Testing 1. 2 Problems of Ideal Tests n Ideal tests detect all defects produced in the manufacturing...

TestingTesting

1

2

Problems of Ideal TestsProblems of Ideal Tests

Ideal tests detect all defects produced in the manufacturing process.

Ideal tests pass all functionally good devices. Very large numbers and varieties of possible

defects need to be tested. Difficult to generate tests for some real

defects. Defect-oriented testing is an open problem.

3

Real TestsReal Tests

Based on analyzable fault models, which may not map on real defects.

Incomplete coverage of modeled faults due to high complexity.

Some good chips are rejected. The fraction (or percentage) of such chips is called the yield loss.

Some bad chips pass tests. The fraction (or percentage) of bad chips among all passing chips is called the defect level.

4

Testing as Filter ProcessTesting as Filter Process

Fabricatedchips

Good chips

Defective chips

Prob(good) = y

Prob(bad) = 1- y

Prob(pass test) = high

Prob(fail test) = high

Prob(fail test) = lowPro

b(pass

te

st) =

low

Mostlygoodchips

Mostlybad

chips

5

Costs of TestingCosts of Testing Design for testability (DFT)

Chip area overhead and yield reduction Performance overhead

Software processes of test Test generation and fault simulation Test programming and debugging

Manufacturing test Automatic test equipment (ATE) capital cost Test center operational cost

6

Design for Testability (DFT)

Design for Testability (DFT)

DFT refers to hardware design styles or addedhardware that reduces test generation complexity.

Motivation: Test generation complexity increasesexponentially with the size of the circuit.

Logicblock A

Logicblock BPI PO

Testinput

Testoutput

Int.bus

Example: Test hardware applies tests to blocks Aand B and to internal bus; avoids test generationfor combined A and B blocks.

7

Cost of Manufacturing Testing in 2000AD

Cost of Manufacturing Testing in 2000AD

0.5-1.0GHz, analog instruments,1,024 digital pins: ATE purchase price = $1.2M + 1,024 x $3,000 = $4.272M

Running cost (five-year linear depreciation) = Depreciation + Maintenance + Operation

= $0.854M + $0.085M + $0.5M = $1.439M/year

Test cost (24 hour ATE operation) = $1.439M/(365 x 24 x 3,600) = 4.5 cents/second

8

Testing PrincipleTesting Principle

9

Automatic Test Equipment Components

Automatic Test Equipment Components Consists of:

Powerful computer Powerful 32-bit Digital Signal

Processor (DSP) for analog testing Test Program (written in high-level

language) running on the computer Probe Head (actually touches the bare

or packaged chip to perform fault detection experiments)

Probe Card or Membrane Probe (contains electronics to measure signals on chip pin or pad)

10

ADVANTEST Model T6682 ATE

ADVANTEST Model T6682 ATE

11

T6682 ATE Block Diagram

T6682 ATE Block Diagram

12

LTX FUSION HF ATELTX FUSION HF ATE

13

Verification TestingVerification Testing

Ferociously expensive May comprise:

Scanning Electron Microscope tests Bright-Lite detection of defects Electron beam testing Artificial intelligence (expert system)

methods Repeated functional tests

14

Characterization TestCharacterization Test

Worst-case test Choose test that passes/fails chips Select statistically significant sample of

chips Repeat test for every combination of 2+

environmental variables Plot results in Shmoo plot Diagnose and correct design errors

Continue throughout production life of chips to improve design and process to increase yield

15

Manufacturing TestManufacturing Test

Determines whether manufactured chip meets specs

Must cover high % of modeled faults Must minimize test time (to control cost) No fault diagnosis Tests every device on chip Test at speed of application or speed

guaranteed by supplier

16

Burn-in or Stress TestBurn-in or Stress Test

Process: Subject chips to high temperature &

over-voltage supply, while running production tests

Catches: Infant mortality cases – these are

damaged chips that will fail in the first 2 days of operation – causes bad devices to actually fail before chips are shipped to customers

Freak failures – devices having same failure mechanisms as reliable devices

17

Sub-types of TestsSub-types of Tests

Parametric – measures electrical properties of pin electronics – delay, voltages, currents, etc. – fast and cheap

Functional – used to cover very high % of modeled faults – test every transistor and wire in digital circuits – long and expensive – main topic of tutorial

18

Fault ModelingFault Modeling

Why model faults? Some real defects in VLSI and PCB Common fault models Stuck-at faults

Single stuck-at faults Fault equivalence Fault dominance and checkpoint theorem Classes of stuck-at faults and multiple faults

Transistor faults Summary

19

Some Real Defects in ChipsSome Real Defects in Chips Processing defects

Missing contact windows Parasitic transistors Oxide breakdown . . .

Material defects Bulk defects (cracks, crystal imperfections) Surface impurities (ion migration) . . .

Time-dependent failures Dielectric breakdown Electromigration . . .

Packaging failures Contact degradation Seal leaks . . .

Ref.: M. J. Howes and D. V. Morgan, Reliability and Degradation - Semiconductor Devices and Circuits, Wiley, 1981.

