on diagnosis of multiple faults using compacted responses

On Diagnosis of Multiple Faults Using Compacted Responses

Jing Ye1,2, Yu Hu1, and Xiaowei Li1

1 Key Laboratory of Computer System and ArchitectureInstitute of Computing Technology

Chinese Academy of Sciences2 Graduate University of Chinese Academy of Sciences

2

Scan-BasedCircuit(CUT)

ATE

Compactor

Decompressor

Scan-BasedCircuit(CUT)

Compressed Stimulus

Compacted ResponseLow-Cost

ATE

MotivationWith the exponential growth in the number of transistors:• Multiple faults may exist in one circuit.• Both of test data volume and test application time increase.

SOURCE: L.-T. Wang, C.-W. Wu, and X. Wen, “VLSI Test Principles and Architectures: Design for Testability,” Morgan Kaufmann, San Fransico, 2006.

It is necessary to diagnose multiple faults using compacted responses.

ATE: Automatic Test Equipment CUT: Circuit Under Test

3

Outline Related Works Terminology• Explanation Capability• Explanation Necessity Issue of Compacted Responses Diagnosis Method

• Suspect Fault Marking• Suspect Fault Simulation• Suspect Fault Evaluation• Suspect Fault Ranking

Experimental Results

4

Related WorksFault diagnosis approaches using compacted responses can be classified into three categories: Bypassing diagnosis

Bypass space compactor[ J. Ye, Y. Hu, et al., DATE10 ][ F. Wang, Y. Hu, et al., ITC09 ]

Indirect diagnosisMathematically de-compact the compacted responses[ H.P.E. Vranken, S.K. Goel, et al., DAC06 ][ J. Rajski, J. Tyszer, et al., ITC03 ]

Direct diagnosis Use space compactorCompare actually observed responses with simulated compacted responses[ S. Holst, H.-J. Wunderlich, DATE09 ][ W.-T. Cheng, K.-H. Tsai, et al., ATS04 ]

Our method is a direct diagnosis method.

5

TerminologyExplanation CapabilityThe explanation capability of a suspect fault reflects the match degree between its simulated responses and the actually observed responses.

For one Observation Point (OP):Observed

signalSimulated signal of

a suspect fault Explanation Capability

Failing Passing

Failing Failing The suspect fault explain the failing OP.

Passing Passing

Passing Failing The suspect fault contaminate the passing OP.

( Failing: A signal is different from the fault-free signal;Passing: A signal is the same as the fault-free signal. )

6

CUT

Expect to observeActually observe 0 0 1 1 1 1 1

OP1 OP2 OP3 OP4 OP5 OP6 OP7

0 0 0 0 0 0 0

Passing OPs Failing OPs

F1

susp

ect f

aults

F2

F3

F4

F5

1 1 1 1 1 1 0Simulate1 0 1 1 1 0 0Simulate0 0 0 0 0 1 1Simulate0 0 1 1 1 0 0Simulate1 1 1 1 0 0 0Simulate

TerminologyExplanation Capability

( OP: Observation Point )

Following is an example of a CUT under one failing pattern.

Observed signal

Simulated signal of a suspect fault Explanation Capability



7



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5c econtaminate explain

Terminology

ec c

c

c c

e e e

e e e

e e

eee

e e


Explanation Capability Observed

signalSimulated signal of

a suspect fault Explanation Capability



Following is an example of a CUT under one failing pattern.

8



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5

e e e e

e e e

e e

e e e

e e

c c

c

c

σ ιExplanation Capability43232

21002

c econtaminate explainc

Terminology


Explanation Capability Two measures:• σ: The number of explained failing OPs of one pattern.• ι: The number of contaminated passing OPs of one pattern.• A suspect fault with a high ( σ – ι ) reflects its high

explanation capability.

( 0 ≤ σ ≤ Number of OPs )( 0 ≤ ι ≤ Number of OPs )

9

TerminologyExplanation NecessityThe explanation necessity of a suspect fault reflects its importance for matching the observed responses.



