Abstract

To define the lifetime of lead-free solder joints, material properties must be measured and the appropriate model parameters identified. For SnPb the Coffin Manson approach, based on plastic strain of the joint, has long been used and is backed up with 40 years of field data. A more recent alternative lifetime model, that of Morrow, uses an energy density approach, where the energy density is that of the stress/strain cycle, typically representative of thermal cycling. This energy density can be calculated from FEA, using creep behaviour. The approach is weak however as the creep data is not fully established and there is no allowance for crack growth or microstructural changes. Further, in the absence of field data for lead-free solders, modelling must turn to the materials data. Conventional testing of the past with bulk solder specimens has given way to new approaches that typically study solder joint volumes that are representative of current circuit board technologies. A machine that can control and measure both force and displacement while simultaneously controlling the temperature is required. This would allow representative hysteresis energy density loops to be measured, and hence facilitate calibration of a lifetime model. The first stage of such an approach is described here where the stress strain cycle is calculated from a controlled force and displacement experiment of a solder specimen in shear, which is measured in real time under isothermal conditions. From cyclic fatigue tests a load drop parameter can be derived and correlated with the hysteresis loop area. Work shows crack propagation along the intermetallic interface but not in the intermetallic for all solders. Comparing SnPb and lead-free solders the damage mechanisms during a fatigue test of solder joints are different, for a majority of applications at small strains. However, SAC solders have similar fatigue properties to SnPb. Additionally a technique is presented for measuring the crack growth rate based on resistance measurements. How these data might be used in lifetime models is discussed

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