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Design For Reliability
1. Design for Reliability
Hilaire Ananda Perera
Define
Measure
Analyze
Improve
Control
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2. Contents
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What is Reliability
Designing for Reliability
Design for Performance and Reliability Concurrently
Reliability Design Tasks
Derating
Accelerated Life Testing (ALT)
Reliability Estimation
Significance of Weibull β values
Gamma Function Assessment
Prediction Models
Stress/Strength Interference & Probabilistic Design
Reliability Estimation with Safety Margin
Mean and Variance for Any Distribution
Binary Synthesis of Classical Equations
Mean and Standard Deviation of an Algebraic Function
Statistical Data from a Tolerance Statement
False Alarm Probability Estimation
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3. Contents
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cntd.
Screening Strength Estimation
Adaptive Environmental Stress Screening
How CDE Model Parameters Obtained
Product Assurance Rolled Throughput Yield (RTY)
The Challenge: DFR Physics of Failure Approach
Types of Failure Mechanisms
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4. What is Reliability ?
• Reliability is the likelihood(probability) that a
product will
– perform its intended function
– within specified tolerances
– under stated conditions
– for a given period of time
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5. Designing for Reliability
• Reliability part of the core design team
• Reliability modeling guides design
• Failure mechanisms designed-out
• Super-accelerated life testing saves time
• Lifetime is metric for design suitability
• Longer lived products
• Reduce manufacturing variability
Iterative Approach
to Reliability
• Test and fix methodology
• Modeling for performance
• Single stress environmental testing
• 1000 hrs or longer per test
• Designing to specifications
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Gap-Bridging Steps
Cycle Time Reduction
Designing for
Reliability
• Understand Failure Mechanisms
• Share Internal Knowledge
• Develop Reliability Databases
• Deploy Super-accelerated Life Testing
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6. Design for Performance and Reliability Concurrently
Time & Money Saved
Reliability
Target
Enhanced
Design
Redesign
Design
Time & $
Spending More Time in Design Speeds Time to Market
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7. Reliability Design Tasks
Reliability Design Tasks should be performed as early as possible in the
product development and iterated as necessary to effectively impact the
product design, emphasizing the need for up-front reliability design
Program Phase and Scope of Reliability Tasks
Program Phase
Concept and Planning
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Design and
Development
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Production and
Manufacturing
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Purpose
Study product feasibility
Consider alternate solutions
Understand design & operating
environmemt
Define approaches & solutions
for producing a product
Develop models or prototypes
Validate through test, analysis or
simulation
Maintain inherent product
reliability
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July 2002
Scope of Task
Trade-off analysis for critical items
Customer needs refined
Part selection alternatives evaluated
Environmental aspects determined
Integration of design & application
guides
Evaluation of design progress
through analyses and/or tests
Construction of product evaluation
processes
Implement process control and
quality assurance procedures
Operating & maintenance manuals
refined
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8. Derating
Purpose: The purpose of Derating is to enhance the item inherent design
reliability, increase safety margins and reduce repair and replacement
costs. The enhancement is accomplished by compensating for many variables
inherent in any design, some of which include:
What % of the
Maximum
Allowed ?
