Statistics and Electronic Reliability

Printed Circuit Board Assemblies Review of Printed Circuit Board Technology 3 Basic Types of PCB-Component Assembly Technology Thru Hole (TH) using prepackaged devices Surface Mount (SMT) using prepackaged devices Micro-electronic using bare die and prepackaged devices 3 Basic Types of PCB Substrates (fabs) Rigid copper-epoxy laminate PCB (single, dual up to ~40 layers) Alumina, Alum Nitride or other ceramic materials (up to ~20 layers) Flexible Substrate (Polyimide-Copper, up to 8 layers) Surface Metalization Finishes HASL – Hot Air Solder Leveling (Eutectic SnPb Surfaces, lowest cost) ENIG – Electroless Ni – Immersion Au (flatter for wirebonding, BGA, ACF) IAg – Immersion Silver (co-deposited with organics to reduce reactivity, replacement for ENIG) Electroplated Finishes – NiAu over Cu, not always possible depending on circuit

Reliability of Electronic Circuits Causes of Electronic Systems Failure Reliability of Electronic Circuits Failures can generally be divided between intrinsic or extrinsic failures Intrinsic failures- Inherent in the component technology Electromigration (semiconductors, substrates) Contact wear (relays, connectors, etc) Contamination effects- e.g. channeling, corrosion, leakage CTE mismatch and other Interconnection joint fatigue Extrinsic failures- External stress to the components ESD or Electrostatic discharge energy Electrical overstress (over voltage, overload, overheat) Shock (Sudden Mechanical Impact) Vibration (Periodic Mechanical G force) Humidity or condensable water Package Mishandling, Bending, Shear, Tensile Many “random” and infantile failures of components are due to extrinsic failures Wearout failures are usually due to intrinsic failures

PCB Assembly Failure Mechanisms Stresses & Major Factors Thermal Excursion and Cycling Coef of Thermal Expansion (CTE) mismatches Package and Substrate Dimensions (Larger is worse) # of Interconnects on Package (More failure opportunities) Solder joint geometry including cracks, voids and skew Mechanical Shock and Vibration Mass of Components and Overall Assembly Height of Component Center of Mass (COG) Thickness, Rigidity and Support Pts of PCB

Printed Circuit Board Assemblies CTE Mismatches in PCB Assemblies Si Die CTE = 2.8e-6/C Gold CTE = 14.2e-6/C 5 – 15 μ in. NiAu Pad Thin Epoxy Solder Mask Nickel CTE = 13.4e-6/C > ~100 μ in. Copper FR4 Laminate CTE = ~20e-6/C Copper CTE = 16.5e-6/C > ~1.2 mil SnPb Eutectic Solder Joint CTE = ~25e-6/C

PCB Assembly Failure Mechanisms Stresses & Major Factors - continued Electrochemical Usage environment incl ambient temp & humidity Usage environment incl corrosive materials, salts, etc Maximum electrical field (induced by spacing, voltage on PCB) Ionic Cleanliness of PCB over/under solder mask and other coatings Cations: Including Lithium, Sodium, Ammonium, Potassium, Magnesium and Calcium. Many companies limit each individual cation contribution to be less than 0.2ug/cm2 and the combined total of all cations to be less than 0.50ug/cm2. Anions: Most Destructive Includes Fluoride, Chloride, Nitride, Bromide, Sulfate, and Phosphates. Many companies limit each individual anion contribution to be less than 0.1ug/cm2 and the combined total of all anions to be less than 0.25ug/cm2. Weak Organic Acids: May include Acetate, Formate, Succinate, Glutamate, Malate, Methane Sulfonate (MSA), Phthalate, Phosphate, Citrate and Adipic Acids. Many companies limit the combined total of all weak organic acids to be less than or equal to ~0.75ug/cm2 Ion cleanliness is tested per IPC TM-650 2.3.28 using Ion Chromatography for high reli assemblies. IPC-6012/15 mandate a total ionic cleanliness of less than 1.56ug/cm2 = 10ug/in2.

Ionic Test Methods for PCBs Resistivity of Solvent Extract (ROSE) Test Method IPC-TM-650 2.3.25 The ROSE test method is used as a process control tool (rinse) to detect the presence of bulk ionics. The IPC upper limit is set at 10.0 mg/in2 .(1.56ug/cm2) This test method provides no evidence of a correlation value with modified ROSE testing or ion chromatography. This test is performed using an ionograph or similar style ionics testing unit that detects total ionic contamination, but does not identify specific ions present. Non destructive test. Modified Resistivity of Solvent Extract (Modified ROSE) The modified ROSE test method involves a thermal extraction. The PCB is exposed in a solvent solution at a predetermined temperature for a specified time period. This process draws the ions present on the PCB into the solvent solution. The solution is tested using an ionograph-style test unit. The results are reported as bulk ions present on the PCB per square inch, similar to the standard ROSE method above. Can be destructive. Ion Chromatography IPC-TM-650 2.3.28 This test method involves a thermal extraction similar to the modified ROSE test. After thermal extraction, the solution is tested using various standards in an ion chromatographic test unit. The results indicate the individual ionic species present and the level of each ion species per unit area. This test is an excellent way to pinpoint likely process steps which are leaving residual contaminants that can lead to early reliability failures. Destructive test.

Specifying Warranty: Must Understand Reliability of Product (1, 5 yrs, etc) Life of Product should be less than wear out failure mode period Bathtub Reli Curve: (Failure Rate vs Time) Area under curve = total failures Infantile Period ~ Constant Failure Rate Minimize or Precipitate using ESS in factory Warranty Period Using ESS Warranty Period

Basic Statistics and Reliability Statistics 2 5 8 10 12 15 18 20 22 25 1.238 1.240 1.242 1.244

Std Deviation is a measure of the inherent spread in the data Basic Statistics Review Example: The following data represents the amount of time it takes 7 people to do a 355 exam problem. X = 2, 6, 5, 2 ,10, 8, 7 in min. n = 7 where X = index notation for each individual. where n = 7 people i i m = X i S 1 n where S X = Sum of the individual times where X and m = Average or Mean Calculate the mean(average): i Equation n = 7 people Mean: X = (2+6+5+2+10+8+7)/7 = 5.7 minutes s = ( X i - ) 2 S 1 n where s = s = Standard Deviation Sum or Variance Calculate the standard deviation: Equation Step 1 Step 2 (Xi - X) (Xi - X) 2-5.7= -3.7 13.69 6-5.7= .3 .09 5-5.7 = -.7 .49 2-5.7 = -3.7 13.69 10-5.7 = 4.3 18.49 8-5.7 = 2.3 5.29 7-5.7 = 1.3 1.69 S (Xi - X) = 53.43 Step 4 2 Definition: Range = Max - Min Median = Middle number when arranged low to high Mode = Most common number This Example: Range = 10 - 2 = 8 minutes Median = 6 minutes Mode = 2 minutes 53.43 Square each one Then Add All s = s = 7 - 1 Standard Deviation: s = s = 2.98 minutes 2 Step 3 Std Deviation is a measure of the inherent spread in the data

Bar Chart or Histogram Provides a visual display of data distribution 2 5 8 10 12 15 18 20 22 25 1.238 1.240 1.242 1.244 Provides a visual display of data distribution Shape of Distribution May be Key to Issues Normal (Bell Shaped) Uniform (Flat) Bimodal (Mix of 2 Normal Distributions) Skewed left or right Total number of bins is flexible but usually no more than 10 By using an infinite number of bins, resultant curve is a distribution Use T-Test to Compare Means, F-Test to Compare Variances

Normal (Gaussian) Distributions

Histogram vs Spec Limits Target Specification Limit 3s Histogram vs Spec Limits Area under curve Is probability of failure 1s Z is the number of Std Devs between the Mean and the spec limit. The higher the value of Z, the lower the chance of producing a defect  66807ppm PPM = Part per Million Defects Z = 3s Much Less Chance of Failure 1s 3.4ppm* * Assumes Z is 4.5 long term Normal Distribution Z = 6s

