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Builtin Fault Diagnosis for Tunable Analog Systems Using an Ensemble Method

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Replacing conventional methods which use expensive measurement ... Measurement errors and prediction errors (outliers) Significant influence on outcome ... – PowerPoint PPT presentation

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Title: Builtin Fault Diagnosis for Tunable Analog Systems Using an Ensemble Method


1
Built-in Fault Diagnosis for Tunable Analog
Systems Using an Ensemble Method
Part I
Specification-driven Embedded Self-Testfor
Mixed-Signal Circuits using loopback
Hongjoong Shin, Byoungho Kim and Jacob A.
Abraham VTS 2006
Part II
  • Hongjoong Shin, Joonsung Park and Jacob A.
    Abraham
  • Submitted to ITC 2006

2
Built-in Fault Diagnosis for Tunable Analog
Systems Using an Ensemble Method
  • Hongjoong Shin, Joonsung Park and
  • Jacob A. Abraham
  • Submitted to ITC 2006

Computer Engineering Research Center Department
of Electrical and Computer Engineering University
of Texas at Austin
3
Analog fault diagnosis
  • Fault diagnosis is important process to improve
    yield and reduce time-to-market slamani ITC92
  • Identification of design weak spots and problems
    in manufacturing process
  • Increasingly important due to parametric yield
    loss

4
Conventional fault diagnosis approach
  • Apply a set of test inputs
  • Test input is selected to highlight a fault
  • However, it may be difficult to find test inputs
  • which effectively isolate a fault
  • Various observations are used
  • Internal nodes as well as primary outputs
  • Magnitude of current and voltage, and phase
  • Performance parameters are insufficient to
    provide accurate diagnosis
  • Number of measurements gtgt number of unknown
    parameters
  • Limitations
  • Long diagnosis time
  • Requires broad range of off-chip measurements
    which usually demand different setups and stimuli
  • Use of expensive measurements

Measure- ments
Sensitivity matrix
Compo- nents
5
Motivation
  • Tackles problems of diagnosis in a more smart way
  • Co-operation with Self-Test and Self-Repair
    designs
  • Development of an efficient low-cost fault
    diagnosis methodology for analog and mixed-signal
    circuits
  • Replacing conventional methods which use
    expensive measurement
  • Can be extended to solve problems of
    diagnosis/self-repair/testing simultaneously

How?
Fault Diagnosis
Self-Test
Self-Repair
6
Proposed fault diagnosis scheme
Error
Digital Cores
Low-cost Signatures
DUT
Performance parameters
DFT

Mapping Functions
Signatures
Pass/Fail
Parameter tuning
Ensemble Method
Faults identification
Self-Repair
  • Based on a specification-based alternate test
  • Supplemental signatures (signatures) are
    generated from a re-configured DUT by parameter
    tuning (self-repair circuits)
  • Ensemble Method is used to overcome diagnosis
    inaccuracy caused by imperfect signatures
  • Widely studied in Data Mining

7
Diagnosability enhancement using parameter tuning
  • Parameter tuning is widely used to tune
    performance parameters
  • Redundant components
  • EX) Programmable Capacitor Arrays (PCAs),
    Resistor Arrays
  • Analog filters and various types of ADCs and DACs

Digital Code
CU
2CU
4CU
8CU
CA
Programmable Capacitor Array
Analog filter with parameter tuning
8
Supplemental signature generation using parameter
tuning
  • Re-configured DUT has different
  • sensitivities to same input
  • Higher-order sensitivity effects
  • Changes of operating point
  • Can be extended to other tuning
  • mechanisms
  • Variable biasing current/voltage

Sensitivity
Deviation of C1 ()
Deviation of C1 ()
(b) Sensitivity of R3 to 3dB
(a) Sensitivity of R3 to gain
Tuning cycle 0
DUT
P1 0.2CP 0.1R1
Tuning cycle 1
P2 0.18(CP?C) 0.13R1
Tuning cycle 2
P3 0.15(CP2?C) 0.15R1
9
Supplemental signature generation
  • Large number of signatures can be achieved using
    parameter tuning
  • Typical tuning circuitry in a practical
    implementation uses 4-8 bit PCA ( signatures
    measurements x tuning cycles)
  • Possible to identify faults with few measurements
    (performance parameters)