• Manufacturing Tolerances
• Component Variation
• Material Differences
• Performance Anomalies
• Parameter Drift
Benefits: From an electronic component application, the benefits include lower
failure rates through reduced stresses, less impact from material and
manufacturing variability, proper circuit operation with part parameter changes and
reduction in end of life failures. For mechanical and structural components, a
reduction in stress or increase in strength means a greater factor of safety from
catastrophic failure
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9. Accelerated Life Testing (ALT)
Accelerated Life Testing involves measuring the performance of the
product at accelerated load or stress conditions, in order to induce
pattern failures quickly. The goal is to accelerate failure mechanisms
and the accumulation damage, reducing the time-to-failure. Proper
ALT requires that:
• The failure mechanisms in the accelerated environment are the same as
those observed under normal operating conditions;
• Acceleration transforms are available to confidently extrapolate from the
test life to the usage life of the product under actual operating conditions;
• The failure probability density functions at normal operating levels and
under accelerated conditions are consistent
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10. Reliability Estimation
Reliability R(T) of a Device with a Mean Time (Hours) To Failure of “MTTF” for a specified
Mission Time of “T” Hours using the Weibull reliability function is:
β
R (T ) = e
T
1
−
⋅Γ 1+
MTTF β
If β = 1; MTTF = 30000 Hrs and T = 2 Hrs
Reliability = 0.99993. This means
99.99% of the missions will be
completed successfully
within 2 Hrs
β is the Weibull Shape Parameter. For an Electronic Device, β = 1 (Exponential Distribution) in
the Useful Life period. Γ represents the Gamma Function. Actual β values to determine
Reliability can be derived using Time To Failure data of End-Units from the operating field
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11. Significance of Weibull β Values
Value of β
β<1
β=1
Product Characteristics
Implies infant mortality. If
product survives infant
mortality, its resistance to
failure improves with age
Implies failures are random in
occurrence. An old part is just
as good (or bad) as a new part
Implies early wearout
If This Occurs, Suspect the Following
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β>1&<4
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β>4
Implies old age (rapid) wearout
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Inadequate environmental stress screening
Quality problems in components
Quality problems in manufacturing
Rework/refurbishment problems
Maintenance/human errors
Failures are inherent, not induced
Mixture of failure modes
Electromigration
Low cycle fatigue
Corrosion or erosion failure modes
Scheduled replacement may be cost
effective
Inherent material property limitations
Gross manufacturing process problems
Small variability in manufacturing or
material
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12. Gamma Function Assessment
Enter the “z”
Value Here
This is the
Calculated
Gamma Value
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13. Prediction Models
– IEEE 1413 Reliability Prediction Process Guide
Framework for Hardware & Software predictions at all levels
– Telcordia TR-332 (previously known as Bellcore)
– RDF 2000 (French)
– MIL-HDBK-217F, N2
Piece-part reliability prediction, sum defect rates
No new technology or high complexity models - obsolete
Need to find a replacement . . . . .
– RAC PRISM
Forces holistic consideration of factors influencing Reliability
– Mission & Duty cycle
– All processes
– Devices
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14. Stress/Strength Interference & Probabilistic Design
Reliability prediction using the Stress/Strength Interference and
Probabilistic Design method:
This method assumes that the material properties are time independent
because of their slow change, and the components are not subjected to wear
related failure modes. When components are subjected to reversing mechanical loads that
