2016
DOI: 10.6113/jpe.2016.16.5.1843
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Numerical Prediction of Solder Fatigue Life in a High Power IGBT Module Using Ribbon Bonding

Abstract: This study focused on predicting the fatigue life of an insulated gate bipolar transistor (IGBT) power module for electric locomotives. The effects of different wiring technologies, including aluminum wires, copper wires, aluminum ribbons, and copper ribbons, on solder fatigue life were investigated to meet the high power requirement of the IGBT module. The module's temperature distribution and solder fatigue behavior were investigated through coupled electro-thermo-mechanical analysis based on the finite elem… Show more

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Cited by 17 publications
(2 citation statements)
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“…IGBT power modules' high generated power losses convert to heat and raise temperature strains, particularly the junction temperature of the module. IGBT module failures are a critical worry for the dependability of power inverters since these temperature stressors have a substantial impact on the operation of such electronic components [4][5][6]. IGBT module failure scenarios brought on by high-temperature strains include solder joint cracks, heel cracking, bonding wire liftoff, and wire burning [7,8].…”
Section: Introductionmentioning
confidence: 99%
“…IGBT power modules' high generated power losses convert to heat and raise temperature strains, particularly the junction temperature of the module. IGBT module failures are a critical worry for the dependability of power inverters since these temperature stressors have a substantial impact on the operation of such electronic components [4][5][6]. IGBT module failure scenarios brought on by high-temperature strains include solder joint cracks, heel cracking, bonding wire liftoff, and wire burning [7,8].…”
Section: Introductionmentioning
confidence: 99%
“…presented an in‐situ shorting/crossover diagnostic method to periodically determine the shorting resistance, thinning, and crossover leak rate of the membrane as the test progressed. Suh et al . put forward a novel multi‐kernel SVM based on polynomial kernel and radial basis kernel function, and the particle swarm optimization algorithm was used to search the kernel parameters, penalty factor, and weight coefficient.…”
Section: Introductionmentioning
confidence: 99%