AN ANALYTICAL APPROACH FOR FATIGUE LIFE ESTIMATION OF COPPER TRACES FOR DESIGN OPTIMIZATION IN ELECTRONIC ASSEMBLIES
This dissertation investigates the durability of the copper traces using experimental results from a fully reversed four point bend test and finite element analysis. The durability data collected from the experiment was used in conjunction with the finite element based critical trace strain, to develop a set of compatible fatigue model constants that best fit the failure behavior observed in the tests. Experimental studies were also conducted in order to determine the impact of assembly variations on the trace fatigue failures including the presence of a surface finish, solder mask as well as the presence of assembled components. In order to validate the established fatigue life model constants further testing was conducted at a different load level. The model was able to predict the test out come with an error of less than 5 % Parametric studies using finite element analysis were also conducted in order to determine the relationship between the various geometric and loading conditions and the critical trace strain in the copper traces. Based on these relationships as well as the experiments to determine the impact of assembly variations of failure of the traces, an analytical model was developed in order to approximate the copper trace strain which is used as the input to the trace fatigue model. To understand the crack initiation and crack propagation process in copper traces, experiments were conducted where the crack growth was periodically monitored. Based on these experiments, the constants for the fatigue crack propagation in copper traces based on Paris’s Law were also determined in this study. Finally the analytical model for trace strain developed was also validated by comparing the copper trace strain evaluated using finite element modeling for the test vehicle used in the experiments. The strains estimated based on the analytical model match well with the strains based on the finite element modeling.