exhibit a single failure mode, the reliability is designed-in by selecting the probability number
representing the Safety Margin. For the use of this methodology, Binary Synthesis of the
classical equations are needed.
Safety Margin (SM) =
Reliability (R) = 1 -
ES&S DFSS - Design For Reliability
July 2002
µS − µs
σ S 2 + σ s2
1
2Π
∫
SM
−t 2
2
µs = Mean Stress of the Stress function
σs = Standard Deviation of the Stress
µS = Mean Strength of material
σS = Standard Deviation of the material Strength
If SM = 3.5
Reliability = 0.9997
e dt
−∞
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15. Reliability Estimation with Safety Margin
To obtain Reliability,
Go Here and Select
Standard Normal
Cumulative Distribution
Enter “SM” Value
to “Z”
Reliability = 0.999767
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16. Mean and Variance for any Distribution
Let f(x) = Probability Density Function of the independent variable x
If f(x) ≥ 0, for all x
+∞
∫
Mean = µ = xf ( x )dx
−∞
Coefficient of Variation (CV)
provides a relative measure of data
dispersion compared to the Mean
CV =
+∞
σ
µ
∫
2
2
Variance = σ = ( x − µ ) f ( x )dx , where σ = Standard Deviation
−∞
When “x = Time”, Lower boundary of the Integral will be 0 instead of -∞
This is the case for Reliability related functions
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17. Binary Synthesis of Classical Equations
In a pressurized cylinder wall of “External Radius = a” and “Bore Radius = b”, the Failure Governing
Stress Function (s) is the Circumferential (Hoop) Stress
s =
a 2
+ 1
b
P. 2
a
− 1
b
Where P = Internal Pressure
For Reliability calculation using Safety Margin determine the Mean (µ) and the
Standard Deviation (σ) of the variables “a”; “b”; “P” and calculate the µ & σ of
the Stress Function (s)
Note: The methodology for µ and σ calculation is in the slides named Mean and Standard Deviation of an Algebraic
Function and Statistical Data from a Tolerance Statement
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18. Mean and Standard Deviation of an Algebraic Function
Algebraic
Function
c
c.x
c.x + d
c.x - d
x+y
x–y
* x.y
Mean
Standard Deviation
c
c.µx
c.µx + d
c.µx - d
µx + µy
0
c.σx
c.σx
c.σx
µx - µy
(µ
µx . µy
x
*
y
µx
µy
n
µxn
* x
* x
0.5
(σ
(σ
(0.5. 4.µ
2
x
− 2.σ x
2
x
)
0.5
+σ y
2 0.5
2
+σ y
2 0.5
x
x
.σ y + µ y .σ x + σ x .σ y
2
1
µ
y
2
)
)
2
(µ
2
2
µ x .σ y + µ y .σ x
.
µ y2 +σ y2
( n −1)
n.µ x
.σ x
2
2
2
− 0.5. 4.µ x − 2.σ x
2
x
2
2
2
)
2 0.5
0.5
)
0.5
c, d, n are constants
* These are good approximations when the Coefficient of Variation (CV) is small. i.e. CV < 0.1
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19. Statistical Data from a Tolerance Statement
When the distributional Probability Density Function of a variable is Normal
(or Gaussian) between the limits Low “a” and High “b”
The Mean (µ) is approximately equal to
a+b
2
The Standard Deviation (σ) is approximately equal to
For a 10K Ohms Resistor with
±5% tolerance
µ = 10K Ohms
σ = 167 Ohms
b−a
6
These simplified calculations are based on theoretical derivations and was justified by
E. B. Haugen in University of Arizona, 1974
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20. False Alarm Probability Estimation
Voltage Divider Circuitry for Min_Limit & Max_Limit
Tested Output Voltage = 12V +/- 180mV µ = 12 ; σ = 0.06
Resistor
X
VRef = 15V +/- 180 mV
Resistor
Y
VTest = (Y/(X+Y))* VRef.
µ = 15; σ = 0.06
Grnd
False Alarms can happen
due to Component
Tolerances and Voltage
Deviations
Resistor Tolerance is +/- 10%
Case 1: Min_Limit; X=100K Ohms; Y=302K Ohms µ (VTest) = 11.27 ; σ(VTest) = 0.064
Safety Margin (SM) = 8.343
Probability of Failure = 0.00E+00
Case 2: Max_Limit; X=100K Ohms; Y=541K Ohms µ (VTest) = 12.66 ; σ(VTest) = 0.079
Safety Margin (SM) = 6.679
Probability of Failure = 1.2084E-11
False Alarm Probability = 1.2084E-11
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21. Screening Strength Estimation
The Screening Strength of a given stress screen profile is defined as the
probability that the stress screen will precipitate a latent defect into a
detectable failure, given that a defect is present. Screening provide
assurance on the Outgoing Reliability
Screening Strength for Temperature Cycling (STn) is a function of Temperature Range =T;