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5

e e e e

e e e

e e

e e e

e e

c c

c

c


21002


10

OP1 OP2 OP3 OP4 OP5 OP6 OP7Explanation Necessityω 11 3 1 4 1 4 1 3 1 21 2

Terminology

• Nce: The number of suspect faults which can contaminate/explain a passing/failing observation point.

• Each observation point is given a weight ω = .



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5

e e e e

e e e

e e

e e e

e e

c c

c

c


21002


Explanation NecessityThe explanation necessity of a suspect fault reflects its importance for matching the observed responses.

1Nce

11

1

11a

cb

011q

Cell11

Cell21ed

Issue of Compacted Responses• Using uncompacted responses, when multiple faults exist, the

suspect faults with the highest explanation capability often hit the actual faults [9]. The situation, that the actual faults do not show the highest explanation capability, rarely occurs.

• This situation becomes serious when using compacted responses.

[9] J. Ye, Y. Hu, and X. Li, “Diagnosis of Multiple Arbitrary Faults with Mask and Reinforcement Effect,” Proc. of Design, Automation, and Test in Europe (DATE), pp. 885-890, 2010.

Actual faults: a/0 (a-stuck-at-0), c/0

Failing observation points: Cell11, qSuspect faults: a/0(d/1), c/0(q/0)

12

1

11a

cb

011q

Cell11

Cell21 Cell11

Cell21Cell22

Cell23Cell15

Cell12

Cell13

Cell14Chain1Chain2

OP1

ed

[9] J. Ye, Y. Hu, and X. Li, “Diagnosis of Multiple Arbitrary Faults with Mask and Reinforcement Effect,” Proc. of Design, Automation, and Test in Europe (DATE), pp. 885-890, 2010.

Actual faults: a/0 (a-stuck-at-0), c/0

Failing observation points: OP1, qSuspect faults: b/0

Failing observation points: Cell11, qSuspect faults: a/0(d/1), c/0(q/0)

Explanation capability alone is not enough for selecting right suspect faults.

• Using uncompacted responses, when multiple faults exist, the suspect faults with the highest explanation capability often hit the actual faults [9]. The situation, that the actual faults do not show the highest explanation capability, rarely occurs.

• This situation becomes serious when using compacted responses.

Issue of Compacted Responses

13

ε

1 23 25 6-13

1 2



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5


e e e e

e e e

e e

e e e

e e

c c

c

c


21002

Explanation Necessityω 1

c econtaminate explain

1 3 1 4 1 4 1 3 1 2

c

1 2

• Each suspect fault is given a score ε under one given pattern:

Combine explanation capability and explanation necessity

Issue of Compacted Responses

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Diagnosis Method

Marking

Simulation

Evaluation

Ranking

15

Diagnosis Method

Marking

Simulation

Evaluation

Ranking

Suspect Fault Marking• Search failing observation points’ related logic cones. 1

11a

cb

011q

Cell11

Cell21

Cell11

Cell21

SpaceCompactor

Cell22

Cell23Cell15

Cell12

Cell13

Cell14Chain1Chain2

OP1

16

Diagnosis Method

Marking

Simulation

Evaluation

Ranking

Suspect Fault Simulation• Each single suspect fault is simulated under all the given patterns to record their compacted responses.• The structure of space compactor is known.

17

Diagnosis Method

Marking

Simulation

Evaluation

Ranking

Suspect Fault Evaluation• Each suspect fault is given a score ε under each given pattern:

• The Final Score (FS) of a suspect fault is the sum of its scores under all given patterns.