Area under Distribution Curve Yields Probabilities 34% 34% 2% 2% 14% 14% -3s -2s -1s m +1s +2s +3s Characterized by Two Parameters m and s2 Normal Distribution = N( m,s2 )

Life Cycle of a Component Standard Normal Distribution Apply Transformation Standard Normal Distribution Original Distribution x-m Z= Area under Curve =1 s m-s m m+s X -1 0 1 Z Examples: Z = +1.0 is one Standard Deviations above the mean Z= -0.5 is 0.5 Standard Deviations below the mean

1 Sided Normal Distribution, Probability Table Z Z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.00 5.00e-001 4.96e-001 4.92e-001 4.88e-001 4.84e-001 4.80e-001 4.76e-001 4.72e-001 4.68e-001 4.64e-001 0.10 4.60e-001 4.56e-001 4.52e-001 4.48e-001 4.44e-001 4.40e-001 4.36e-001 4.33e-001 4.29e-001 4.25e-001 0.20 4.21e-001 4.17e-001 4.13e-001 4.09e-001 4.05e-001 4.01e-001 3.97e-001 3.94e-001 3.90e-001 3.86e-001 0.30 3.82e-001 3.78e-001 3.74e-001 3.71e-001 3.67e-001 3.63e-001 3.59e-001 3.56e-001 3.52e-001 3.48e-001 0.40 3.45e-001 3.41e-001 3.37e-001 3.34e-001 3.30e-001 3.26e-001 3.23e-001 3.19e-001 3.16e-001 3.12e-001 0.50 3.09e-001 3.05e-001 3.02e-001 2.98e-001 2.95e-001 2.91e-001 2.88e-001 2.84e-001 2.81e-001 2.78e-001 0.60 2.74e-001 2.71e-001 2.68e-001 2.64e-001 2.61e-001 2.58e-001 2.55e-001 2.51e-001 2.48e-001 2.45e-001 0.70 2.42e-001 2.39e-001 2.36e-001 2.33e-001 2.30e-001 2.27e-001 2.24e-001 2.21e-001 2.18e-001 2.15e-001 0.80 2.12e-001 2.09e-001 2.06e-001 2.03e-001 2.00e-001 1.98e-001 1.95e-001 1.92e-001 1.89e-001 1.87e-001 0.90 1.84e-001 1.81e-001 1.79e-001 1.76e-001 1.74e-001 1.71e-001 1.69e-001 1.66e-001 1.64e-001 1.61e-001 1.00 1.59e-001 1.56e-001 1.54e-001 1.52e-001 1.49e-001 1.47e-001 1.45e-001 1.42e-001 1.40e-001 1.38e-001 1.10 1.36e-001 1.33e-001 1.31e-001 1.29e-001 1.27e-001 1.25e-001 1.23e-001 1.21e-001 1.19e-001 1.17e-001 1.20 1.15e-001 1.13e-001 1.11e-001 1.09e-001 1.07e-001 1.06e-001 1.04e-001 1.02e-001 1.00e-001 9.85e-002 1.30 9.68e-002 9.51e-002 9.34e-002 9.18e-002 9.01e-002 8.85e-002 8.69e-002 8.53e-002 8.38e-002 8.23e-002 1.40 8.08e-002 7.93e-002 7.78e-002 7.64e-002 7.49e-002 7.35e-002 7.21e-002 7.08e-002 6.94e-002 6.81e-002 1.50 6.68e-002 6.55e-002 6.43e-002 6.30e-002 6.18e-002 6.06e-002 5.94e-002 5.82e-002 5.71e-002 5.59e-002 1.60 5.48e-002 5.37e-002 5.26e-002 5.16e-002 5.05e-002 4.95e-002 4.85e-002 4.75e-002 4.65e-002 4.55e-002 1.70 4.46e-002 4.36e-002 4.27e-002 4.18e-002 4.09e-002 4.01e-002 3.92e-002 3.84e-002 3.75e-002 3.67e-002 1.80 3.59e-002 3.51e-002 3.44e-002 3.36e-002 3.29e-002 3.22e-002 3.14e-002 3.07e-002 3.01e-002 2.94e-002 1.90 2.87e-002 2.81e-002 2.74e-002 2.68e-002 2.62e-002 2.56e-002 2.50e-002 2.44e-002 2.39e-002 2.33e-002 2.00 2.28e-002 2.22e-002 2.17e-002 2.12e-002 2.07e-002 2.02e-002 1.97e-002 1.92e-002 1.88e-002 1.83e-002 2.10 1.79e-002 1.74e-002 1.70e-002 1.66e-002 1.62e-002 1.58e-002 1.54e-002 1.50e-002 1.46e-002 1.43e-002 2.20 1.39e-002 1.36e-002 1.32e-002 1.29e-002 1.25e-002 1.22e-002 1.19e-002 1.16e-002 1.13e-002 1.10e-002 2.30 1.07e-002 1.04e-002 1.02e-002 9.90e-003 9.64e-003 9.39e-003 9.14e-003 8.89e-003 8.66e-003 8.42e-003 2.40 8.20e-003 7.98e-003 7.76e-003 7.55e-003 7.34e-003 7.14e-003 6.95e-003 6.76e-003 6.57e-003 6.39e-003 2.50 6.21e-003 6.04e-003 5.87e-003 5.70e-003 5.54e-003 5.39e-003 5.23e-003 5.08e-003 4.94e-003 4.80e-003 2.60 4.66e-003 4.53e-003 4.40e-003 4.27e-003 4.15e-003 4.02e-003 3.91e-003 3.79e-003 3.68e-003 3.57e-003 2.70 3.47e-003 3.36e-003 3.26e-003 3.17e-003 3.07e-003 2.98e-003 2.89e-003 2.80e-003 2.72e-003 2.64e-003 2.80 2.56e-003 2.48e-003 2.40e-003 2.33e-003 2.26e-003 2.19e-003 2.12e-003 2.05e-003 1.99e-003 1.93e-003 2.90 1.87e-003 1.81e-003 1.75e-003 1.69e-003 1.64e-003 1.59e-003 1.54e-003 1.49e-003 1.44e-003 1.39e-003 3.00 1.35e-003 1.31e-003 1.26e-003 1.22e-003 1.18e-003 1.14e-003 1.11e-003 1.07e-003 1.04e-003 1.00e-003 3.10 9.68e-004 9.35e-004 9.04e-004 8.74e-004 8.45e-004 8.16e-004 7.89e-004 7.62e-004 7.36e-004 7.11e-004 3.20 6.87e-004 6.64e-004 6.41e-004 6.19e-004 5.98e-004 5.77e-004 5.57e-004 5.38e-004 5.19e-004 5.01e-004 3.30 4.83e-004 4.66e-004 4.50e-004 4.34e-004 4.19e-004 4.04e-004 3.90e-004 3.76e-004 3.62e-004 3.49e-004 3.40 3.37e-004 3.25e-004 3.13e-004 3.02e-004 2.91e-004 2.80e-004 2.70e-004 2.60e-004 2.51e-004 2.42e-004 3.50 2.33e-004 2.24e-004 2.16e-004 2.08e-004 2.00e-004 1.93e-004 1.85e-004 1.78e-004 1.72e-004 1.65e-004 3.60 1.59e-004 1.53e-004 1.47e-004 1.42e-004 1.36e-004 1.31e-004 1.26e-004 1.21e-004 1.17e-004 1.12e-004 3.70 1.08e-004 1.04e-004 9.96e-005 9.57e-005 9.20e-005 8.84e-005 8.50e-005 8.16e-005 7.84e-005 7.53e-005 3.80 7.23e-005 6.95e-005 6.67e-005 6.41e-005 6.15e-005 5.91e-005 5.67e-005 5.44e-005 5.22e-005 5.01e-005 3.90 4.81e-005 4.61e-005 4.43e-005 4.25e-005 4.07e-005 3.91e-005 3.75e-005 3.59e-005 3.45e-005 3.30e-005 4.00 3.17e-005 3.04e-005 2.91e-005 2.79e-005 2.67e-005 2.56e-005 2.45e-005 2.35e-005 2.25e-005 2.16e-005 4.10 2.07e-005 1.98e-005 1.89e-005 1.81e-005 1.74e-005 1.66e-005 1.59e-005 1.52e-005 1.46e-005 1.39e-005 4.20 1.33e-005 1.28e-005 1.22e-005 1.17e-005 1.12e-005 1.07e-005 1.02e-005 9.77e-006 9.34e-006 8.93e-006 4.30 8.54e-006 8.16e-006 7.80e-006 7.46e-006 7.12e-006 6.81e-006 6.50e-006 6.21e-006 5.93e-006 5.67e-006 4.40 5.41e-006 5.17e-006 4.94e-006 4.71e-006 4.50e-006 4.29e-006 4.10e-006 3.91e-006 3.73e-006 3.56e-006 4.50 3.40e-006 3.24e-006 3.09e-006 2.95e-006 2.81e-006 2.68e-006 2.56e-006 2.44e-006 2.32e-006 2.22e-006 4.60 2.11e-006 2.01e-006 1.92e-006 1.83e-006 1.74e-006 1.66e-006 1.58e-006 1.51e-006 1.43e-006 1.37e-006 4.70 1.30e-006 1.24e-006 1.18e-006 1.12e-006 1.07e-006 1.02e-006 9.68e-007 9.21e-007 8.76e-007 8.34e-007 4.80 7.93e-007 7.55e-007 7.18e-007 6.83e-007 6.49e-007 6.17e-007 5.87e-007 5.58e-007 5.30e-007 5.04e-007 4.90 4.79e-007 4.55e-007 4.33e-007 4.11e-007 3.91e-007 3.71e-007 3.52e-007 3.35e-007 3.18e-007 3.02e-007