Tuning cycle 0
DUT
P1 0.2CP 0.1R1
Tuning cycle 1
P2 0.16(CP2?C) 0.17R1
CP
R1
Tuning cycle N
PN 0.15(CPN?C) 0.15R1
10
Issues low-cost signature generation
  • Imperfect signatures may cause errors in solving
    the obtained equations
  • Measurement errors and prediction errors
    (outliers)
  • Significant influence on outcome
  • Equations are solved simultaneously
  • Obtained equations may not be independent
  • Fine tuning (small deviations of coefficients)
  • Within tolerance limits of noise redundant
    signatures
  • May not be helpful for meeting requirements
  • number of measurements gtgt number of unknown
    parameters

Tuning cycle 0
DUT
P1 0.2CP 0.1R1
CP
Tuning cycle 1
P2 0.16(CP2?C) 0.17R1
R1
Tuning cycle N
PN 0.15(CPN?C) 0.15R1
11
Example of Issues Imperfect signatures
faulty measurement
h6
Actual solution
h2
h4
over-fitted estimate
h1
h5
h3
Example 1
Example 2
  • Estimation error can be dictated by a single
    error
  • Estimation is over-fitted to the error

12
Ensemble method
  • Widely studied in data mining and machine
    learning
  • Basic principle
  • Develops a system in which the basic
    functionality is re-implemented with a number of
    models
  • Pursues an effective combination of various
    models so as to compensate each others weakness
  • Thus produces better decisions or predictions

13
Proposed ensemble method I
  • Measurement space diversified using parameter
    tuning is partitioned into groups of measurements
  • Groups of measurements are solved independently
  • Individual estimate is evaluated by
    cross-correlation (distance) and uncorrelated
    estimates are pruned from the solution candidates
  • K-means Clustering algorithm
  • Finally, by averaging the remaining estimates,
    the final estimate is determined

14
Proposed ensemble method II

Measurement Partitioning
Clustering and Ensemble Averaging

LSE
Yes
Reject?
LSE
No
Averaging
LSE
LSE Least Square Estimates
15
Example - proposed ensemble method

faulty measurement
h6
Actual solution
Estimate of G1
h2
Estimate of G3
h4
h1
h5
h3
Estimate of G2
16
Simulation results
  • State-variable active filter (ITC benchmark
    circuit)
  • 3-bit PCA
  • 100 DUTs are generated by introducing statistical
    variations
  • Value of passive elements (Resistor and
    Capacitor) are estimated

Analog filter with 3-bit PCA
17
Simulation setup I

Measurement Error
4
DUT
HPO
Least Square Estimate (LSE)
D 0
4
LPO
C1
CP(D0)
3
BPO
(a) Parameter Estimation based on 11
specifications and D 0

D 0,17
3
Measurement Error
DUT
HPO
LSE
4
D 0,1,,7
LPO
C1
CP(D)
BPO
(b) Parameter Estimation based on 4
specifications and D 0,1,..7
18
Results - Simulation setup I
  • Predicts 5 unknown variables with 4
    specifications
  • Multiplied by tuning parameters
  • Proposed method is as accurate as method which
    uses more measurements (internal node)
  • Note Ensemble Method is not applied yet

19
Simulation setup II

Measurement Error
4
DUT
HPO
Least Square Estimate (LSE)
D 0
4
LPO
C1
CP(D0)
3
BPO
(a) Parameter Estimation based on 11
specifications and D 0

D 1,2,7
3
Measurement Error
4
DUT
HPO
Ensemble Method
4
D 1,2,,7
LPO
C1
CP(D0)
3
BPO
(b) Parameter Estimation based on 11
specifications and D 1,2,..7
20
Results - Simulation setup II
  • Estimation Error Versus Total Number of Tuning
    Cycles

Estimation Errors()
Number of Tuning Cycles
Number of Tuning Cycles
Number of Tuning Cycles
No tuning conventional method
21
Conclusions and Future work
  • Efficient BIST-based fault diagnosis method
    proposed
  • Provides fast and low-cost fault identification
    (signature-based)
  • Removes expensive off-chip measurements
  • Diagnosis accuracy is significantly improved by
    using parameter tuning and ensemble method
  • Overcome on-chip limited resources and accuracy
  • Also, can be used to facilitate a self-repair
    mechanism by accurately identifying the source of
    errors

22
Future work
  • Extend the proposed technique to diagnosis of
    device parameters of active elements
  • Conjunct with variable biasing
  • Develop self-repair algorithm based on proposed
    technique
  • Beat conventional adaptive algorithm


DUT
DUT
Adaptive models
Fault diagnosis
performance
Trial
23
Thank you!! Any questions?
24
Results - Simulation setup II
(a) Estimation Error Versus Total Number of
Tuning Cycles
(b) Estimation Error Versus Injected Measurement
Noise
25
Comparison with previous work
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