20

Observed PCB DefectsObserved PCB DefectsDefect classes

ShortsOpensMissing componentsWrong componentsReversed componentsBent leadsAnalog specificationsDigital logicPerformance (timing)

Occurrence frequency (%)

51 1 613 6 8 5 5 5

Ref.: J. Bateson, In-Circuit Testing, Van Nostrand Reinhold, 1985.

21

Common Fault ModelsCommon Fault Models

Single stuck-at faults Transistor open and short faults Memory faults PLA faults (stuck-at, cross-point,

bridging) Functional faults (processors) Delay faults (transition, path) Analog faults etc.

22

Single Stuck-at FaultSingle Stuck-at Fault Three properties define a single stuck-at fault

Only one line is faulty The faulty line is permanently set to 0 or 1 The fault can be at an input or output of a gate

Example: XOR circuit has 12 fault sites ( ) and 24 single stuck-at faults

a

b

c

d

e

f

1

0

g h i 1

s-a-0

j

k

z

0(1)1(0)

1

Test vector for h s-a-0 fault

Good circuit valueFaulty circuit value

23

Fault EquivalenceFault Equivalence Number of fault sites in a Boolean gate circuit =

#PI + #gates + # (fanout branches). Fault equivalence: Fault sets f1 and f2 are

equivalent if all tests that detect f1 also detect f2 and vice versa.

If faults f1 and f2 are equivalent then the corresponding faulty functions are identical.

Fault collapsing: All single faults of a logic circuit can be divided into disjoint equivalence subsets, where all faults in a subset are mutually equivalent. A collapsed fault set contains one fault from each equivalence subset.

24

Equivalence RulesEquivalence Rules

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0

sa1

sa0

sa1

sa0

sa0sa1

sa1

sa0

sa0

sa0sa1

sa1

sa1

AND

NAND

OR

NOR

WIRE

NOT

FANOUT

25

Dominance ExampleDominance Example

sa0 sa1sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

sa0 sa1

Faults in redremoved byequivalencecollapsing

15Collapse ratio = ── = 0.47 32

Faults in yellowremoved bydominancecollapsing

26

Fault DominanceFault Dominance If all tests of some fault F1 detect another fault F2, then F2 is

said to dominate F1. Dominance fault collapsing: If fault F2 dominates F1, then F2

is removed from the fault list. When dominance fault collapsing is used, it is sufficient to

consider only the input faults of Boolean gates. See the next example.

In a tree circuit (without fanouts) PI faults form a dominance collapsed fault set.

If two faults dominate each other then they are equivalent.

27

Dominance ExampleDominance Example

s-a-1F1

s-a-1F2 001

110 010 000101 100

011

All tests of F2

Only test of F1s-a-1

s-a-1

s-a-1s-a-0

A dominance collapsed fault set

28

CheckpointsCheckpoints Primary inputs and fanout branches of a combinational

circuit are called checkpoints. Checkpoint theorem: A test set that detects all single

(multiple) stuck-at faults on all checkpoints of a combinational circuit, also detects all single (multiple) stuck-at faults in that circuit.

Total fault sites = 16

Checkpoints ( ) = 10

29

Transistor (Switch) Faults

Transistor (Switch) Faults

MOS transistor is considered an ideal switch and two types of faults are modeled:

Stuck-open -- a single transistor is permanently stuck in the open state.

Stuck-short -- a single transistor is permanently shorted irrespective of its gate voltage.

Detection of a stuck-open fault requires two vectors.

Detection of a stuck-short fault requires the measurement of quiescent current (IDDQ).

30

Stuck-Open ExampleStuck-Open Example

Two-vector s-op testcan be constructed byordering two s-at testsA

B

VDD

C

pMOSFETs

nMOSFETs

Stuck-open

1

0

0

0

0 1(Z)

Good circuit states

Faulty circuit states

Vector 1: test for A s-a-0(Initialization vector)

Vector 2 (test for A s-a-1)

31

Stuck-Short ExampleStuck-Short Example

A

B

VDD

C

pMOSFETs

nMOSFETs

Stuck-short

1

0

0 (X)

Good circuit state

Faulty circuit state

Test vector for A s-a-0

IDDQ path infaulty circuit

32

Functional vs. Structural ATPGFunctional vs.

Structural ATPG

33

Carry CircuitCarry Circuit

34

Functional vs. Structural(Continued)

Functional vs. Structural(Continued)

Functional ATPG – generate complete set of tests for circuit input-output combinations 129 inputs, 65 outputs: 2129 = 680,564,733,841,876,926,926,749,

214,863,536,422,912 patterns Using 1 GHz ATE, would take 2.15 x 1022 years

Structural test: No redundant adder hardware, 64 bit slices Each with 27 faults (using fault equivalence) At most 64 x 27 = 1728 faults (tests) Takes 0.000001728 s on 1 GHz ATE

Designer gives small set of functional tests – augment with structural tests to boost coverage to 98+ %

35

Exhaustive AlgorithmExhaustive Algorithm

For n-input circuit, generate all 2n input patterns

Infeasible, unless circuit is partitioned into cones of logic, with 15 inputs Perform exhaustive ATPG for each cone Misses faults that require specific

activation patterns for multiple cones to be tested

36

Random-Pattern Generation

Random-Pattern Generation

Flow chart for method

Use to get tests for 60-80% of faults, then switch to D-algorithm or other ATPG for rest

37

History of Algorithm Speedups

History of Algorithm Speedups

Algorithm

D-ALGPODEMFANTOPSSOCRATESWaicukauski et al.ESTTRANRecursive learningTafertshofer et al.

