Reliability Simulations for Solder Joints Using Stochastic Finite Element and Artificial Neural Network Models

Abstract

The field reliability of solder joints depends on the manufacturing process tolerance of design parameters and on the capability of manufacturing processes to achieve the tolerance. This process capability is usually expressed through measures such as “six-sigma.” In this paper, a systematic procedure to estimate the reliability of solder joints due to manufacturing process induced variations on the design is presented. The reliability is calculated using the stochastic finite element method and is most naturally expressed in terms of a mean life and a standard deviation in life. An integrated finite element solution procedure for predicting solder joint profile (during reflow) and life is also presented in the paper. A physico-neural approach in which the finite element models are used to build an artificial neural network model is next developed to combine the accuracy of the finite element models with the computational efficiency of neural networks. This physico-neural approach is shown to reduce the computational time required per design evaluation by four orders of magnitude without significant loss of accuracy. The developed procedures are applied to the 72 I/O OMPAC BGA package from Motorola, Inc. It is shown that a ±10 percent process tolerance on solder joint height, volume and pad sizes with a “six-sigma” process capability on these variables will result in solder joint with over ±20 percent variations in life about the mean life at ±6σ level. It is also shown that variations in life of BGA solder joints are most sensitive to variations in solder joint height. Variations in PWB pad size, solder volume, and substrate pad size are relatively less important, but in the order listed.