Temp. Rate of Change =R; Number of Cycles =n
STn
= 1 − e[−[0.0017⋅(T + 0.6)
0.6
⋅ln(e + R ) ⋅n]]
3
Screening Strength for Random Vibration (SVt) is a function of G = gRMS; Vibration Duration = t
SVt
= 1 − e[−(0.0046⋅G
1.71
⋅t )]
Combined Screen Strength (SS) = 1 - (1-STn).(1-SVt)
When T = 111oC; R = 5oC/Min
n =16 Cyc; G = 2gRMS;
t = 15 Min
SS = 0.98432
The Screening Strength equations were developed by Hughes Aircraft Company, and modified by Rome Air
Development Centre (RADC) based on the data from McDonnel Aircraft Co. and Grumman Aerospace
Corporation
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22. Adaptive Environmental Stress Screening (ESS)
The principle of adaptive screening is to adjust the screens on the basis of
observed screening results, so that the screens are always most costeffective while meeting ESS program goals. Contract terms should be
flexible enough to permit modifications of screening parameters when such
modifications can be shown to be beneficial
The Chance Defective Exponential (CDE) Model is the chosen prediction model for failure rate
distribution analysis, as the constant failure rate portion could be extracted for Acceleration
Factor calculation, the average rate of defect precipitation determined for Best Thermal Cycling
Time and Failure Free Time calculation. CDE equation parameters are obtained using the
SigmaPlot computer program
P/N XXXX100-07 ESS
Period: 01 Jan 99 - 31 Dec 99
0.04
Failure
Rate (fr)
0.03
Fail/Hour
Outgoing Defect Density = 5300 PPM
Yield = 0.9947
4σ < Capability < 5σ
Time To Remove 99.999% Defects = 32 Hrs
Failure Free Time (99.99% Yield) at 90% LCL = 20Hrs
0.02
fr = 0.0031+ 0.0385*exp(-0.3669*t)
0.01
0.00
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
ESS Time (t) Hours
CONDITION: Cumulative Thermal Energy due to previous runs And/Or NFF
And/Or BIE Failure are also responsible for relevant failure precipitation
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23. How CDE Model Parameters Obtained
Time To
Failure
Data
Failure
Rate
Data
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CDE Model
Parameters
Coefficient of
Variation (CV)
used as a gauge
of the accuracy
of the fitted
curve parameters
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24. Product Assurance Rolled Throughput Yield (RTY)
RTY = Multiplication of the Yields at all steps in the Process
Product Assurance (PA) Process R = Reliability
Product Assurance (PA) Process S = Environmental Stress Screening
Process S
Process R
If Temp. Range = 111oC
If Mission Time = 2 Hrs
If Temp. Rate of Change = 5oC/Min
RTYPA = 98.43%
Achieved MTBF = 30000 Hrs
Performed Temp. Cycles = 16
DPMOPA = 7850
Yield = 0.99993
If Vibration Level = 2gRMS
Sigma Level = 3.92
Performed Vibration = 15 Min
Yield = 0.98432
DPMO = Defects (Failures) Per Million Opportunities
Yield = e - TDU where TDU = Total Defects (Failures) Per Unit = Outgoing Defect (Failure) Density
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25. The Challenge
DFR Physics of Failure Approach
The Physics of Failure (PoF) approach to Design for Reliability is
founded on the fact that the failure of electronics is governed by
fundamental mechanical, electrical, thermal and chemical
processes.
By understanding the possible failure mechanism, design teams
can identify and solve potential reliability problems before they
arise. The PoF process can be extremely complex, and so
requires the use of an expert system for its completion
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26. Types of Failure Mechanisms
Overstress failure
mechanisms occur when a
stress excursion exceeds
strength
Wear-out failure
mechanisms occur when
accumulated damage
exceeds endurance
Mechanical
•
Fracture
•
Buckling
•
Yielding
Mechanical
•
Fatigue
•
Creep
•
Corrosion
Electrical
•
Fused or shorted wires
•
Electrostatic discharge
•
Electrical overstress
Electrical
•
Leakage current
•
Metal migration
•
Threshold voltage shift
Thermal
•
Melting
Thermal
•
Elasticity degradation
Physical/Chemical
•
Electron-hole pairs generation
due to ionizing radiation
Physical/Chemical
•
Interdiffusion
•
Depolymerization
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