Est. speedup over D-ALG(normalized to D-ALG time)17232921574 ATPG System2189 ATPG System8765 ATPG System3005 ATPG System48525057

Year

1966198119831987198819901991199319951997

† † †

†

Testability MeasuresTestability Measures

Definition Controllability and observability SCOAP measures

Combinational circuits Sequential circuits

Summary

38

What are Testability Measures?

What are Testability Measures?

Approximate measures of: Difficulty of setting internal circuit lines to 0 or 1

from primary inputs. Difficulty of observing internal circuit lines at

primary outputs. Applications:

Analysis of difficulty of testing internal circuit parts – redesign or add special test hardware.

Guidance for algorithms computing test patterns – avoid using hard-to-control lines.

39

Testability AnalysisTestability Analysis

Determines testability measures Involves Circuit Topological analysis, but no test vectors (static analysis) and no search algorithm. Linear computational complexity

Otherwise, is pointless – might as well use automatic test-pattern generation and calculate:

Exact fault coverage Exact test vectors

40

SCOAP MeasuresSCOAP Measures SCOAP – Sandia Controllability and Observability Analysis

Program Combinational measures:

CC0 – Difficulty of setting circuit line to logic 0 CC1 – Difficulty of setting circuit line to logic 1 CO – Difficulty of observing a circuit line

Sequential measures – analogous: SC0 SC1 SO

Ref.: L. H. Goldstein, “Controllability/Observability Analysis of Digital Circuits,” IEEE Trans. CAS, vol. CAS-26, no. 9. pp. 685 – 693, Sep. 1979.

41

Range of SCOAP MeasuresRange of SCOAP Measures

Controllabilities – 1 (easiest) to infinity (hardest) Observabilities – 0 (easiest) to infinity (hardest) Combinational measures:

Roughly proportional to number of circuit lines that must be set to control or observe given line.

Sequential measures: Roughly proportional to number of times flip-flops

must be clocked to control or observe given line.

42

Combinational ControllabilityCombinational Controllability

43

Controllability Formulas

(Continued)

Controllability Formulas

(Continued)

44

Combinational ObservabilityCombinational ObservabilityTo observe a gate input: Observe output and make other input

values non-controlling.

45

Observability Formulas(Continued)

Observability Formulas(Continued)

Fanout stem: Observe through branch with best observability.

46

Comb. ControllabilityComb. ControllabilityCircled numbers give level number. (CC0, CC1)

47

Controllability Through Level 2

Controllability Through Level 2

48

Final Combinational Controllability

Final Combinational Controllability

49

Combinational Observability for Level

1

Combinational Observability for Level

1Number in square box is level from primary outputs (POs).

(CC0, CC1) CO

50

Combinational Observabilities for Level 2

Combinational Observabilities for Level 2

51

Final Combinational Observabilities

Final Combinational Observabilities

52

Sequential Measures (Comparison)

Sequential Measures (Comparison)

Combinational

Increment CC0, CC1, CO whenever you pass through

a gate, either forward or backward.

Sequential

Increment SC0, SC1, SO only when you pass through

a flip-flop, either forward or backward.

Both

Must iterate on feedback loops until controllabilities

stabilize.

53

D Flip-Flop EquationsD Flip-Flop Equations Assume a synchronous RESET line. SC1 (Q) = SC1 (D) + SC1 (C) + SC0 (C) + SC0

(RESET) + 1 SC0 (Q) = min [SC1 (RESET) + SC1 (C) + SC0 (C),

SC0 (D) + SC1 (C) + SC0 (C)] + 1 SO (D) = SO (Q) + SC1 (C) + SC0 (C) + SC0

(RESET)

54

D Flip-Flop Clock and ResetD Flip-Flop Clock and Reset CO (RESET) = CO (Q) + CC1 (Q) + CC1 (RESET) + CC1 (C) + CC0 (C) SO (RESET) is analogous Three ways to observe the clock line:

1. Set Q to 1 and clock in a 0 from D2. Set the flip-flop and then reset it3. Reset the flip-flop and clock in a 1 from D

CO (C) = min [ CO (Q) + CC1 (Q) + CC0 (D) + CC1 (C) + CC0 (C), CO (Q) + CC1 (Q) + CC1 (RESET) + CC1 (C) + CC0 (C), CO (Q) + CC0 (Q) + CC0 (RESET) + CC1 (D) + CC1 (C) + CC0 (C)] SO (C) is analogous

55

Testability ComputationTestability Computation1. For all PIs, CC0 = CC1 = 1 and SC0 = SC1 = 0

2. For all other nodes, CC0 = CC1 = SC0 = SC1 = ∞3. Go from PIs to POs, using CC and SC equations to get

controllabilities -- Iterate on loops until SC stabilizes -- convergence is guaranteed.