Normal Distribution (cont.) Z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 5.00 2.87e-007 2.72e-007 2.58e-007 2.45e-007 2.33e-007 2.21e-007 2.10e-007 1.99e-007 1.89e-007 1.79e-007 5.10 1.70e-007 1.61e-007 1.53e-007 1.45e-007 1.37e-007 1.30e-007 1.23e-007 1.17e-007 1.11e-007 1.05e-007 5.20 9.96e-008 9.44e-008 8.95e-008 8.48e-008 8.03e-008 7.60e-008 7.20e-008 6.82e-008 6.46e-008 6.12e-008 5.30 5.79e-008 5.48e-008 5.19e-008 4.91e-008 4.65e-008 4.40e-008 4.16e-008 3.94e-008 3.72e-008 3.52e-008 5.40 3.33e-008 3.15e-008 2.98e-008 2.82e-008 2.66e-008 2.52e-008 2.38e-008 2.25e-008 2.13e-008 2.01e-008 5.50 1.90e-008 1.79e-008 1.69e-008 1.60e-008 1.51e-008 1.43e-008 1.35e-008 1.27e-008 1.20e-008 1.14e-008 5.60 1.07e-008 1.01e-008 9.55e-009 9.01e-009 8.50e-009 8.02e-009 7.57e-009 7.14e-009 6.73e-009 6.35e-009 5.70 5.99e-009 5.65e-009 5.33e-009 5.02e-009 4.73e-009 4.46e-009 4.21e-009 3.96e-009 3.74e-009 3.52e-009 5.80 3.32e-009 3.12e-009 2.94e-009 2.77e-009 2.61e-009 2.46e-009 2.31e-009 2.18e-009 2.05e-009 1.93e-009 5.90 1.82e-009 1.71e-009 1.61e-009 1.51e-009 1.43e-009 1.34e-009 1.26e-009 1.19e-009 1.12e-009 1.05e-009 6.00 9.87e-010 9.28e-010 8.72e-010 8.20e-010 7.71e-010 7.24e-010 6.81e-010 6.40e-010 6.01e-010 5.65e-010 6.10 5.30e-010 4.98e-010 4.68e-010 4.39e-010 4.13e-010 3.87e-010 3.64e-010 3.41e-010 3.21e-010 3.01e-010 6.20 2.82e-010 2.65e-010 2.49e-010 2.33e-010 2.19e-010 2.05e-010 1.92e-010 1.81e-010 1.69e-010 1.59e-010 6.30 1.49e-010 1.40e-010 1.31e-010 1.23e-010 1.15e-010 1.08e-010 1.01e-010 9.45e-011 8.85e-011 8.29e-011 6.40 7.77e-011 7.28e-011 6.81e-011 6.38e-011 5.97e-011 5.59e-011 5.24e-011 4.90e-011 4.59e-011 4.29e-011 6.50 4.02e-011 3.76e-011 3.52e-011 3.29e-011 3.08e-011 2.88e-011 2.69e-011 2.52e-011 2.35e-011 2.20e-011 6.60 2.06e-011 1.92e-011 1.80e-011 1.68e-011 1.57e-011 1.47e-011 1.37e-011 1.28e-011 1.19e-011 1.12e-011 6.70 1.04e-011 9.73e-012 9.09e-012 8.48e-012 7.92e-012 7.39e-012 6.90e-012 6.44e-012 6.01e-012 5.61e-012 6.80 5.23e-012 4.88e-012 4.55e-012 4.25e-012 3.96e-012 3.69e-012 3.44e-012 3.21e-012 2.99e-012 2.79e-012 6.90 2.60e-012 2.42e-012 2.26e-012 2.10e-012 1.96e-012 1.83e-012 1.70e-012 1.58e-012 1.48e-012 1.37e-012 7.00 1.28e-012 1.19e-012 1.11e-012 1.03e-012 9.61e-013 8.95e-013 8.33e-013 7.75e-013 7.21e-013 6.71e-013 7.10 6.24e-013 5.80e-013 5.40e-013 5.02e-013 4.67e-013 4.34e-013 4.03e-013 3.75e-013 3.49e-013 3.24e-013 7.20 3.01e-013 2.80e-013 2.60e-013 2.41e-013 2.24e-013 2.08e-013 1.94e-013 1.80e-013 1.67e-013 1.55e-013 7.30 1.44e-013 1.34e-013 1.24e-013 1.15e-013 1.07e-013 9.91e-014 9.20e-014 8.53e-014 7.91e-014 7.34e-014 7.40 6.81e-014 6.31e-014 5.86e-014 5.43e-014 5.03e-014 4.67e-014 4.33e-014 4.01e-014 3.72e-014 3.44e-014 7.50 3.19e-014 2.96e-014 2.74e-014 2.54e-014 2.35e-014 2.18e-014 2.02e-014 1.87e-014 1.73e-014 1.60e-014 7.60 1.48e-014 1.37e-014 1.27e-014 1.17e-014 1.09e-014 1.00e-014 9.30e-015 8.60e-015 7.95e-015 7.36e-015 7.70 6.80e-015 6.29e-015 5.82e-015 5.38e-015 4.97e-015 4.59e-015 4.25e-015 3.92e-015 3.63e-015 3.35e-015 7.80 3.10e-015 2.86e-015 2.64e-015 2.44e-015 2.25e-015 2.08e-015 1.92e-015 1.77e-015 1.64e-015 1.51e-015 7.90 1.39e-015 1.29e-015 1.19e-015 1.10e-015 1.01e-015 9.33e-016 8.60e-016 7.93e-016 7.32e-016 6.75e-016 8.00 6.22e-016 5.74e-016 5.29e-016 4.87e-016 4.49e-016 4.14e-016 3.81e-016 3.51e-016 3.24e-016 2.98e-016 8.10 2.75e-016 2.53e-016 2.33e-016 2.15e-016 1.98e-016 1.82e-016 1.68e-016 1.54e-016 1.42e-016 1.31e-016 8.20 1.20e-016 1.11e-016 1.02e-016 9.36e-017 8.61e-017 7.92e-017 7.28e-017 6.70e-017 6.16e-017 5.66e-017 8.30 5.21e-017 4.79e-017 4.40e-017 4.04e-017 3.71e-017 3.41e-017 3.14e-017 2.88e-017 2.65e-017 2.43e-017 8.40 2.23e-017 2.05e-017 1.88e-017 1.73e-017 1.59e-017 1.46e-017 1.34e-017 1.23e-017 1.13e-017 1.03e-017 8.50 9.48e-018 8.70e-018 7.98e-018 7.32e-018 6.71e-018 6.15e-018 5.64e-018 5.17e-018 4.74e-018 4.35e-018 8.60 3.99e-018 3.65e-018 3.35e-018 3.07e-018 2.81e-018 2.57e-018 2.36e-018 2.16e-018 1.98e-018 1.81e-018 8.70 1.66e-018 1.52e-018 1.39e-018 1.27e-018 1.17e-018 1.07e-018 9.76e-019 8.93e-019 8.17e-019 7.48e-019 8.80 6.84e-019 6.26e-019 5.72e-019 5.23e-019 4.79e-019 4.38e-019 4.00e-019 3.66e-019 3.34e-019 3.06e-019 8.90 2.79e-019 2.55e-019 2.33e-019 2.13e-019 1.95e-019 1.78e-019 1.62e-019 1.48e-019 1.35e-019 1.24e-019 9.00 1.13e-019 1.03e-019 9.40e-020 8.58e-020 7.83e-020 7.15e-020 6.52e-020 5.95e-020 5.43e-020 4.95e-020 9.10 4.52e-020 4.12e-020 3.76e-020 3.42e-020 3.12e-020 2.85e-020 2.59e-020 2.37e-020 2.16e-020 1.96e-020 9.20 1.79e-020 1.63e-020 1.49e-020 1.35e-020 1.23e-020 1.12e-020 1.02e-020 9.31e-021 8.47e-021 7.71e-021 9.30 7.02e-021 6.39e-021 5.82e-021 5.29e-021 4.82e-021 4.38e-021 3.99e-021 3.63e-021 3.30e-021 3.00e-021 9.40 2.73e-021 2.48e-021 2.26e-021 2.05e-021 1.86e-021 1.69e-021 1.54e-021 1.40e-021 1.27e-021 1.16e-021 9.50 1.05e-021 9.53e-022 8.66e-022 7.86e-022 7.14e-022 6.48e-022 5.89e-022 5.35e-022 4.85e-022 4.40e-022 9.60 4.00e-022 3.63e-022 3.29e-022 2.99e-022 2.71e-022 2.46e-022 2.23e-022 2.02e-022 1.83e-022 1.66e-022 9.70 1.51e-022 1.37e-022 1.24e-022 1.12e-022 1.02e-022 9.22e-023 8.36e-023 7.57e-023 6.86e-023 6.21e-023 9.80 5.63e-023 5.10e-023 4.62e-023 4.18e-023 3.79e-023 3.43e-023 3.10e-023 2.81e-023 2.54e-023 2.30e-023 9.90 2.08e-023 1.88e-023 1.70e-023 1.54e-023 1.39e-023 1.26e-023 1.14e-023 1.03e-023 9.32e-024 8.43e-024 10.00 7.62e-024 6.89e-024 6.23e-024 5.63e-024 5.08e-024 4.59e-024 4.15e-024 3.75e-024 3.39e-024 3.06e-024

