An Analytical Model for Developing a Canary Device to Predict Solder Joint Fatigue Failure under Thermal Cycling Conditions

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Solder joint fatigue failure is a prevalent failure mechanism for electronics subjected to thermal cycling loads. The failure is attributed to the thermo-mechanical stresses in the solder joints caused by differences in the coefficient of thermal expansion of the printed circuit board (PCB), electronic component, and solder. Physics of failure models incorporate the knowledge of a product's material properties, geometry, life-cycle loading and failure mechanisms to estimate the remaining useful life of the product. Engelmaier's model is widely used in the industry to estimate the fatigue life of electronics under thermal cycling conditions. However, for leadless electronic components, the Engelmaier strain metric does not consider the solder attachment area, the solder fillet thickness, and the thickness of the PCB.

In this research a first principles model to estimate the strain in the solder interconnects has been developed. This new model considers the solder attachment area, and the geometry and material properties of the solder, component and PCB respectively. The developed model is further calibrated based on the results of finite element analysis. The calibrated model is validated by comparing its results with results of testing of test assemblies under different thermal cycling loading conditions.

Further, the calibrated first principles model is used to design reduced solder attachment areas for electronic components so that under the same loading conditions they fail faster than components with regular solder attachment areas. Such structures are called expendable Canary devices and can be used to predict the solder joint fatigue failure of regular electronic components in the actual field conditions. The feasibility of using a leadless chip resistor with reduced solder attachment area as a canary device to predict the failure of ball grid array (BGA) component has been proven based on testing data.

Further, a methodology for the developing and implementing canary device based prognostics has been developed in this research. Practical implementation issues, including estimating the number of canary devices required, determination of appropriate prognostic distance, and failure prediction schemes that may be used in the actual field conditions have also been addressed in this research.