神经模糊机器学习方法在 SAC305 焊点疲劳蠕变可靠性建模中的应用。

Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, 21163, Jordan.

Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, P.O.BOX 330127, Jordan.

Sci Rep. 2023 May 26;13(1):8585. doi: 10.1038/s41598-023-32460-4.

The accuracy of reliability models is one of the most problematic issues that must be considered for the life of electronic assemblies, particularly those used for critical applications. The reliability of electronics is limited by the fatigue life of interconnected solder materials, which is influenced by many factors. This paper provides a method to build a robust machine-learning reliability model to predict the life of solder joints in common applications. The impacts of combined fatigue and creep stresses on solder joints are also investigated in this paper. The common alloy used in solder joint fabrication is SAC305 (Sn-Ag-Cu). The test vehicle includes individual solder joints of SAC305 alloy assembled on a printed circuit board. The effects of testing temperature, stress amplitude, and creep dwell time on the life of solder joints were considered. A two-parameter Weibull distribution was utilized to analyze the fatigue life. Inelastic work and plastic strain were extracted from the stress-strain curves. Then, Artificial Neural Networks (ANNs) were used to build a machine learning model to predict characteristic life obtained from the Weibull analysis. The inelastic work and plastic stains were also considered in the ANN model. Fuzzy logic was used to combine the process parameters and fatigue properties and to construct the final life prediction model. Then a relationship equation between the comprehensive output measure obtained from the fuzzy system and the life was determined using a nonlinear optimizer. The results indicated that increasing the stress level, testing temperature, and creep dwell time decreases reliability. The case of long creep dwell time at elevated temperatures is worst in terms of impact on reliability. Finally, a single robust reliability model was computed as a function of the fatigue properties and process parameters. A significant enhancement of the prediction model was achieved compared to the stress-life equations.

可靠性模型的准确性是电子组件寿命必须考虑的最具问题性的问题之一，特别是对于那些用于关键应用的组件。电子产品的可靠性受到互连焊点材料疲劳寿命的限制，而这又受到许多因素的影响。本文提供了一种构建稳健的机器学习可靠性模型的方法，以预测常见应用中焊点的寿命。本文还研究了疲劳和蠕变应力组合对焊点的影响。在焊点制造中常用的合金是 SAC305（Sn-Ag-Cu）。测试车辆包括组装在印刷电路板上的 SAC305 合金的单个焊点。考虑了测试温度、应力幅度和蠕变停留时间对焊点寿命的影响。使用双参数 Weibull 分布来分析疲劳寿命。从应力-应变曲线上提取弹性能和塑性应变。然后，使用人工神经网络 (ANN) 构建机器学习模型来预测 Weibull 分析得到的特征寿命。弹性能和塑性应变也被考虑在 ANN 模型中。模糊逻辑用于结合工艺参数和疲劳特性，并构建最终的寿命预测模型。然后，使用非线性优化器确定从模糊系统获得的综合输出度量与寿命之间的关系方程。结果表明，增加应力水平、测试温度和蠕变停留时间会降低可靠性。在高温下长时间蠕变停留时间的情况对可靠性的影响最严重。最后，作为疲劳特性和工艺参数的函数计算了单个稳健的可靠性模型。与应力寿命方程相比，预测模型得到了显著提高。