Reliability by design : CAE techniques for electronic components and systems

목차 (Table of Contents)

• CONTENTS
• Preface = ⅹiii
• Acknowledgements = ⅹvii
• 1. Introduction = 1
• 1.1. Design optimization = 2
• 1.2. Reliability = 5
• 1.3. Reliability analysis used in the design process = 7
• 2. The usability of existing reliability prediction methods for reliability optimization = 9
• 2.1. Introduction = 9
• 2.2. Existing reliability prediction methods = 9
• 2.2.1. Statistical evaluation methods used in the development of reliability prediction models = 12
• 2.2.2. United States Department of Defence MIL-HDBK-217 = 15
• 2.2.3. British Telecom HRD-4 = 18
• 2.2.4. Other reliability models for integrated circuits = 20
• 2.2.4.1. The Philips failure rate model for integrated circuits = 20
• 2.2.4.2. Kasouf & Mercurio model for integrated circuits = 20
• 2.2.4.3. The CNET failure rate model for integrated circuits = 21
• 2.2.4.4. The extended MIL-HDBK-2l7E model for integrated circuits (Pantic model) = 22
• 2.2.5. Summary of standard failure rate handbooks = 22
• 2.3. Reliability calculations using standard reliability prediction handbooks = 22
• 2.3.1. Reliability prediction of test circuits compared to practical reliability figures = 24
• 2.3.1.1. Reliability prediction test circuits A and B = 25
• 2.3.1.2. Reliability prediction test circuit C = 27
• 2.3.2. Summary of practical reliability predictions = 29
• 2.4. Reliability optimization according to standard reliability prediction handbooks = 29
• 2.4.1. Parameters in existing reliability models usable for reliability optimization = 30
• 2.4.2. Reliability optimizations derived from standard reliability prediction models = 33
• 2.4.3. Summary of reliability optimization using existing reliability prediction models = 34
• 3. Failure prediction using stressor/susceptibility interaction = 39
• 3.1. Introduction = 39
• 3.2. Part failure mechanisms = 40
• 3.3. Stressors = 41
• 3.3.1. Stressors defined as stochastic functions = 42
• 3.3.2. Stressor probability density function : single circuit, single mode = 42
• 3.3.3. Stressor probability density function : single circuit, multiple modes = 45
• 3.3.4. Stressor probability density function : multiple circuits, multiple modes = 47
• 3.3.5. Multi-variable stressor probability density functions (practical example) = 49
• 3.3.6. Examples of practical stressors = 51
• 3.3.6.1. Electrical stressors = 51
• 3.3.6.2. Thermal stressors = 53
• 3.3.6.3. Mechanical stressors = 53
• 3.4. Susceptibility for (combinations of-) stressors = 53
• 3.4.1. One variable catastrophic susceptibility model = 54
• 3.4.2. Multi-variable catastrophic susceptibility models = 55
• 3.4.3. Gradual susceptibility models = 56
• 3.4.4. Constantly degrading susceptibility models = 58
• 3.4.5. Susceptibility models for large series components = 59
• 3.4.6. Weak sub-populations = 60
• 3.5. Failure probability and reliability = 60
• 3.5.1. Failure probability for single failure mechanisms = 61
• 3.5.2. Component failure probability for multiple failure mechanisms = 65
• 3.5.3. Components with identical constant susceptibility = 69
• 3.5.4. Components with different but constant susceptibility = 73
• 3.5.5. Weak sub-populations = 74
• 3.5.6. Degradation effects = 75
• 3.5.7. Gradual failure mechanisms, cumulative effects = 76
• 3.5.8. Combined effects = 77
• 3.6. Failure probabilities in terms of design parameters = 77