• Examples of Fault/Failure Rates on The Sigma Scale PPM Defects (with ± 1.5  shift) 1,000,000 Tax Advice (phone-in) (140,000 PPM) 100,000 Restaurant Bills Doctor Prescription Writing 10,000 • Restaurant Checks Average Company Airline Baggage Handling 1,000 100 AircraftCarrier Landings 10 Best-in-Class 1 2 3 4 5 6 7 Z Domestic Airline Flight Fatality Rate Examples of Fault/Failure Rates on The Sigma Scale (0.43 PPM)

Short Term Capability Snapshots of the Product Over time, a “typical” product process may shift or drift by ~ 1.5 . . . also called “short-term capability” . . . reflects ‘within group’ variation Shift and Drift Time 1 Time 2 Time 3 Time 4 Actual Sustained Capability of the Process . . . also called “long-term capability” . . . reflects ‘total process’ variation LSL T USL Two Challenges: Center the Process and Eliminate Variation!

Statistics Example: IPC Workmanship Classes: Solder Volume, Shape, Placement Control High Reliability Electronic Products: Includes the equipment for commercial and military products where continued performance or performance on demand is critical. Equipment downtime cannot be tolerated, and functionality is required for such applications as life support or missile systems. Printed board assemblies in this class are suitable for applications where high levels of assurance are required and service is essential. Requirement for Aero-Space, Certain Military, Certain Medical Dedicated Service Electronic Products: Includes communications equipment, sophisticated business machines, instruments and military equipment where high performance and extended life is required, and for which uninterrupted service is desired but is not critical. Typically the end-use environment would NOT cause failures. Requirement for High Eng Telecom, COTS Military, Medical General Electronic Products: Includes consumer products, some computer and peripherals, as well as general military hardware suitable for applications where cosmetic imperfections are not important and the major requirement is function of the completed printed board assembly. 100 % 75 % 50 % 25 % 0 % 100 % 75 % 50 % 25 % 0 % IPC-7095 BGA Std Class 1 Class 2 Class 3 Max Void Size 60% Dia 36% Area 45% Dia 20.3% Area 30% Dia 9% Area Max Void Size at Interfaces 50% Dia 25% Area 35% Dia 12.3% Area 20% Dia 4% Area Min PTH Vertical Fill: Class 2 = 75% Class 3 = 100% Ref: IPC-A-610, IPC-JSTD-001

BGA Void Size and Locations, Uniform Void Position Distribution, Varying Diameter Sampling_Grid Position Model Solder_Joint_Radius Void_Distance Void_Radius S Void_Solder Interface Distance S = Shell Potential for Early Life Failure (ELFO) if S < D/10 = (solder_joint_radius)/10 S =Shell = solder_joint_radius – (void_distance + void_radius)

CLASS 3 - BEST P(D<10) = 81.11 % CLASS 2 - BETTER CLASS 1 - GOOD Solder Joint_Radius: 0.225 mm Void_Radius: 0.135 mm Void_Area: 36% of Joint Area Failure criteria: D/10 P(D<10) = 81.11 % CLASS 2 - BETTER Solder Joint_Radius: 0.225 mm Void_Radius: 0.1013 mm Void_Area: 20% of Joint Area Failure criteria: D/10 P(D<10) = 52.21 % CLASS 3 - BEST Solder Joint_Radius: 0.225 mm Void_Radius: 0.0675 mm Void_Area: 9% of Joint Area Failure criteria: D/10 P(D<10) = 27.00 %

Class vs Shell Size Relative Probabilities ~ 2x more likely to exceed D/10 threshold with Class 2 vs Class 3 S = Shell Depth

Exponential Distributions = Reliability = Time

R(t) = e-lt, Note: l is in failures/time, and t is time Definitions l General Failure Rate Variable: Recall the Bathtub Curve- Failure Rate (l) vs. Time behavior For CONSTANT FAILURE RATES – Exponential Distribution Applies and R(t) = (Reliability at Time t) = Probability that a system will not fail for a time period “t,” assuming constant failure rate; R(t) = e-lt, Note: l is in failures/time, and t is time Note: At T=0, R(0)=1.0 (100%) l FIT = FITs = Failures per 109 hours MTBF (years) = 1x109 / (l FIT * 8766 hours /year ) l MTBF = 1/MTBF = 1/Mean time between failure in hours R(t) = e-lt, Note: lMTBF in hr-1 and t in hr

Definitions l General Failure Rate Variable: For CONSTANT FAILURE RATES – Exponential Distribution Applies and R(t) = (Reliability at Time t) = Probability that a system will not fail for a time period “t,” assuming constant failure rate; R(t) = e-lt, Note: l is in failures/time, and t is time Note: At T=0, R(0)=1.0 (100%) F(t) = (UnReliability at Time t or Failures at Time t) = Fraction of population that has failed at Time t, probability that a given system will fail for a time period “t,” assuming constant failure rate; F(t) = 1-e-lt, Note: l is in failures/time, and t is time Note: At T=0, F(0)=0.0 (0%)

