RELIABILITY SIMULATIONS FOR SOLDER JOINTS USING STOCHASTIC FINITE-ELEMENT AND ARTIFICIAL NEURAL-NETWORK MODELS

The field reliability of solder joints depends on the manufacturing pr ocess tolerance of design parameters and on the capability of manufact uring processes to achieve the tolerance. This process capability is u sually expressed through measures such as ''six-sigma.'' In this paper , a systematic procedure to estimate reliability of solder joints due to manufacturing process induced variations on the design is presented . The reliability is calculated using the stochastic finite element me thod and is most naturally expressed in terms of a mean life and a sta ndard deviation in life. An integrated finite element solution procedu re for predicting solder joint profile (during reflow) and life is als o presented in the paper. A physio-neural approach in which the finite element models are used to build an artificial neural network model i s next developed to combine the accuracy of the finite element models with the computational efficiency of neural networks. This physico-neu ral approach is shown to reduce the computational time required per de sign evaluation by four orders of magnitude without significant loss o f accuracy. The developed procedures are applied to the 72 I/O OMPAC B GA package from Motorola, Inc. It is shown that a +/-10 percent proces s tolerance on solder joint height, volume, and pad sizes with a ''six -sigma'' process capability on these variables will result in solder j oints with over +/-20 percent variations in life about the mean life a t +/-6 sigma level. It is also shown that variations in life of BGA so lder joints are most sensitive to variations in solder joint height. V ariations in PWB pad size, solder volume, and substrate pad size are r elatively less important, but in the order listed.