4. Set CO = SO = 0 for POs, ∞ for all other lines.

5. Work from POs to PIs, Use CO, SO, and controllabilities to get observabilities.

6. Fanout stem (CO, SO) = min branch (CO, SO)

7. If a CC or SC (CO or SO) is ∞ , that node is uncontrollable (unobservable).

56

Sequential Example Initialization

Sequential Example Initialization

57

After 1 IterationAfter 1 Iteration

58

After 2 IterationsAfter 2 Iterations

59

After 3 IterationsAfter 3 Iterations

60

Stable Sequential Measures

Stable Sequential Measures

61

Final Sequential Observabilities

Final Sequential Observabilities

62

Testability Measures are Not Exact

Testability Measures are Not Exact

Exact computation of measures is NP-Complete and impractical Green (Italicized) measures show correct (exact) values – SCOAP

measures are in orange -- CC0,CC1 (CO)

1,1(6)1,1(5,∞)

1,1(5)1,1(4,6)

1,1(6)

1,1(5,∞)

6,2(0)4,2(0)

2,3(4)2,3(4,∞)

(5)(4,6)

(6)

(6)

2,3(4)2,3(4,∞)

63

SummarySummary

Testability measures are approximate measures of: Difficulty of setting circuit lines to 0 or 1 Difficulty of observing internal circuit lines

Applications: Analysis of difficulty of testing internal circuit parts

Redesign circuit hardware or add special test hardware where measures show poor controllability or observability.

Guidance for algorithms computing test patterns – avoid using hard-to-control lines

64

ExerciseExercise

Compute (CC0, CC1) CO for all lines in the following circuit.

Questions: 1. Is observability of primary input correct?

2. Are controllabilities of primary outputs correct?

3. What do the observabilities of the input lines ofthe AND gate indicate?

65

66

Major Combinational Automatic Test-Pattern Generation Algorithms

Major Combinational Automatic Test-Pattern Generation Algorithms

Definitions D-Algorithm (Roth) – 1966 PODEM (Goel) -- 1981

67

Forward ImplicationForward Implication Results in logic gate

inputs that are significantly labeled so that output is uniquely determined

AND gate forward implication table:

68

Backward ImplicationBackward Implication

Unique determination of all gate inputs when the gate output and some of the inputs are given

69

Implication StackImplication Stack Push-down stack. Records:

Each signal set in circuit by ATPG Whether alternate signal value already tried Portion of binary search tree already searched

70

Implication Stack after Backtrack

Implication Stack after Backtrack

0

1

0 0

0

0

0 11 1

1

E

F

BB

F F

1

UnexploredPresent AssignmentSearched and Infeasible

71

Objectives and Backtracing of ATPG

Algorithm

Objectives and Backtracing of ATPG

Algorithm Objective – desired signal value goal for ATPG

Guides it away from infeasible/hard solutions Backtrace – Determines which primary input

and value to set to achieve objective Use testability measures

72

D-Algorithm -- Roth IBM

(1966)

D-Algorithm -- Roth IBM

(1966) Fundamental concepts invented:

First complete ATPG algorithm D-Cube D-Calculus Implications – forward and backward Implication stack Backtrack Test Search Space

73

Primitive D-Cube of Failure

Primitive D-Cube of Failure

Models circuit faults: Stuck-at-0 Stuck-at-1 Bridging fault (short circuit) Arbitrary change in logic function

AND Output sa0: “1 1 D” AND Output sa1: “0 X D ”

“X 0 D ” Wire sa0: “D” Propagation D-cube – models

conditions under which fault effect propagates through gate

74

Implication ProcedureImplication Procedure

1. Model fault with appropriate primitive D-cube of failure (PDF)

2. Select propagation D-cubes to propagate fault effect to a circuit output (D-drive procedure)

3. Select singular cover cubes to justify internal circuit signals (Consistency procedure)

Put signal assignments in test cube Regrettably, cubes are selected very

arbitrarily by D-ALG

75

D-Algorithm – Top LevelD-Algorithm – Top Level

1. Number all circuit lines in increasing level order from PIs to POs;

2. Select a primitive D-cube of the fault to be the test cube;

Put logic outputs with inputs labeled as D (D) onto the D-frontier;

3. D-drive ();4. Consistency ();5. return ();

76

D-Algorithm – D-driveD-Algorithm – D-drivewhile (untried fault effects on D-frontier)

select next untried D-frontier gate for propagation;while (untried fault effect fanouts exist)

select next untried fault effect fanout;generate next untried propagation D-cube;D-intersect selected cube with test cube;if (intersection fails or is undefined) continue;if (all propagation D-cubes tried & failed) break;if (intersection succeeded)

add propagation D-cube to test cube -- recreate D-frontier;Find all forward & backward implications of assignment;save D-frontier, algorithm state, test cube, fanouts, fault;break;

else if (intersection fails & D and D in test cube) Backtrack ();else if (intersection fails) break;

if (all fault effects unpropagatable) Backtrack ();

77

D-Algorithm -- ConsistencyD-Algorithm -- Consistencyg = coordinates of test cube with 1’s & 0’s;if (g is only PIs) fault testable & stop;for (each unjustified signal in g)