Weibull or 2 Parameter Distributions For VARYING FAILURE RATES – Weibull Distribution Applies and R(t) = (Reliability at Time t) = Probability that a system will not fail for a time period “t,”; R(t) = e-(t/ h)b, Note: h is the dimensionless scale parameter (stretches) b is the shape or slope parameter (exponent) Note: At T=0, R(0)=1.0 (100%) Relationship of Weibull parameters to Failure Rate = (/h)(t/h)-1 R(t) = e-(t/ h)b F(t) = 1-e-(t/ h)b

Typical Reliability Plot using Weibull Dist = (/h)(t/h)-1 F(t) = 1-e-(t/ h)b F(t) = (1 – R(t))100% Time to Failure(s) At some time, t, 100% of the population will fail

Typical Reliability Plot assuming Weibull Dist = (/h)(t/h)-1 R(t) = e-(t/ h)b F(t) = 1-e-(t/ h)b Time to failure plot using Weibull tool Decreasing Failure Rates <1 Increasing Failure Rates >1 The Bathtub Curve Constant Failure Rates =1 Exponential Distribution Failure Rate,  Weibull slope indicates where the product may be on the bathtub curve. Early Life Wear out Useful Life Time

Reliability Prediction: Assume Constant Failure Rate b = 1.0 Basic Series Reli Method of an Electronic System: Component 1 l1 Component 2 l2 Component i li Component N lN Each component has an associated reliability l The System Reli lss is the sum of all the component l lss = S li Reli l is expressed in “FITs” failure units x FIT = x Failures/109 hours Note: 109 hours = 1 Billion Hours

Example: MTBF not so good to use for Reliability Specification An electronics assembly product has an MTBF of 20000 hours; constant failure rate What is the probability that a given unit will work continuously for one year? For this problem, we have the following facts; Reliability R(t) = e-lt l = 1/MTBF = 1/20000 hr l = 0.00005 hr-1 (Failure rate) t = 8766 hours (1 year)

Example: MTBF not so good to use for Reliability Specification An electronics assembly product has an MTBF of 20000 hours; constant failure rate What is the probability that a given unit will work continuously for one year? Reliability R(t) = e-lt l = 1/MTBF = 1/20000 hr l = 0.00005 hr-1 (Failure rate) t = 8766 hours (1 year) R(1yr) =e-(8766/20,000) = 0.65 = 65%  F(1yr) = 35% of population has failed ! In other words, the Mean Time Between failures is 20,000 hours or about 2.3 years But … 35% of the units would likely fail in the first year of operation. Remember, after 1 MTBF period R(t) = 1/e = 0.368  63.2% of population will fail!

Intro to Reliability Evaluation Basic Series Reli Method of an Electronic System: Component 1 R1 Component 2 R2 Component i Ri Component N RN Each component also has an associated reliability R The System R is the product of all the component R R = P Ri Recall, Reli R is a probability (0 to 1) expressed in percent

Reliability R Flowdown Example Power supply R = 0.94 Drive System, needs R= 0.9 at 10 years Subsystem Level System Level Motor R = 0.97 Part R=0.9999 Control Card R = 0.99 R=0.999 Component Level

Reliability Requirements Flowdown- Example Customer’s need: Meet R=90%@ 10 years Partition requirements to subsystems Based on engineering analysis, experience, vendor data, parts count, etc. Allocation: Rsystem = Rpower * Rcontroller * Rmotor Rsystem = 0.94 * 0.99 * 0.97 = 0.90 Each of the 3 subsystems should in turn be allocated to components

More Reason to use R(t) and not MTBF Example An electronics product team has a goal of warranty cost which requires that a Minimum reliability after 1 year be 99% or higher, R(1yr) >= 0.99. Assume Constant Failure Rates. What MTBF should the team work towards to meet the goal? Recall Equations: R = e -lt and MTBF = 1/l Solve for MTBF: MTBF = 1/ l = 1/ {(-1/t) * ln R }, R = 0.99, t = 8766 hrs MTBF >= 872,000 hours (99.5 yrs) ! What is your product warranty cost goal expressed as an R(t)?: Answer: What is the scrap or repair cost of a given % of failures during the warranty period? Need to know, annual production, and an assumed R(t). Good products have less than 1% annualized warranty cost as a percentage of the total contribution margin for that product.

Some Typical Stresses Environmental: Temp, Humid, Pressure, Wind, Sun, Rain Mechanical: Shock, Vibration, Rotation, Abrasion Electrical: Power Cycle, Voltage Tolerance, Load, Noise ElectroMagnetic: ESD, E-Field, B-Field, Power Loss Radiation: Xray (non-ionizing), Gamma Ray (ionizing) Biological: Mold, Algae, Bacteria, Dust Chemical: Alchohol, Ph, TSP, Ionic

Environmental Conditions Susceptible Parts and Materials Common Circuit Bd Temperature-Induced Failures Failure Category Failure Mode Root Cause Environmental Conditions Susceptible Parts and Materials High temperature degradation Strength/insulation degradation degradation Temperature + Time Plastic materials, resins Heat disintegration Chemical change Temperature Distortion Softening, melting, evaporation, sublimation Metals, plastics materials, thermal fuse Oxide film formation High temperature oxidation Contact material Broken wire Thermal diffusion Metal plating involving different metals, and contact areas Creep Fatigue, damage Metal/Plastic under mechanical stress Temperature + Stress + Time Springs, structural parts Migration Disconnection, broken wire Electro-migration Temperature + Current W, Cu, Al (especially Al wiring on IC) Low temperature brittleness Damage Chemical property of metal Low temperature Body-centered cubic crystalline (Cu, Mo, W), closed-packed cubic crystalline (Zn, Ti, Mg) & alloys Flux loose Noise, imperfect contact Flux steam adheres to cold metal surface Parts attached to printed board (e.g. switches, connectors) Thermal Cycling Change in conductor resistance PCB through holes degradation; solder cracking Thermal cycling Printed circuit board w/ solder

Intro to Reliability Estimation Each l may be impacted by other factors or stresses, p: Some commonly used factors pT = Temperature Stress Factor pV = Electrical Stress Factor pE = Environmental Factor pQ = Quality Factor Overall Component l = lB * pT * pV * pE * pQ Where lB = Base Failure Rate for Component

Reliability Prediction Methods/Standards Bellcore (TR-TSY-000332): Developed by Bell Communications Research for general use in electronics industry although geared to telecom. Highest Stress Factor is Electrical Stress Data based upon field results, lab testing, analysis, device mfg data and US Military Std 217 Stress Factors include environment, quality, electrical, thermal US Military Handbook 217F: Developed by the US Department of Defense as well as other agencies for use by electronic manufacturers supplying to the military Describes both a “parts count” method as well as a “parts stress” method Data is based upon lab testing including highly accelerated life testing (HALT) or highly accelerated stress testing (HAST) Stress factors include environment and quality

Reliability Prediction Methods/Standards HRD4 (Hdbk of Reliability Data for Comp, Issue 4): Developed by the British Telecom Materials and Components Center for use by designers and manufacturers of telecom equipment Stress factors include thermal as well as environment, quality with quality being dominant Standard describes generic failure rates based upon a 60% confidence interval around data collected via telecom equipment field performance in the UK CNET: Developed by the French National Center of Telecommunications Similar to HRD4, stress factors include thermal as well as environment and dominant quality Data is based upon field experience of French commercial and military telecom equipment

Reliability Prediction Methods/Standards Siemens AG (SN29500): Developed by Siemens for internal uniform reliability predictions Stress factors include thermal and electrical however thermal dominates Standard describes failures rates based upon applications data, lab testing as well as US Mil Std 217 Components are classified into technology groups each with tuned reliability model

