Search results

The solder joint reliability of ceramic chip resistors assembled to laminate substrates has been a long time concern for systems exposed to harsh environments. In this work, the thermal cycling reliability of several 2512 chip resistor lead‐free solder joint configurations has been investigated. In an initial study, a comparison has been made between the solder joint reliabilities obtained with components fabricated with both tin‐lead and pure tin solder terminations. In the main portion of the reliability testing, two temperature ranges (−40‐125°C and −40‐150°C) and five different solder alloys have been examined. The investigated solders include the normal eutectic Sn‐Ag‐Cu (SAC) alloy recommended by earlier studies (95.5Sn‐3.8Ag‐0.7Cu), and three variations of the lead‐free ternary SAC alloy that include small quaternary additions of bismuth and indium to enhance fatigue resistance.

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.