• 3.7. Summary of failure prediction = 81
• 4. Deriving susceptibility models from failure mechanisms = 83
• 4.1. Introduction = 83
• 4.2. Failure mechanisms in electronic components = 85
• 4.3. Electrical overstress failure mechanisms = 85
• 4.3.1. Thermal considerations = 85
• 4.3.2. Current breakdown (Hot-spot melting) = 89
• 4.3.3. Power breakdown (thermal cracks) = 91
• 4.3.4. High-voltage breakdown = 93
• 4.3.4.1. Impact ionization = 93
• 4.3.4.2. Avalanche and Zener breakdown = 94
• 4.3.4.3. Electron-trap ionization = 95
• 4.4. Long term failure mechanisms = 96
• 4.4.1. Corrosion = 97
• 4.4.2. Electromigration = 98
• 4.4.3. Secondary diffusion = 99
• 4.5. Additional failure mechanisms for bipolar semiconductors = 100
• 4.5.1. Pulse power effects = 100
• 4.5.2. Second breakdown = 103
• 4.5.2.1. Geometrical transistor aspects related to breakdown effects = 106
• 4.5.2.2. Forward-bias second breakdown = 109
• 4.5.2.3. Reverse-bias second breakdown = 111
• 4.5.3. Summary of the discussed failure mechanisms = 114
• 4.6. Susceptibility models for practical components = 116
• 4.6.1. Diode X (schottky diode) = 116
• 4.6.1.1. Pulse power effects = 117
• 4.6.1.2. Current breakdown = 118
• 4.6.1.3. Avalanche breakdown = 119
• 4.6.1.4. Power breakdown = 119
• 4.6.2. High voltage transistor Y = 120
• 4.6.2.1. Current breakdown = 121
• 4.6.2.2. Power breakdown & secondary diffusion = 121
• 4.6.2.3. Avalanche breakdown = 121
• 4.6.2.4. Forward bias second breakdown = 123
• 4.6.2.5. Reverse bias second breakdown = 123
• 4.6.3. Integrated circuit Z (motor driver IC) = 124
• 4.6.3.1. Power dissipation and secondary diffusion = 130
• 4.6.3.2. Current breakdown and electromigration = 130
• 4.6.3.3. Avalanche breakdown = 130
• 4.6.3.4. Flyback diodes D1 & D2, pulse power effects = 130
• 4.6.3.5. Switching transistors T1 & T2, second breakdown = 131
• 4.7. Summary of susceptibility models = 133
• 5. Stressor sets for practical circuits = 135
• 5.1. Introduction = 135
• 5.2. Acquiring stressor sets = 139
• 5.3. Deriving stressor sets from computer simulation results = 140
• 5.3.1. Requirements for simulation software = 141
• 5.3.2. Requirements on functional component models = 143
• 5.3.3. Parameters required for simulation models = 144
• 5.3.4. Requirements for component tolerance models = 145
• 5.3.5. Summary of the demands on circuit simulation for stressor/susceptibility analysis = 147
• 5.4. Deriving stressor sets from practical measurements = 148
• 5.4.1. Requirements for measurement hardware = 148
• 5.4.2. Measurement of individual stressor-sets = 150
• 5.4.3. Measurements of mean stressor-sets = 151
• 5.4.3.1. The use of pre-selected circuits = 152
• 5.4.3.2. Relating failures in feedback circuits to mean stressor-sets = 153
• 5.5. Practical stressor/susceptibility interactions = 156
• 5.5.1. Diode X, circuit A = 157
• 5.5.1.1. Individual stressor set = 159
• 5.5,1.2. Mean stressor-set = 160
• 5.5.1.3. Stressor/susceptibilily interaction = 162
• 5.5.2. Transistor Y in circuits A and B = 164
• 5.5.2.1. Individual stressor set = 165
• 5.5.2.2. Tolerance effects of transistor Y = 169
• 5.5.2.3. Tolerance influence of other components = 173
• 5.5.2.4. Relation to time-failure probability = 173
• 5.6. Summary of practical stressor／susceptibility interaction = 174
• 6. Reliability optimization using stressor／susceptibility models = 175
• 6.1. Introduction = 175
• 6.2. Deriving stressor sets from circuit simulation = 178