Reliability Prediction Basic Series Reli Method of an Electronic System: Component 1 l1 Component 2 l2 Component i li Component N lN Above Reliability Prediction Model is flawed because; Components may not have constant reliability rates l lss = S li Component applications, stresses, etc may not be well matched by the method used to model reliability Not all component failures may lead to a system failure Example: A bypass capacitor fails as an open circuit

595 Standard Failure Rates in FIT (Data is not accurate in all cases) Component Type Method A - l Method B - l Method C - l Method D - l Method E - l BJT/FET 5.0 3.8 3.2 7.6 4.0 Switch 44.0 30.0 1.0 20.0 Metal Film Res 0.7 2.5 0.05 0.2 Carbon Res 18.2 2.7 1.1 2.6 Varistor, tc Res 6.0 10.0 Electrolytic Cap 210 22.0 120 16.0 Polyester Cap 8.5 2.0 3.0 0.5 7.0 Tantalum Cap 15.0 8.0 Ceramic Cap 0.25 1.2 Si PN, Shottkey, PIN Diode 2.4 1.6 3.6 Zener Diode 13.6 17.4 18.8 70.0 LED 9.0 280 65.0 BJT Dig IC <100 Gates 138 2.3 6.7 BJT Dig IC < 1000 Gates 150 1.5 MOS Dig IC < 1000 Gates 27.3 301 13.3 MOS Dig IC => 1000 Gates 55.0 550 2.2 31.0 EM Coil Relay 385 302 220 715 SSR, Optocoupler 105 47.0 190 12.0 BJT Linear IC < 1000 Transistors 14.0 27.0 4.3 50.0 MOS Linear IC < 1000 Transistors 19.0 54.0 Transformer < 1VA 33.0 90.0 60.0 Transformer > 1VA

595 Standard Failure Rates in FIT (Data is not accurate in all cases) Component Type Method A - l Method B - l Method C - l Method D - l Method E - l Plastic Shell Connector, Plug, Jack 100.0 55.0 150.0 120.0 105.0 Metal Shell Connector, Plug, Jack 33.0 18.0 57.0 40.0 35.0 Pb, NCd, Li, Lio, NmH Battery 7.0 1.0 50.0 8.0 22.0 Quartz Crystal Thru Hole 115.0 113.8 113.2 117.6 114.0 Quartz Crystal SMT 15.0 34.0 30.0 51.0 20.0 Quartz Oscillator Module CMOS 10.0 12.5 10.5 Diode Bridge 4.8 1.6 3.6 2.4 LED Display 19.0 280 165.0 21.0 LCD Display 119.0 215.0 380 1165.0 206.0 BJT Linear IC > 1000 Transistors 217.0 41.3 91.0 MOS Linear IC > 1000 Transistors 29.0 74.0 113.3

595 Standard Stress Factors Factor Definitions (may not represent standard models) pT = Temperature Stress Factor = e[Ta/(Tr-Ta)] – 0.4 Where Ta = Actual Max Operating Temp, Tr = Rated Max Op Temp, Tr>Ta pV = Cap/Res/Transistor Electrical Stress Factor = e[(Va)/Vr-Va]-2.0 Where Va = Actual Max Operating Voltage, Vr = Abs Max Rated Voltage, Vr>Va pE = Environmental (Overall) Factor >>> Indoor Stationary = 1.0 Indoor Mobile = 2.5 Outdoor Stationary = 3.0 Outdoor Mobile = 5.0 Automotive = 7.0 pQ = Quality Factor (Parts and Assembly) Mil Spec/Range Parts = 0.75 100 Hr Powered Burn In = 0.75 Commercial Parts Mfg Direct = 1.0 Commerical Parts Distributor = 1.25 Hand Assembly Part = 3.0

Example: Method A, 0-50C Ambient, Indoor Mobile, Distributor Components +5VDC +12VDC C1 0.1uf 50V Polyester C4 0.1uf 50V Ceramic +5VDC LED Vf=1.5V R1 2KW 1/4W Brand A Metal Film Vin BPLR OP AMP C2 0.1uf 50V Polyester 5V 1W Zener 74HCT14 R2 150W 1/4W Brand B Metal Film C3 10uf 15V Electrolytic -12VDC Part Max Tr Max Vr pT pV pE pQ C1 105C  50V  2.082  0.186  2.5  1.25 C2 C3 85C  15V  3.773  0.223 C4 125C 50V   1.548  0.151 R1 120C 20V  1.643  0.232 R2 150C 6V  1.249  0.549 Zener Diode 100C N/A  2.318 1.0 Op Amp 36V  1.0 74HCT14 7V  1.649 1.25  LED   1.25 lFITS 10.29 10.29 552.2 1.46 0.83 1.50 23.18 67.73 217.77 106.16 991.37 Fits  115.1 Yrs MTBF

Stress Factors Drive Simple: 595 Standard Deratings Resistors, Potentiometers <= 50% maximum power Caps/Res <= 60% maximum working voltage Transistors <= 50% maximum working voltage Note: Most discrete devices as well as linear IC’s have parameters which will vary with temperature which is expressed as Tc (temp coefficient). Typically a delta or percent of change per deg C from ambient.

EXAMPLE: Actual Reli Tool Input List of components, their number, MTBF Data Input Sheet for e-Reliability.com COST: $500 per report System / Equipment Name:   Assembly Name:   Quantity of this assembly:   Parts List Number:   Environment: Select One Of : GB, GF, GM, NS, NU, AIC, AIF, AUC, AUF, ARW, SF, MF, ML, or CL Parts Quality: Select Either: Mil-Spec or Commercial/Bellcore   Quantity  Description ---------- Bipolar Integrated Circuits    IC / Bipolar, Digital 1-100 Gates    IC / Bipolar, Digital 101-1000 Gates    IC / Bipolar, Digital 1001-3000 Gates    IC / Bipolar, Digital 3001-10000 Gates    IC / Bipolar, Digital 10001-30000 Gates    IC / Bipolar, Digital 30001-60000 Gates    IC / Bipolar, Linear 1-100 Transistors    IC / Bipolar, Linear 101-300 Transistors    IC / Bipolar, Linear 301-1K Transistors    IC / Bipolar, Linear 1001-10K Transistors , etc.  EXAMPLE: Actual Reli Tool Input List of components, their number, Environment conditions, components quality

Example Reliability calculation using actual MIL-HDBK-217F Failure rate of a Metal Oxide Semiconductor (MOS) can be expressed as Parameters are listed in MIL Data base. Temperature factor is modeled using Arrhenius type Eqn 595 charts are greatly simplified from actual parts count Reli

Example Reliability report --------------------------------------------------------------------------------------- | | | | | Failure Rate in | | | | | | Parts Per Million Hours | | Description/ | Specification/ | Quantity | Quality |-------------------------| | Generic Part Type | Quality Level | | Factor | | | | | | | (Pi Q) | Generic | Total | | | | | | | | |=====================|================|==========|=========|============|============| | Integrated Circuit/ | Mil-M-38510/ | 16 | 1.00 | 0.07500 | 1.20000 | | Bipolar, Digital | B | | | | | | 30001-60000 Gates | | | | | | | Integrated Circuit/ | Mil-M-38510/ | 8 | 1.00 | 0.01700 | 0.13600 | | Bipolar, Linear | B | | | | | | 101-300 Transistors | | | | | | | Diode/ | Mil-S-19500/ | 2 | 2.40 | 0.00047 | 0.00226 | | Switching | JAN | | | | | | Diode/ | Mil-S-19500/ | 4 | 2.40 | 0.00160 | 0.01536 | | Voltage Ref./Reg. | JAN | | | | | | (Avalanche & Zener) | | | | | | | Transistor/ | Mil-S-19500/ | 4 | 2.40 | 0.00007 | 0.00067 | | NPN/PNP | JAN | | | |

Reliability Prediction Drawbacks

Parts Count Method Reliability Prediction Drawbacks Prediction Methods not always effective in representing future reality of a product. Tend to be pessimistic, however they are generally inaccurate. Best utilized for design comparison and order of magnitude reliability prediction (must use same methods for comparisons) Single Stress Factors must be employed to represent a composite average or worst case of the population. Difficult to predict average stress levels, peak stress levels Methods give an overall average failure rate, one dimensional Time to failure distributions (Weibull) are two dimensional describing infantile failures as well as end of life failures Reli growth using actual stress testing is a much more effective process (however also more expensive approach) MIL-STD-217F Notice 2 was the last revision of this long used standard (Jan 1995), No further releases planned.

