This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics.,A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately.,A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature.,The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

中文翻译：

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用于预测焊点可靠性的机器学习框架

本文旨在提出一种使用大数据分析来预测焊点可靠性的机器学习框架。本研究的目的是利用大数据分析准确预测焊点的可靠性。,提出了一种利用大数据分析的机器学习框架来准确预测焊点的可靠性。,一种预测寿命的机器学习框架在这项研究中已经开发了准确的焊点。为了验证其准确性和效率，它被用于预测无铅 Sn96.5Ag3.0Cu0.5 (SAC305) 对于 OSP、ENIG 和 IAg 三种常用表面处理的长期可靠性。得到的结果表明，基于机器学习方法的故障预测比威布尔方法准确得多。此外，