Select highest # unjustified signal z in g, not a PI;if (inputs to gate z are both D and D) break;while (untried singular covers of gate z)

select next untried singular cover;if (no more singular covers)

If (no more stack choices) fault untestable & stop;else if (untried alternatives in Consistency)

pop implication stack -- try alternate assignment;else

Backtrack ();D-drive ();

If (singular cover D-intersects with z) delete z from g, add inputs to singular cover to g, find all forward and backward implications of new assignment, and break;

If (intersection fails) mark singular cover as failed;

78

BacktrackBacktrack

if (PO exists with fault effect) Consistency ();else pop prior implication stack setting to try

alternate assignment;if (no untried choices in implication stack)

fault untestable & stop;else return;

79

Example 7.2 Fault A sa0Example 7.2 Fault A sa0

Step 1 – D-Drive – Set A = 1

D1 D

80

Step 2 -- Example 7.2Step 2 -- Example 7.2

D1

0

D

Step 2 – D-Drive – Set f = 0

D

81


D1

0

D

Step 3 – D-Drive – Set k = 1

D

1

D

82


D1

0

D

Step 4 – Consistency – Set g = 1

D

1

D

1

83


D1

0

D

Step 5 – Consistency – f = 0 Already set

D

1

D

1

84


D1

0

D

Step 6 – Consistency – Set c = 0, Set e = 0

D

1

D

1

0

0

85

D-Chain Dies -- Example 7.2

D-Chain Dies -- Example 7.2

D1

0

X

D

Step 7 – Consistency – Set B = 0 D-Chain dies

D

1

D

1

0

0

0

Test cube: A, B, C, D, e, f, g, h, k, L

86

Example 7.3 – Fault s sa1

Example 7.3 – Fault s sa1

Primitive D-cube of Failure

1

Dsa1

87

Example 7.3 – Step 2 s sa1


Propagation D-cube for v

1

D

0

sa1 D1D

88

Example 7.3 – Step 2 s sa1Example 7.3 – Step 2 s sa1 Forward & Backward Implications

1

Dsa1

0D

D

1 1

0

11

89



Propagation D-cube for Z – test found!

1

Dsa1

0D

D

1 1

0

11

1

D

90

Example 7.3 – Fault u sa1

Example 7.3 – Fault u sa1

Primitive D-cube of Failure

1

D

0

sa1

91

Example 7.3 – Step 2 u sa1Example 7.3 – Step 2 u sa1 Propagation D-cube for v

1

D

0

sa1D

0

92

Example 7.3 – Step 2 u sa1Example 7.3 – Step 2 u sa1 Forward and backward implications

1

D

0

sa1D

0

01

0

1

0

93

InconsistentInconsistent

d = 0 and m = 1 cannot justify r = 1 (equivalence) Backtrack Remove B = 0 assignment

94

Example 7.3 – BacktrackExample 7.3 – Backtrack Need alternate propagation D-cube for v

1

sa1 D

0

95

Example 7.3 – Step 3 u sa1Example 7.3 – Step 3 u sa1 Propagation D-cube for v

1

sa1 D

01

D

96

Example 7.3 – Step 4 u sa1


Propagation D-cube for Z

D

1

sa1D

01

D

1

1

97



Propagation D-cube for Z and implications

D

1

sa1D

01

D

1

1

00

0

1 1

98

PODEM -- Goel IBM

(1981)

PODEM -- Goel IBM

(1981)

New concepts introduced: Expand binary decision tree only

around primary inputs Use X-PATH-CHECK to test

whether D-frontier still there

Objectives -- bring ATPG closer to propagating D (D) to PO

Backtracing

99

MotivationMotivation

IBM introduced semiconductor DRAM memory into its mainframes – late 1970’s

Memory had error correction and translation circuits – improved reliability D-ALG unable to test these circuits

Search too undirected Large XOR-gate trees Must set all external inputs to define

output Needed a better ATPG tool

100

PODEM High-Level FlowPODEM High-Level Flow

1. Assign binary value to unassigned PI2. Determine implications of all PIs3. Test Generated? If so, done.4. Test possible with more assigned PIs? If

maybe, go to Step 15. Is there untried combination of values on

assigned PIs? If not, exit: untestable fault6. Set untried combination of values on

assigned PIs using objectives and backtrace. Then, go to Step 2

101

Select path s – Y for fault propagation

sa1

Example 7.3 AgainExample 7.3 Again

102

Initial objective: Set r to 1 to sensitize fault

1

sa1

Example 7.3 -- Step 2 s sa1


103


Example 7.3 -- Step 3 s sa1 Backtrace from r

1

sa1

104

Example 7.3 -- Step 4 s sa1Example 7.3 -- Step 4 s sa1 Set A = 0 in implication stack

1

0

sa1

105

Example 7.3 -- Step 5 s sa1Example 7.3 -- Step 5 s sa1 Forward implications: d = 0, X = 1

1

sa1

00

1

106

Example 7.3 -- Step 6 s sa1Example 7.3 -- Step 6 s sa1 Initial objective: set r to 1

