed
Problems
B CA
clock
SE = 1
SI
SO
clock
SI
1 10 0
0 A B C
Case study
@speed Vs Stuck at@speed Vs Stuck atIntro
@speed@speed
problems
Case study
@speed-Accurate @speed-Accurate clocksclocks
Intro
@speed@speed
problems
Case study
There can be variations between the testers clock and the PLL clocking
@speed - solution@speed - solution One solution is having the ATPG One solution is having the ATPG
(automatic test pattern generation) (automatic test pattern generation) to decide which clock is necessary to decide which clock is necessary
Intro
@speed@speed
problems
Case study
Presentation ProgressPresentation Progress
Intro
Market
Problems
Case study
ProblemsProblems
Test patterns for transition Test patterns for transition faults are not as efficient as for faults are not as efficient as for stuck at faultsstuck at faults
Transition faults test = 5 times Transition faults test = 5 times in size as a static fault testin size as a static fault test
When combined ,expensive When combined ,expensive tester reloads is performedtester reloads is performed
Intro
Market
ProblemsProblems
Case study
ProblemsProblems
An interesting fact:An interesting fact: Intro
Market
ProblemsProblems
Case studyTransition test pattern detect a significant percentage of stack at faults
Starting to get the picture?
ProblemsProblems
If you need to add TFP reduce If you need to add TFP reduce the number of SAP. the number of SAP.
Do it by creating the TAP first Do it by creating the TAP first and the SAP next.and the SAP next.
Intro
Market
ProblemsProblems
Case study
Presentation ProgressPresentation Progress
The Need
@speed
Problems
Case study
Case studyCase study
The goal:The goal:
Getting the best possible test Getting the best possible test coverage without doing coverage without doing expensive tester memory expensive tester memory reloads.reloads.
The NeedMarke
tproblems
Case studyCase study
Case studyCase study
Characteristic of test design:Characteristic of test design: The NeedMarke
tproblems
Case studyCase study
Case studyCase study
For the design, the test requirements are :For the design, the test requirements are :The NeedMarke
tproblems
Case studyCase study
Tester can hold up to 10,000 test patterns
The highest priority is to get max coverage for SAF
The test coverage for the TF must be as high as possible as long as it still fits the memory.
Case studyCase study
ResultResult::The NeedMarke
tproblems
Case studyCase study
Case studyCase study
Truncating the TDF results in a Truncating the TDF results in a significant lost in transition significant lost in transition coveragecoverage
TDF coverage 85.14% TDF coverage 85.14% 63.93% 63.93% Clearly not idealClearly not ideal
The NeedMarke
tproblems
Case studyCase study
Case studyCase study
How can we improve it?How can we improve it? The NeedMarke
tproblems
Case studyCase study Recognize that for each TDF its equivalent SAF is also detected
remove the least effective patterns
Case studyCase study
ResultResult::The NeedMarke
tproblems
Case studyCase study
Case studyCase studyThe NeedMarke
tproblems
Case studyCase study