Reliability Growth Methods

Reliability Growth Methods: HALT HALT Strategy: Highly Accelerated Life Testing One or more stresses used at accelerated amplitudes from what the product would see during application Stress level is gradually increased until failure is detected Failure is then autopsied to fundamental root cause Corrective/Preventive action taken to remove chance of recurring failure Test is then restarted Must be prepared to destroy prototypes, spend money Failure must be detectable, identifiable Repeat

2 Types of Acceleration Time Compression or Time Acceleration Basic usage cycle is reduced by eliminating idle time and or off time. Example: Opening and Closing a car door 10,000 times in 1 day. ~10 year:1day Acceleration Stress Acceleration or Amplitude Acceleration Amplitude of Stress is increased above normal usage cycle levels Example: Thermal cycling a circuit board from –40 to 125C knowing the board will see a maximum ambient range of only 10 to 35C in its application. ~163cyles:1cycle Acceleration

Estimation of the test protocol, plan and execution time. Example of Time Accelerated Life Test (595 Team Project): “Rotating Bicycle Apparatus Project” Potential reliability stress is the periodic g-load (start-stop cycles). This causes fatigue failure mode (cracks in ceramic material, creep of plastics, adhesives, solder electrical contacts failure). Estimation of the test protocol, plan and execution time. The start-stop requirements for cycle: 10 s to accelerate from 0 to 5 rev/sec max rotational speed (60 mph) 5 s to decelerate from 5 rev/sec to 0. 35 starts-stops cycles per day One cycle time (from start to stop) is going to be: T = 10+5+5=20s, where 5 s is added as a lag time to accommodate the transition from stopping back to starting Assuming the throughput 35 start-stops/day for 365 days/year the total number of rotation cycles for 1 year is 35*365=12775 cycles /year (=12775 start-stops). Assuming 20% overhead the total number of cycles is going to be 1.2*12775=15330 cycles/year. Test time worth of 1 year of the number of cycles is going to be 15330*20/(3600*24)=3.5 days

Stress Accelerations. High Temperature. High Voltage. Thermal Cycling Stress Accelerations * High Temperature * High Voltage * Thermal Cycling * Vibration

High Temperature Acceleration Factor Modified Arrhenius Equation: Svante August Arrhenius AT = Acceleration Factor Ea = Activation Energy Depends on failure modes; incl electromigration, contamination, etc.

Examples of Arrhenius Temperature Acceleration

Voltage Stress Acceleration Factor Modified Arrhenius Equation:

Thermal Cycle Stress Accelerations Primarily used to stress CTE mismatch, accumulated fatigue damage failures Basic Coffin-Manson Equation – Temperature Cycle SnPb Eutectic Solder Joint Creep Failure Application Failure Mechanism/Material E 316 Stainless Steel 1.5 4340 Steel 1.8 Solder (97Pb/03Sn) T > 30°C 1.9 Solder (37Pb/63Sn) T < 30°C 1.2 Solder (37Pb/63Sn) T > 30°C 2.7 Solder (37Pb/03Ag & 91Sn/09Zn) 2.4 Aluminum Wire Bond 3.5 Au 4 Al fracture in wire bonds 4.0 PQFP Delamination / Bond Failure 4.2 ASTM 2024 Aluminum Alloy Copper 5.0 Au Wire Bond Heel Crack 5.1 ASTM 6061 Aluminum Alloy 6.7 Alumina Fracture 5.5 Interlayer Dielectric Cracking 4.8-6.2 Silicon Fracture Silicon Fracture (cratering) 7.1 Thin Film Cracking 8.4 AF = (DTs/ DTa)E Where; DTs = Stress Test Thermal Excursion Range oK DTa = Application Thermal Excursion Range oK E = Material Dependent Exponent E = 1.9 – 2.7 for 63/37 SnPb Eutectic Solders AF = Per Cycle Stress Test Acceleration Factor

Tmin = 10 oC = 283 oK, Tmax = 50 oC = 323 oK Example SnPb Eutectic Solder Joint Creep Failure Application, Conservative Acceleration AF = (DTs/ DTa)1.9 Application, 1 Cycle/Day; Tmin = 10 oC = 283 oK, Tmax = 50 oC = 323 oK Stress Test Design; Tmin = -40 oC = 233 oK, Tmax = 125 oC = 398 oK DTs = 165 oK, DTa = 40 oK AF = (165/ 40)1.9 = 14.8 1 Stress Cycle = 14.8 Applications Cycles If 1 Stress Cycle takes ~60 minutes (average chamber ramp rate) 1 Stress Cycle Day = 24 x 14.8 = 355.2 Application Day Cycles

Modified Coffin-Manson Equation – Temp and Temp Gradient SnPb Solder Joint Creep Failure AF = (DTs/ DTa)E (Fa/Fs)1/3 e(DTsa/100) Where; DTs = Stress Test Thermal Excursion Range oK DTa = Application Thermal Excursion Range oK E = Material Dependent Exponent (1.9 – 2.7 SnPb Solders) Ts(max) = Max Stress Temp oK Ta(max) = Max Application Temp oK DTsa = Ts(max) – Ta(max) oK Fs = Thermal Cycle Frequency of Stress Test Fa = Thermal Cycle Frequency of Application AF = Per Cycle Stress Test Acceleration Factor

Alternate Form Modified Coffin-Manson Equation (Common) Norris-Landsberg Equation for Solder Joint Creep Failure AF = (DTs/ DTa)E (Fa/Fs)1/3 e1414(1/Tamax – 1/Tsmax) Where; DTs = Stress Test Thermal Excursion Range oK DTa = Application Thermal Excursion Range oK E = Material Dependent Exponent (1.9 – 2.7 SnPb Solders) Tsmax = Max Stress Temp oK Tamax = Max Application Temp oK DTsa = Ts(max) – Ta(max) oK Fs = Thermal Cycle Frequency of Stress Test Fa = Thermal Cycle Frequency of Application AF = Per Cycle Stress Test Acceleration Factor

Modified Coffin-Manson Equation SnPb Solder Joint Creep Failure Example Application; Tmin = 10 oC = 283 oK, Tmax = 50 oC = 323 oK, DTa = 40 oK Fa = 1 cycle/day Stress Test Design; Tmin = -40 oC = 233 oK, Tmax = 125 oC = 398 oK, DTs = 165 oK Ts(max) = 398 oK, Ta(max) = 323 oK, DTsa = 75 oK Fs = 1 cycle/hr = 24 cycle/day AF = (165/40)1.9 (1/24)1/3 e(75/100) = 10.8 1 Stress Test Cycle = 10.8 Application Cycles 1 Stress Test Day = Fs X AF = 259.2 Application Cycles (Taking thermal gradient into account is more conservative)

Reliability Growth Methods: HAST HAST Strategy: Highly Accelerated Stress Testing One or more stresses used at accelerated amplitudes from what the product would see during application Stress level is constant, time to failure is primary measurement Failure may also be autopsied to fundamental root cause Corrective/Preventive action NOT necessarily taken Test is then restarted using higher or lower stress amplitude to get additional data points Used to find empirical relationship between stress level and time to failure (life) Repeat

Reliability Growth Methods: HASS HASS Strategy: Highly Accelerated Stress Screening Used in production to accelerate infantile failures and keep them from shipping to customers Must have HAST data to understand how much life is expended with stress screen One or more stresses used at slightly accelerated amplitudes from what the product would see during application Common application is powered burn-in time during which electronics are powered and thermal cycled. (Example MIL-STD-883) Assemblies tested during or after burn-in for failure inducements Repeat