1

sa1

00

1

107

Example 7.3 -- Step 7 s sa1Example 7.3 -- Step 7 s sa1 Backtrace from r again

1

sa1

00

1

108

Example 7.3 -- Step 8 s sa1Example 7.3 -- Step 8 s sa1 Set B to 1. Implications in stack: A = 0, B = 1

1

sa1

00

1

1

109

D

Example 7.3 -- Step 9 s sa1Example 7.3 -- Step 9 s sa1 Forward implications: k = 1, m = 0, r = 1, q =

1, Y = 1, s = D, u = D, v = D, Z = 1

1

sa1

1

0

1

1

DD

1

0

1

0

1

110

Backtrack -- Step 10 s sa1

Backtrack -- Step 10 s sa1 X-PATH-CHECK shows paths s – Y and s – u – v – Z blocked (D-frontier

disappeared)

1

sa1

00

1

111

Step 11 -- s sa1Step 11 -- s sa1 Set B = 0 (alternate assignment)

1

sa1

0

0

112

Backtrack -- s sa1Backtrack -- s sa1

1sa1

00

1

0 1

0

1

01

01

Forward implications: d = 0, X = 1, m = 1, r = 0, s = 1, q = 0, Y = 1, v = 0, Z = 1. Fault not sensitized.

113

Step 13 -- s sa1Step 13 -- s sa1 Set A = 1 (alternate assignment)

1

sa1

1

114

Step 14 -- s sa1Step 14 -- s sa1 Backtrace from r again

1

sa1

1

115

Step 15 -- s sa1Step 15 -- s sa1 Set B = 0. Implications in stack: A = 1, B = 0

1

sa1

1

0

116

Backtrack -- s sa1Backtrack -- s sa1 Forward implications: d = 0, X = 1, m = 1, r =

0. Conflict: fault not sensitized. Backtrack

sa1

1

0

0

0

1

1

1

1

10

01

117

Step 17 -- s sa1Step 17 -- s sa1 Set B = 1 (alternate assignment)

1

sa1

1

1

118

Fault Tested -- Step 18 s sa1

Fault Tested -- Step 18 s sa1 Forward implications: d = 1, m = 1, r = 1, q =

0, s = D, v = D, X = 0, Y = D

1

sa1

1

1

11

0

D

0

D

D

X

D

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Backtrace (s, vs)Pseudo-Code

Backtrace (s, vs)Pseudo-Code

v = vs;while (s is a gate output)

if (s is NAND or INVERTER or NOR) v = v;if (objective requires setting all inputs)

select unassigned input a of s with hardest controllability to value v;

elseselect unassigned input a of s with easiest

controllability to value v;s = a;

return (s, v) /* Gate and value to be assigned */;

120

Objective Selection CodeObjective Selection Code

if (gate g is unassigned) return (g, v);select a gate P from the D-frontier;select an unassigned input l of P;if (gate g has controlling value)

c = controlling input value of g;else if (0 value easier to get at input

of XOR/EQUIV gate)c = 1;

else c = 0;return (l, c );

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PODEM AlgorithmPODEM Algorithmwhile (no fault effect at POs)

if (xpathcheck (D-frontier))

(l, vl) = Objective (fault, vfault);

(pi, vpi) = Backtrace (l, vl);

Imply (pi, vpi);

if (PODEM (fault, vfault) == SUCCESS) return (SUCCESS);

(pi, vpi) = Backtrack ();

Imply (pi, vpi);

if (PODEM (fault, vfault) == SUCCESS) return (SUCCESS);Imply (pi, “X”);return (FAILURE);

else if (implication stack exhausted)return (FAILURE);

else Backtrack ();return (SUCCESS);

FAN -- Fujiwara and Shimono(1983)

FAN -- Fujiwara and Shimono(1983)

New concepts: Immediate assignment of

uniquely-determined signals Unique sensitization Stop Backtrace at head lines Multiple Backtrace

122

PODEM Fails to Determine Unique Signals

PODEM Fails to Determine Unique Signals

Backtracing operation fails to set all 3 inputs of gate L to 1 Causes unnecessary search

123

FAN -- Early Determination of Unique

Signals

FAN -- Early Determination of Unique

Signals

Determine all unique signals implied by current decisions immediately Avoids unnecessary search

124

PODEM Makes Unwise Signal Assignments

PODEM Makes Unwise Signal Assignments

Blocks fault propagation due to assignment J = 0

125

Unique Sensitization of FAN with No Search

Unique Sensitization of FAN with No Search

FAN immediately sets necessary signals to propagate fault

Path over which fault is uniquely sensitized

126

HeadlinesHeadlines

Headlines H and J separate circuit into 3 parts, for which test generation can be done independently

127

Contrasting Decision TreesContrasting Decision Trees

PODEM decision tree

FAN decision tree

128

Multiple BacktraceMultiple Backtrace

FAN – breadth-firstpasses –

1 time

PODEM –depth-first

passes – 6 times

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AND Gate Vote Propagation

AND Gate Vote Propagation

AND Gate Easiest-to-control Input –

# 0’s = OUTPUT # 0’s # 1’s = OUTPUT # 1’s

All other inputs -- # 0’s = 0 # 1’s = OUTPUT # 1’s

[5, 3]

[5, 3]

