High-Level Fault Grading

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High-Level Fault Grading
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Improving Gate-Level Fault Coverage by RTL Fault
Grading

• W. Mao and R. K. Gulati, ITC 1996, pp. 150-159.

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Motivation

• Gate-level fault simulation is computationally
  infeasible for large circuits
• RTL design available early in the design cycle
  for fault grading of available (validation or
  legacy) test vectors
• It would be nice if fault analysis could be done
  at RTL level but the results closely approximated
  gate-level coverage.

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Basic Idea

• Inject stuck-type faults on all PIs, internal
  signals, and their fanouts using RTL constructs.
• Use an RTL fault simulator (e.g. Verifault for
  Verilog) for fault grading at the RTL level
• Verify the closeness of approximation against
  gate-level fault coverage.

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Example RTL Model and Signal- Flow Diagram
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RTL Modified for Fault Injection
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Testability Analysis Flow
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Handling Signal Fanouts(Optimistic Mode)
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Handling Signal Fanouts(Pessimistic Mode)
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RTL vs. Gate-Level Fault Coverage
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Critique

• Technique works well only when the circuit is
  simulated at low-level RTL
• Errors arise because of
• Differences in fanouts at the two levels
• Most signal fanout lot more at the RTL level
  (exception Reset signal)
• Complex blocks with only I/O visibility
• The last shortcoming is addressed in the
  stratified sampling approach of Thaker, Agrawal,
  and Zaghloul that we consider next.

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Stratified Sampling for Fault Coverage of VLSI
Systems

• Vishwani D. Agrawal
• Auburn University
• Collaborators Pradip Thaker and Mona Zaghloul

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VLSI System Design
Register-transfer level (RTL) design and
verification
90-100 stuck-at fault coverage required
Logic synthesis
Test generation
Timing and physical design
Design and test data for manufacturing
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Problem

• Accurately estimate the gate-level fault coverage
  for a VLSI system at the RT-level
• Advantages
• Improve test
• Improve design
• Avoid expensive design changes
• Previous approaches do not accurately represent
  gate-level fault coverage (function errors,
  mutation, statement faults, branch faults, etc.)

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Solution

• Model faults as representative sample of the
  targeted (gate-level stuck-at) faults.
• Treat the coverage in an RTL module as a
  statistical sampling estimate.
• For a multi-module VLSI system, combine module
  coverages according to the stratified sampling
  technique.

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Outline of Talk

• Introduction to fault sampling.
• RTL fault model and application to modules.
• Coverage in a multi-module system
• Need for stratified sampling
• Stratum weights
• Experimental results
• Conclusion
• References

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Fault Sampling

• A randomly selected subset (sample) of faults is
  simulated.
• Measured coverage in the sample is used to
  estimate fault coverage in the entire circuit.
• Advantage Saving in computing resources (CPU
  time and memory.)
• Disadvantage Limited data on undetected faults.

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Random Sampling Model
Detected fault
Undetected fault
All faults with a fixed but unknown coverage
Random picking
Np total number of faults (population
size) C fault coverage (unknown)
Ns sample size Ns ltlt Np
c sample coverage (a random variable)
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Probability Density of Sample Coverage, c

(x--C )2

-- ------------
1 2s 2 p (x )
Prob(x lt c lt x dx ) -------------- e
s (2
p) 1/2
C (1 - C) Variance, s 2
------------ Ns
Sampling error
s
s
p (x )
Mean C
x
1.0
C 3s
C -3s
x
C
Sample coverage
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Sampling Error Bounds
C (1 - C ) x - C 3
-------------- 1/2 Ns
Millot, 1923
Solving the quadratic equation for C, we get the
3-sigma (99.8 confidence) estimate
(Agrawal-Kato, 1990)
4.5 C 3s x ------- 1
0.44 Ns x (1 - x )1/2 Ns

Where Ns is sample size and x is the measured
fault coverage in the sample. Example A circuit
with 39,096 faults has an actual fault coverage
of 87.1. The measured coverage in a random
sample of 1,000 faults is 88.7. The
above formula gives an estimate of 88.7 3.
CPU time for sample simulation was about 10 of
that for all faults.

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An RTL Fault Model(ITC-2000)

• Language operators are assumed to be fault-free
• Variables (map onto signal lines) contain faults
• stuck-at-0
• stuck-at-1
• Only one fault is applied at a time (single fault
  assumption)

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RTL Fault Injection

• Not affected by faults
• Synthetic operators - gt lt !
• Boolean operators
• Logical operators !
• Sequential elements (flip-flops latches)
• Faults introduced in signal variables (stems and
  fan-outs)
• Separate faults for bits of data words

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Fault Modeling for Boolean Operators
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Stem and Fan-out Fault Modeling

• RTL fan-out faults if(X) then ZY else Z!Y
• Unique RTL fault is placed on each fan-out of
  each bit of a variable
• Unique RTL fault on each stem

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More RTL Faults
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Observations and Assumption RTL Faults

• RTL faults may have detection probability
  distribution similar to that of collapsed
  gate-level faults
• Statistically, an RTL fault-list approximates a
  random sample from the gate-level fault-list
• Number of RTL faults vs. gate-level faults
  depends on
• Level of RTL description
• Synthesis procedure used to convert RTL to gate
  level

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RTL Fault Simulation

• Analogous to gate-level approach
• Faults injected in RTL code of the design
  description by a C parser a logic buffer
  element inserted at fault site (technique
  identical to Mao Gulatis).
• Fault report contains statistics on detected and
  undetected RTL faults
• Cadences Verifault-XL used as RTL fault simulator

