Remaining Useful Life Estimation using a combined Physics of Failure and Deep Learning-based approach on SAC305 Solder PCBs subjected to Thermo-Mechanical Vibration Loads

Abstract

This paper focuses on the real-time remaining useful life (RUL) estimation of SAC305, SAC105 and SnPb solder alloy PCBs subjected to combined temperature and vibration loads. The RUL estimation of the packages on the PCB were carried out using a combined physics of failure and deep learning approaches for different operating conditions. The test boards used in this study are of same configuration for all the three solder materials and it consists of a multilayer FR4 configuration with JEDEC standard dimensions. The failure predictions and feature vector identifications are carried out using the strain gauge signals attached at the back of the PCB. The strain signals are analyzed both in the time and frequency domain to identify the different feature vectors that can predict failure of the packages as the number of drop increases. Principal component analysis is used as the pattern recognition and data reduction technique for the time and frequency domain data of the strain signals. Frequency components including and excluding the natural frequency of the test boards were used to identify the different patterns of before and after failure strain signals. The remaining useful life estimations are very useful in improving the efficiency and proactively helps to schedule maintenance effectively. The use of deep learning helps to models complex systems with multiple parameters involving nonlinear behaviors. In this paper the feature vectors identified from different operating conditions are modelled using a combined physics of failure and deep learning-based approach to estimate the remaining useful life of the packages. The changes in the material characteristics of the solders with different operating conditions are also modelled to the Long Short-term Memory (LSTM) deep learning model with the feature vectors to predict the failure of the packages. A regression model to predict the failure is also modelled to predict the failure based on the loading and material characteristics of the solders. LSTM models for each solder materials for multiple use-cases are modeled, and combined models involving different acceleration levels are also modeled.