[0, 3]

[0, 3]

[0, 3]

130

Multiple Backtrace Fanout Stem VotingMultiple Backtrace Fanout Stem Voting

Fanout Stem -- # 0’s = Branch # 0’s, # 1’s = Branch # 1’s

[5, 1][1, 1][3, 2]

[4, 1]

[5, 1]

[18, 6]

131

Multiple Backtrace Algorithm

Multiple Backtrace Algorithm

repeat

remove entry (s, vs) from current_objectives;

If (s is head_objective) add (s, vs) to head_objectives;

else if (s not fanout stem and not PI)vote on gate s inputs;if (gate s input I is fanout branch)

vote on stem driving I;add stem driving I to stem_objectives;

else add I to current_objectives;

132

Rest of Multiple BacktraceRest of Multiple Backtraceif (stem_objectives not empty)

(k, n0 (k), n1 (k)) = highest level stem from stem_objectives;

if (n0 (k) > n1 (k)) vk = 0;

else vk = 1;

if ((n0 (k) != 0) && (n1 (k) != 0) && (k not in fault cone))

return (k, vk);

add (k, vk) to current_objectives;

return (multiple_backtrace (current_objectives));remove one objective (k, vk) from head_objectives;

return (k, vk);133

Logic simulation and fault simulation

Logic simulation and fault simulation

134

True-Value Simulation Algorithms

True-Value Simulation Algorithms

Compiled-code simulation Applicable to zero-delay combinational logic Also used for cycle-accurate synchronous sequential

circuits for logic verification Efficient for highly active circuits, but inefficient for low-

activity circuits High-level (e.g., C language) models can be used

Event-driven simulation Only gates or modules with input events are evaluated

(event means a signal change) Delays can be accurately simulated for timing verification Efficient for low-activity circuits Can be extended for fault simulation

135

Compiled-Code Algorithm

Compiled-Code Algorithm

Step 1: Levelize combinational logic and encode in a compilable programming language

Step 2: Initialize internal state variables (flip-flops) Step 3: For each input vector

Set primary input variables Repeat (until steady-state or max. iterations)

Execute compiled code

Report or save computed variables

136

Event-Driven Algorithm(Example)

Event-Driven Algorithm(Example)

2

2

4

2

a =1

b =1

c =1 0

d = 0

e =1

f =0

g =1

Time, t 0 4 8

g

t = 0

1

2

3

4

5

6

7

8

Scheduledevents

c = 0

d = 1, e = 0

g = 0

f = 1

g = 1

Activitylist

d, e

f, g

g

Tim

e s

tack

137

Time Wheel (Circular Stack)Time Wheel (Circular Stack)

t=01

2

3

45

67

maxCurrenttimepointer Event link-list

138

Efficiency of Event-driven Simulator

Efficiency of Event-driven Simulator

Simulates events (value changes) only Speed up over compiled-code can be ten times or

more; in large logic circuits about 0.1 to 10% gates become active for an input change

Large logicblock without

activity

Steady 0

0 to 1 event

Steady 0(no event)

139

Fault Simulation Algorithms

Fault Simulation Algorithms

Serial Parallel Deductive Concurrent Differential

140

Serial AlgorithmSerial Algorithm Algorithm: Simulate fault-free circuit and save

responses. Repeat following steps for each fault in the fault list:

Modify netlist by injecting one fault Simulate modified netlist, vector by vector, comparing

responses with saved responses If response differs, report fault detection and suspend

simulation of remaining vectors Advantages:

Easy to implement; needs only a true-value simulator, less memory

Most faults, including analog faults, can be simulated

141

Serial Algorithm (Cont.)Serial Algorithm (Cont.) Disadvantage: Much repeated computation; CPU

time prohibitive for VLSI circuits Alternative: Simulate many faults together

Test vectors Fault-free circuit

Circuit with fault f1

Circuit with fault f2

Circuit with fault fn

Comparator f1 detected?

Comparator f2 detected?

Comparator fn detected?

142

Parallel Fault Simulation

Parallel Fault Simulation

Compiled-code method; best with two-states (0,1)

Exploits inherent bit-parallelism of logic operations on computer words

Storage: one word per line for two-state simulation

Multi-pass simulation: Each pass simulates w-1 new faults, where w is the machine word length

Speed up over serial method ~ w-1

143

Parallel Fault Sim. Example

Parallel Fault Sim. Example

a

b c

d

e

f

g

1 1 1

1 1 1 1 0 1

1 0 1

0 0 0

1 0 1

s-a-1

s-a-0

0 0 1

c s-a-0 detected

Bit 0: fault-free circuit

Bit 1: circuit with c s-a-0

Bit 2: circuit with f s-a-1

144

Deductive Fault Simulation

Deductive Fault Simulation

One-pass simulation Each line k contains a list Lk of faults

detectable on k Following true-value simulation of each vector,

fault lists of all gate output lines are updated using set-theoretic rules, signal values, and gate input fault lists

PO fault lists provide detection data Limitations:

Set-theoretic rules difficult to derive for non-Boolean gates

Gate delays are difficult to use

145

Deductive Fault Sim.Example

Deductive Fault Sim.Example

a

b c

d

e

f g

1

1 1

0

1

{a0}

{b0 , c0}

{b0}

{b0 , d0}

Le = La U Lc U {e0}

= {a0 , b0 , c0 , e0}

Lg = (Le Lf ) U {g0}

= {a0 , c0 , e0 , g0}

U

{b0 , d0 , f1}

Notation: Lk is fault list for line k

kn is s-a-n fault on line

k

Faults detected bythe input vector

146

Concurrent Fault SimulationConcurrent Fault Simulation Event-driven simulation of fault-free circuit and only

those parts of the faulty circuit that differ in signal states from the fault-free circuit.