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Diagnosis Method

Marking

Simulation

Evaluation

Ranking

Suspect Fault Ranking



0 0 0 0 0 0 0


1 1 1 1 1 1 0SimulateF1

susp

ect f

aults

1 0 1 1 1 0 0SimulateF2

0 0 0 0 0 1 1SimulateF3

0 0 1 1 1 0 0SimulateF4

1 1 1 1 0 0 0SimulateF5


e e e e

e e e

e e

e e e

e e

c c

c

c


21002

Explanation Necessityω 1

c econtaminate explain

ε

1 23 25 6-13

1 3 1 4 1 4 1 3 1 2

c

1 2

1 2


For two faults A, B (FS: Final Score of a suspect fault)• FS(A)>FS(B) : Rank(A)>Rank(B)• FS(A)=FS(B) & σ(A)>σ(B) : Rank(A)>Rank(B)• FS(A)=FS(B) & σ(A)=σ(B) & ι(A)<ι(B) : Rank(A)>Rank(B)

( The suspect faults with the same ranks are considered indistinguishable )

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Experimental ResultsExperimental Setup• Benchmark circuits: ISCAS’89, ITC’99• Test patterns: Generated by a commercial ATPG tool

• Injected fault types: Stuck-at faults, Transition faults• Space compactor:

• For each circuit, each number of multiple faults, each type of faults, and each compaction ratio, 100 diagnosis cases are conducted.

[20] S. Holst, H.-J. Wunderlich, “A Diagnosis Algorithm for Extreme Space Compaction,” Proc. of Design, Automation, and Test in Europe (DATE), pp. 1355-1360, 2009.

Circuit s9234 s13207 s15850 s35932 s38417

Number of test patterns 142 275 132 24 104

Cell14 Cell13 Cell12 Cell11



Cell44 Cell43 Cel42 Cell41

Chain1

Chain2

Chain3

Chain4

Extreme response compactor [20]

Compaction ratio=2

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Experimental ResultsEvaluation Metrics• Success rate In this work, only the accuracy of the most likely suspect faults is concerned. A diagnosis case is considered successful if the top-ranked suspect faults hit at least one actual fault.

• Experimental focus Success rate vs Compaction ratio Success rate vs The number of injected faults• Comparison with [20] S. Holst, H.-J. Wunderlich, “A Diagnosis Algorithm for Extreme Space Compaction,” Proc. of Design, Automation, and Test in Europe (DATE), pp. 1355-1360, 2009.

Success rate =Number of successful diagnosis cases

Number of total diagnosis cases100%

21

Experimental ResultsSuccess rate vs Compaction ratio• S (%): Success rate of our method

• ΔS (%): Success rate of our method - Success rate of the method proposed in [20]• T (seconds): Average run time of diagnosis in our experiments


22



Success rate vs Compaction ratio• S (%): Success rate of our method

• ΔS (%): Success rate of our method - Success rate of the method proposed in [20]• T (seconds): Average run time of diagnosis in our experiments

23

Success rate vs Compaction ratio

• Compaction ratio = 2

• Compaction ratio = 32

75%

80%

85%

90%

95%

100%

75%

80%

85%

90%

95%

100%

compaction ratio = 32compaction ratio = 16compaction ratio = 8compaction ratio = 4

our method method proposed in [20]success rate

compaction ratio = 2



94.98% 99.94%

86.47% 98.64%

s9234, s13207, s15850, s35932, s38417, s38584, b17, b20, b22

24



Success rate vs The number of injected faults• Compaction ratio = 32• S (%): Success rate of our method• ΔS (%): Success rate of our method - Success rate of the method proposed in [20]• T (seconds): Average run time of diagnosis in our experiments

25

Success rate vs The number of injected faults

• Number of injected faults = 2

• Number of injected faults = 32

30%

40%

50%

60%

70%

80%

90%

100%

30%

40%

50%

60%

70%

80%

90%

100%

25191075

success rate our method method proposed in [20]

number of injected multiple faults2 3 4 14 32



( Compaction ratio = 32 )

91.4% 99.2%

49.8% 93.3%s9234, s13207, s15850, s35932, s38417, s38584, b17, b20, b22

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• Propose the explanation necessity.• Combine the explanation capability and the

explanation necessity:

• 98.8% of top-ranked suspect faults hit actual faults.

Conclusion

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• Thank You For Your Attention!

• Questions?

on diagnosis of multiple faults using compacted responses

Documents

faultfree signalpassing

observation point op

simulated compacted

signalsimulated signal

failing pattern

simulated responses

diagnosis of multiple

direct diagnosis method