Reliability Bathtub Curve LFailures/Time Time infant mortality constant failure rate wearout Infant mortality- often due to manufacturing defects ….. Can be screened out In electronics systems, prediction models assume constant failure rates (Bellcore model, MIL-HDBK-217F, others) Understanding wearout requires knowledge of the particular device failure physics - Semiconductor devices should not show wearout except at long times - Discrete devices which wearout: Relays, EL caps, fans, connectors, solder

Life Stress Models and Qualification Specify Device Storage/Shipment Profiles: Specify Device Heavy User Profiles: Number of Power Cycles Number of Thermal Cycles and Min-Max Excursion (oC) per cycle Number, Amplitude (G force) and Direction of Mechanical Shocks Amplitude (Grms), Duration (Hrs), Freq Range (Hz) and Direction (1, 2 or 3 axis) Mechanical Vibration Total airflow volume (M3) and particulates (Kg)

Appendices

More on Component Derating Intentional limiting of usage stress vs rated capability Voltage Power

Physics of Failure: Accumulated Fatigue Damage (AFD) is related to the number of stress cycles N, and mechanical stress, S, using Miner’s rule Exponent B comes from the S-N diagram. It is typically between 2 and 20 Example: Solder Joint Shear Force voids Effective cross-sectional Area: D/2 Effective cross-sectional Area: D F Applied stress: Applied stress: Let  = 10, then AFD with voids will “age” about 1000x faster than AFD with no voids Voids in solder joints

Physics of failure: Thermal Fatigue Models Coefficients for Coffin - Manson Mechanical Fatigue Model • The Coffin - Manson model is most often used to model mechanical failures caused by thermal cycling in mechanical parts or electronics. (Most electronic failures are mechanical in nature) b cycles T a N D = N cycles = number of cycles to failure at reference condition b = typical value for a given failure mechanism, a = prop constant • The values of the coefficient b for various failure mechanisms and materials (derived or taken from empirical data) Failure Mechanism/Material b 316 Stainless Steel 1.5 4340 Steel 1.8 Solder (97Pb/03Sn) T > 30°C 1.9 Solder (37Pb/63Sn) T < 30°C 1.2 Solder (37Pb/63Sn) T > 30°C 2.7 Solder (37Pb/03Ag & 91Sn/09Zn) 2.4 Aluminum Wire Bond 3.5 Au 4 Al fracture in wire bonds 4.0 PQFP Delamination / Bond Failure 4.2 ASTM 2024 Aluminum Alloy Copper 5.0 Au Wire Bond Heel Crack 5.1 ASTM 6061 Aluminum Alloy 6.7 Alumina Fracture 5.5 Interlayer Dielectric Cracking 4.8-6.2 Silicon Fracture Silicon Fracture (cratering) 7.1 Thin Film Cracking 8.4 General Failure Mechanism b Ductile Metal Fatigue 1 to 2 Commonly Used IC Metal Alloys and Intermetallics 3 to 5 Brittle Fracture 6 to 8 Reference: “EIA Engineering Bulletin: Acceleration Factors”, SSB 1.003, Electronics Industries Alliance, Government Electronics and Information Technology Association - Engineering Department, 1999.

Normal operating conditions cycling 15C to 60C (T=45C) Plan for N Stress (Accelerated) cycles –40 to 125 C (T=165C) Find Mean life at stress level MTTF=4570 hrs=0.5 yrs Calculated acceleration factor and MTTF (and B10) @ normal stress: AF = Nstress / Nuse = (DT/DT)b = (165/45)2.7 = 33.4 MTTF (use)=MTTF(stress)*AF = 4570*33.4 = 152638hrs = 17.4 yrs b cycles T a N D =

Reliability Distributions are non-Normal, require 2 parameters beta, b - slope/shape parameter Intro: Weibull Distribution F(t) = 1 - e - ( ) t / h b ln ln (1 / (1 – F(t))) = b ln(t) – b ln(h) F(t) = Cumulative fraction of parts that have failed at time t Y = b X + a eta, h – characteristic life or scale parameter when t = h F(t) = 63.2% Knowing the distribution Function allows to address the following problem (anticipated future failure): What is the probability, P , that the failure will occur for the period of time T if it did not occur yet for the period of time t ? (T>t) P={F(T)-F(t)}/[1-F(t)]=

Physical Significance of Weibull Parameters Cumulative Failure (%) When Weibull distribution parameters are defined, B10 and MTTF can be computed. 99 MTTF = mean time to failure (non-repairable) = h G ( 1 + 1/b ) When b = 1.0, MTTF = h When b = 0.5, MTTF = 2h Cumulative Failure (%) F(t) MTBF = mean time between failure (repairable) (MTBSC) Slope = b = total time on all systems / # of failures 10 When there is no suspension data, MTBF = MTTF 1 B10 10 100 Time to Failure (t) The slope parameter, Beta (b), indicates failure type b < 1 rate of failure is decreasing infantile (early) failure b = 1 rate of failure is constant random failure b > 1 rate of failure is increasing wear out failure

Estimating Reliability from Test Data In testing electronics assemblies or parts, there are frequently few (or no) failures How do you estimate the reliability in this case? Use the chi-squared distribution and the following equation: MTBF = 2 * Number of hours on test * Acceleration factor / c 2 In this equation, c2 is a function of two variables n, the degrees of freedom, defined as n= 2 * number of failures + 2 and F, the confidence level of the results (e.g. 90%, 95%, 99%)

Example The following test was conducted: A new design was qualified by testing 20 boards for 1000 hours The test was conducted at elevated temperatures, where the test would accelerate failures by 10X the usage rate One board failed at 500 hours, the other 19 passed for the full 1000 hours What is the MTBF of the board design at 90% confidence? Solution: First, determine n = 2 * number of failure + 2 = 4; so c2 = 7.78 (at 90% confidence) Second, determine number of hours = 19 samples * 1000 + 1 * 500 = 19, 500 hours So, the answer is: MTBF = 2 * 19500 (total hours) * 10 (acceleration factor) / 7.78 = 50, 128 hours

Pass/Fail Test Sample Sizes? The calculations are based on the Binomial Distribution and the following formula:                                          Confidence Level CL =                            where: n = sample size p proportion defective r number defective                     probability of k or fewer failures occurring in a test of n units Example: Suppose that 3 failed parts have been observed in the test equivalent to 1 year life, what minimum sample size is needed to be 95% confident that the product is no more than 10% defective? Inputs in the formula are: p =0.1(10%), r = 3, CL = 0.95(95%), P(r<k) = 0.05 and calculate n. The minimum sample size will be 76. Reliability test should start using just a few parts in order to get preliminary number of failed parts. Using this data a required sample size can then be estimated.

i.e. 63% of units on average will fail for 10 years MTTF~=10 years (B10=1 year) results in failure rate 1-F=1-exp(-1/10*10)=0.63, i.e. 63% of units on average will fail for 10 years MTTF= 47.5 years (B10=5 years) results in failure rate 1-F=1-exp(-1/47.5*10)=0.19, i.e. 19% of units on average will fail for 10 years. System Reliability Target Must be Allocated

Commonly Used Methods to Present and Analyze Data

Plot or Scatter Plot Used to Illustrate Correlation or Relationships Linear Correlation of Input to Output 0.2 0.4 0.6 0.8 1 1.2 1.4 0.5 1.5 Input Output mArms Vrms Used to Illustrate Correlation or Relationships

Pareto Chart Used to Illustrate Contributions of Multiple Sources Root Cause Failures Example Used to Illustrate Contributions of Multiple Sources Excellent when data is abundant

Illustrates Cause & Effect Relationship Fishbone Diagram Ambient Temp Load Res Line Voltage Effect: Temp Of Amp For example Line Frequency Volume Input Amplitude Illustrates Cause & Effect Relationship

Replacement Parts Example Year to Date Summary Replacement Parts Example