BGA封装寿命预测的有限元建模

G. Gustafsson, I. Guven, V. Kradinov, E. Madenci
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引用次数: 66

摘要

电子封装的寿命预测分析需要一系列关于有限元模型的关键假设,如切片模型和带或不带子模型的全局模型。虽然这种分析的细节在文献中是可用的,但它们之间的比较与测量的生命周期相同的电子封装不存在。本研究通过考虑具有不同几何和材料组成的两个特定包装来解决在寿命预测分析中产生的问题。通过建立以下常用的有限元模型对每个封装的焊点可靠性进行分析:(1)非线性切片模型,(2)线性超单元非线性全局模型,(3)非线性子模型线性全局模型,(4)非线性子模型非线性全局模型,(5)非线性全局模型。在带子模型的全局分析中,根据全局分析得到的位移场外推不同温度下的位移场,并将其作为边界条件以精细网格形式应用于焊点的子模型。在非线性分析中,四个热循环足以实现稳定的磁滞回线。计算了各种情况下的体积加权塑性功密度和特征寿命。基于广泛接受的达尔沃经验常数的寿命预测与实验测量进行了比较。这项研究有助于确定某些建模假设对特征寿命预测的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finite element modeling of BGA packages for life prediction
The life-prediction analysis of an electronic package requires a sequence of critical assumptions concerning the finite element models, such as the slice model and a global model with or without a submodel. Although specifics of such analyses are available in the literature, a comparison among themselves against the same electronic package with measured life cycles does not exist. This study addresses the questions arising during the life-prediction analysis by considering two particular packages with different geometric and material compositions. Solder joint reliability analyses for each of the packages are performed by constructing the following commonly accepted finite element models: (1) nonlinear slice model, (2) nonlinear global model with linear super elements, (3) linear global model with nonlinear submodel, (4) nonlinear global model with a nonlinear submodel, and (5) nonlinear global model. In the global analysis with a submodel, the displacement fields obtained from the global analysis are extrapolated for different temperatures and applied as boundary conditions in the submodel of the solder joint with a refined mesh. In the nonlinear analysis, four thermal cycles are sufficient to achieve a stable hysteresis loop. The volume-weighted plastic work density and the characteristic life are calculated in all cases. The life predictions based on widely accepted Darveaux empirical constants are compared to experimental measurements. This study helps to identify the effects of certain modeling assumptions on characteristic life predictions.
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