A list per gate containing copies of the gate from all faulty circuits in which this gate differs. List element contains fault ID, gate input and output values and internal states, if any.

All events of fault-free and all faulty circuits are implicitly simulated.

Faults can be simulated in any modeling style or detail supported in true-value simulation (offers most flexibility.)

Faster than other methods, but uses most memory.

147

Conc. Fault Sim. ExampleConc. Fault Sim. Example

a

b c

d

e

f

g

1

11

0

1

1

11

1

01

1 0

0

10

1

00

1

00

1

10

1

00

1

11

1

11

0

00

0

11

0

00

0

00

0 1 0 1 1 1

a0 b0 c0 e0

a0 b0

b0

c0 e0

d0d0 g0 f1

f1

148

Fault Coverage and Efficiency

Fault Coverage and Efficiency

Fault coverage =

Faultefficiency

# of detected faultsTotal # faults

# of detected faultsTotal # faults -- # undetectable faults

=

149

Test Generation SystemsTest Generation Systems

CircuitDescription

TestPatterns

UndetectedFaults

RedundantFaults

AbortedFaults

BacktrackDistribution

Fault

List

Compacter

SOCRATESWith faultsimulator

150

Test Compaction Example

Test Compaction Example

t1 = 0 1 X t2 = 0 X 1

t3 = 0 X 0 t4 = X 0 1

Combine t1 and t3, then t2 and t4 Obtain:

t13 = 0 1 0 t24 = 0 0 1

Test Length shortened from 4 to 2

151

Test CompactionTest Compaction

Fault simulate test patterns in reverse order of generation ATPG patterns go first Randomly-generated patterns go last

(because they may have less coverage) When coverage reaches 100%, drop

remaining patterns (which are the useless random ones)

Significantly shortens test sequence – economic cost reduction

152

Static and Dynamic Compaction of Sequences

Static and Dynamic Compaction of Sequences Static compaction

ATPG should leave unassigned inputs as X Two patterns compatible – if no conflicting

values for any PI Combine two tests ta and tb into one test

tab = ta tb using D-intersection

Detects union of faults detected by ta & tb Dynamic compaction

Process every partially-done ATPG vector immediately

Assign 0 or 1 to PIs to test additional faults

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Sequential CircuitsSequential Circuits

A sequential circuit has memory in addition to combinational logic.

Test for a fault in a sequential circuit is a sequence of vectors, which

Initializes the circuit to a known state Activates the fault, and Propagates the fault effect to a primary output

Methods of sequential circuit ATPG Time-frame expansion methods Simulation-based methods

154

Example: A Serial Adder

Example: A Serial Adder

FF

An Bn

Cn Cn+1

Sn

s-a-0

11

1

1

1X

X

X

D

D

Combinational logic

155

Time-Frame ExpansionTime-Frame Expansion

An Bn

FF

Cn Cn+11

X

X

Sn

s-a-011

11

D

D

Combinational logicSn-1

s-a-011

11 X

D

D

Combinational logic

Cn-1

11

D

D

X

An-1 Bn-1 Time-frame -1 Time-frame 0

156

Concept of Time-Frames

Concept of Time-Frames

If the test sequence for a single stuck-at fault contains n vectors,

Replicate combinational logic block n times Place fault in each block Generate a test for the multiple stuck-at fault

using combinational ATPG with 9-valued logic

Comb.block

Fault

Time-frame0

Time-frame-1

Time-frame-n+1

Unknownor givenInit. state

Vector 0Vector -1Vector -n+1

PO 0PO -1PO -n+1

Statevariables

Nextstate

157

Example for Logic Systems

Example for Logic Systems

FF2

FF1

A

B

s-a-1

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Five-Valued Logic (Roth)0,1, D, D, X

Five-Valued Logic (Roth)0,1, D, D, X

A

B

X

X

X

0

s-a-1D

A

B

X X

X

0

s-a-1D

FF1 FF1

FF2 FF2D D

Time-frame -1 Time-frame 0

159

Nine-Valued Logic (Muth)0,1, 1/0, 0/1, 1/X, 0/X, X/0, X/1,

X

Nine-Valued Logic (Muth)0,1, 1/0, 0/1, 1/X, 0/X, X/0, X/1,

XA

B

X

X

X

0

s-a-10/1

A

B

0/X 0/X

0/1

X

s-a-1X/1

FF1 FF1

FF2 FF20/1 X/1

Time-frame -1 Time-frame 0

160

Testing 1. 2 Problems of Ideal Tests n Ideal tests detect all defects produced in the manufacturing...

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