• 6.2.1. Worst-case analysis = 178
• 6.2.2. Parameter regionalization = 179
• 6.2.3. Monte Carlo Analysis = 181
• 6.2.4. Pass/fail diagrams = 182
• 6.3. Reliability optimization using the centre of gravity method = 185
• 6.3.1. Long-term stressor and susceptibility models in reliability optimization = 189
• 6.3.2. Suggestions for enhancement of the CoG method = 190
• 6.3.3. Tolerance models required for reliability optimization = 194
• 6.4. Practical example = 195
• 6.4.1. Parameter tolerances = 196
• 6.4.2. Optimization using the Centre of Gravity method = 197
• 7. Conclusions = 201
• 7.1. Impossibility to use existing reliability prediction methods = 201
• 7.2. New method : stressor/susceptibilityinteraction = 202
• 7.3. Practical use of stressor/susceptibility models = 203
• 7.4. Development of susceptibility models = 203
• 7.5. Stressor sets = 204
• 7.6. Reliability optimization = 204
• 7.7. Practical use of stressor/susceptibility analysis in industry = 205
• 7.8. Recommendations for further research = 206
• A. Shot explanation of the test circuits used = 207
• A.1. Introduction = 207
• A.2. Practical circuits = 207
• A.3. Reliability predictions for circuits used in consumer electronics = 208
• A.4. Circuit A = 209
• A.4.1. Functional structure circuit A = 210
• A.4.2. Components used (reliability aspects) = 213
• A.4.3. Reliability prediction using existing prediction methods = 215
• A.4.4. Practical failure data = 216
• A.5. Circuit B = 218
• A.5.1. Functional structure circuit B = 219
• A.5.2. Components used (reliability aspects) = 219
• A.5.3. Reliability prediction using existing prediction methods = 222
• A.5.4. Practical failure data = 223
• A.6. Circuit C = 224
• A.6.1. Function = 224
• A.6.1.1. The motor drive IC = 225
• A.6.1.2. Function of the motordrive IC in circuit C = 227
• A.6.2. Components used (reliability aspects) = 227
• A.6.3. Reliability prediction using existing prediction methods = 228
• A.6.3.1. The MIL-HDBK-217 motor model = 229
• A.6.3.2. Reliability figures used components = 230
• B. Failure mechanisms in simple components = 233
• B.1. Resistive components = 233
• B.1.1. General failure mechanisms = 233
• B.1.1.1. Power overstress = 233
• B.1.1.2. Pulse power effects = 234
• B.1.1.3. Effects of inhomogeneities on power dissipation = 237
• B.1.1.4. Voltage overstress = 237
• B.1.2. Practical components = 238
• B.1.2.1. Wire wound resistors = 238
• B.1.2.2. Film resistors = 239
• B.2. Capacitive components = 241
• B.2.1. General failure mechanisms = 242
• B.2.1.1, High-voltage breakdown = 242
• B.2.1.2. Effects of inhomogeneous structures on voltage breakdown = 243
• B.2.1.3. Power breakdown = 243
• B.2.2. Practical components = 243
• B.2.2.1. Ceramic/plastic capacitors = 243
• B.2.2.2. Electrolytic capacitors = 244
• B.3. Inductive components = 247
• B.3.1. Single air coils = 248
• B.3.2. Multiple air coils = 249
• B.3.3. Inductive devices using magnetic cores (coils, transformers) = 250
• B.4. Conclusions = 252
• C. Tolerance models and examples = 253
• C.1. Introduction = 253
• C.2. Example 1 : Foil transformer (circuit B) = 254
• C.3. Example 2 : The optocoupler (circuit B) = 256
• C.4. Example 3 : High voltage transistor = 260
• C.5. Conclusions = 261
• D. Bibliography = 263
• E. Terms used = 267
• E.1. General terms = 267
• E.2. Stressor/susceptibility = 267
• E.3. Traditional reliability prediction models = 268
• E.4. Electrical variables = 269
• Index = 271