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Estimation Error for Module Fault Coverage

• RTL fault coverage assumed to be an estimate of
  the collapsed gate-fault coverage within
  statistical bound Agrawal and Kato, DT, 1990

a 3.00 for confidence probability of 99.8 c
ratio of detected to total number of RTL faults M
number of gate faults N number of RTL faults,
k 1 - N/M
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DSP Interface Module(3,168 Gates)
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RTL Faults and VLSI System Coverage

• Experimental results demonstrate RTL fault
  coverage of a module to be a good statistical
  estimate of the gate-level fault coverage
• A VLSI system consists of many interconnected
  modules
• Overall RTL fault-list of a VLSI system does not
  constitute a representative sample of the
  gate-level fault-list

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Error at System Level
Gate- level
M1 150 faults 90 cov.
RTL
M1 100 faults 91 cov.
M2 400 faults 40 cov.
M2 100 faults 39 cov.

• RTL Coverage (0.91 x 100 0.39 x 100) / 200
  65
• Gate Coverage (0.90 x 150 0.40 x 400) / 550
  54
• A correct estimation of gate-level fault coverage
  from RTL coverage

91 x (150 / 550) 39 x (400 / 550) 53
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Application of Stratified Sampling

• Fault population of a VLSI system divided into
  strata according to RTL module boundaries
• RTL faults in each module are considered a sample
  of corresponding gate-level faults
• The stratified RTL coverage is an estimate of the
  gate-level coverage

Wm stratum weight of mth module Gm/G cm RTL
fault coverage of mth module Gm number of
gate-level faults in mth module G number of all
gate-level faults in the system M number of RTL
modules in the system
M C S Wmcm m1
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Application of Stratified Sampling
C t s

• Range of coverage,

s2 -------- cm(1 -
cm)
M
Wm
S
where,
rm - 1
m1
rm number of RTL faults in mth module t
value from tables of normal distribution
The technique requires knowledge of stratum
weights and not absolute values of Gm and G
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Stratum Weight Extraction Techniques

• Logic synthesis based weight extraction
• Wm Gm/G
• Floor-planning based weight extraction
• Wm Am/A
• Entropy-measure based weight extraction

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Experimental Procedure

• Technology-dependent weight extraction
• Several unique gate-level netlists obtained by
  logic synthesis from the same RTL code
• Each synthesis run performed using a different
  set of constraints, e.g., area optimization
  (netlist 1), speed optimization (netlist 2), or
  combined area and speed optimizations (netlists 3
  and 4)
• Strata weights calculated using gate-level fault
  lists of various synthesized netlists
• Technology-independent weight extraction
• Stratum weights calculated using area
  distribution among modules
• Each set of stratum weights used to calculate RTL
  fault coverage and error bounds
• Impact of estimation error investigated

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Experimental Data Weight Distributions
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Experimental Data RTL Fault Coverage
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Experimental Data Error Bounds
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Timing Controller ASIC (17,126 Gates)
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A DSP ASIC(104,881 Gates)
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Conclusion

• Main ideas of RTL fault modeling
• A small or high-level RTL module contributes few
  RTL faults, but large statistical tolerance gives
  a correct coverage estimate
• Stratified sampling accounts for varying module
  sizes and for different RTL details that may be
  used
• Stratum weights appear to be insensitive to
  specific details of synthesis
• Advantages of the proposed RTL fault model
• High-level test generation and evaluation
• Early identification of hard-to-test RTL
  architectures
• Potential for significantly reducing run-time
  penalty of the gate-level fault simulation

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References - 1

• V. D. Agrawal, Sampling Techniques for
  Determining Fault Coverage in LSI Circuits, J.
  Digital Systems, vol. V, no. 3, pp. 189-202,
  1981.
• V. D. Agrawal and H. Kato, Fault Sampling
  Revisited, IEEE Design Test of Computers, vol.
  7, no. 4, pp. 32-35, Aug. 1990.
• P. A. Thaker, M. E. Zaghloul, and M. B. Amin,
  Study of Correlation of Testability Aspects of
  RTL Description and Resulting Structural
  Implementation, Proc. 12th Int. Conf. VLSI
  Design, Jan. 1999, pp. 256-259.
• P. A. Thaker, V. D. Agrawal, and M. E. Zaghloul,
  Validation Vector Grade (VVG) A New Coverage
  Metric for Validation and Test, Proc. 17th IEEE
  VLSI Test Symp., Apr. 1999, pp. 182-188.

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References - 2

• P. A. Thaker, Register-Transfer Level Fault
  Modeling and Evaluation Techniques, PhD Thesis,
  George Washington University, Washington, D.C.,
  May 2000.
• P. A. Thaker, V. D. Agrawal, and M. E. Zaghloul,
  Register-Transfer Level Fault Modeling and Test
  Evaluation Techniques for VLSI Circuits, Proc.
  Int. Test Conf., Oct. 2000, pp. 940-949.
• This presentation is available from the website
  http//cm.bell-labs.com/cm/cs/who/va

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Other Related Papers

• OCCOM Fallah et al., IEEE TCAD, Aug. 2001, pp.
  1003-1015.
• IFMB Santos et al., ITC2001, pp. 377-385.
• Probabilistic Testability Fernandes et al.,
  DATE04, pp. 10176-10181.
• Kang et al., VTS07.