J1879 Robustness Validation Hand Book A Joint SAE, ZVEI, JSAE, AEC Automotive Electronics Robustness Validation Plan

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J1879 Robustness Validation Hand Book A Joint
SAE, ZVEI, JSAE, AEC Automotive Electronics
Robustness Validation Plan
Robustness Diagram
Trends and Challenges
Robustness Decision
Robustness Curve - Sufficient Robustness If the
Area Of The Robustness Curve (blue ring in figure
1) lies outside the specification limits, the
robustness of the component is sufficient. In
general, the target is met if the robustness
curve lies outside the specification
limits. Robustness Curve Insufficient
Robustness Strategies for improvement of
insufficient robustness. In case of insufficient
robustness, improvement measures have to be
defined and implemented during the development
phase. Corrections for Unexpected Failures that
are discovered after the development phase should
be corrected and added to the knowledge
base.
The current qualification and verification
methods do not provide statistical evidence that
a device under test will meet customer demand of
parts per million failure rates. The correlation
between applied stress condition and lifetime
under use condition is not established. The
result of a qualification is qualitative, under
the same requirements, it is possible to select
the worst choice. Analyzed returns from the
field demonstrate that some tests are not
detecting deficiencies during the qualification
process. In consequence, a more pragmatic
validation approach must be introduced.
Robustness validation seeks to define the guard
band between the outer limits of the
specification and the component's actual
performance. Robustness is the degree to which a
component or system is impervious or resistant to
factors which can effect its function,
performance or other identified attribute or
quality characteristic.
Solutions for Improvement
Robustness Validation Components

• Before implementing the solution for improving
  robustness, the solution must be reviewed with
  respect to several other aspect, i.e.
• Does the expected improvement meet the
  robustness target?
• Does the improvement solution influence the
  robustness of other failure mechanisms?
• What is the implementation risk (probability that
  the device fails in its implementation)?
• Robustness (1-Implementation Risk) It should be
  taken into account that, in general, from a
  statistical point of view, nearly everything that
  reduces the risk increases the robustness.

• Robustness Validation has four key components
• Knowledge of use/application conditions - Mission
  Profile
• Knowledge of failure mechanisms, failure modes
  and their interactions Captured in the
  Knowledge Matrix database
• Acceleration models for the failure mechanisms
  needed for defining and assessing accelerated
  tests
• Testing of the part until failure or
  determination of End of Life
• Robustness Validation results in a product being
  qualified as fit for application, not fit for
  standard. This approach requires more up front
  communication and explanation between customer
  and supplier than a stress-based qualification.

Figure 1.
The robustness diagram is a way to demonstrate
robustness validation results graphically. The
figure above gives an example for two parameters,
i.e., Temperature and Voltage. The guard band, or
safety margin, between the limits of the
specification and the component's actual
performance determines the components robustness
as indicated by the three robustness curves.
Qualification Process Flow Using Robustness
Validation
Expected Outcomes
Input

• Mindset Change Rather than relying on the
  current reliability system of probability
  prediction, i.e., 95Reliability / 90Confidence
  (50,000 ppm), Robustness Validation provides more
  and better data to prove whether or not a
  component of system will achieve very high
  reliability in its intended application.
• Smarter testing which is quicker, better and
  less expensive than traditional past and present
  methods eliminates the wastes associated with
  low-value testing/analysis and the risks
  associated with insufficient testing/analysis.
• Better more efficient, effective, reliable
  methods for validating changes to the design,
  process and application.
• Address the real problems, including NTF (no
  trouble found), HW/SW interfaces, system issues
  and soft failures such as out-of-spec
  performance.
• Provide methods for preventing and capturing
  problems caused by unintended and undisclosed
  design/process changes.

Knowledge Matrix - A knowledge matrix is needed
to capture the basic mechanisms behind each
potential failure mechanism, the root cause(s) of
each failure mechanism and the effects of
failure to the electronic component, product
performance and application. The knowledge matrix
is the database for generating a qualification
plan based on the application profile and for
generating the reliability performance numbers
under use conditions. Mission Profile - The
mission profile determines, within a specific
group of applications, the range of
environmental, life time and manufacturing
conditions to which the device is exposed during
its life. This life time includes the whole
supply chain (storage, shipping, processing,
operating and non-operating). Based on the
mission profile the potential risks to fail in
the application together with the potential
failure mechanisms can be defined. Robustness
Assessment - A robustness assessment has to be
done separately for each failure mechanism. The
overall component robustness can be estimated by
the failure modes statistical data.
Quality Engineering requirements
Environment defined by mission profile Performance
in the application spec
Failure mechanism Known potential failure
mechanisms Unknown potential failure mechanisms
Qualification plan Reliability tests, test
conditions, duration acceptance, test vehicle,
Sample size / number of lots
Stress tests according to quality plan
Review stress set up Design for
Reliability Screening strategy Technology
solution Application review Mission profile
review
Parameter change over time of stress Fail
distribution Model used for extrapolation
Robustness Assessment
Compare data to requirements Compare delta to
robustness target
Robustness Sufficient?
NO
Improvement
YES
Intrinsic reliability monitoring Extrinsic
reliability monitoring Frequency of
monitoring/sample size Actions on deviations in
monitoring results
Production
Figure 2.