Degradation Estimation and Prediction of Electronic Packages Using Data-Driven Approach

Abstract

Recent trends in automotive electronics such as automated driving will increase the number and complexity of electronics used in safety relevant applications. Applications in logistics or ridesharing will require a specific year of service rather than the conventional mileage usage. Reliable operations of the electronic systems must be assured at all times, regardless of the usage condition. A more dynamic and on-demand way of assuring the system availability will have to be developed. This paper proposes a thermo-mechanical stress-based prognostics method as a potential solution. The goal is achieved by several novel advancements. On the experimental front, a key microelectronics package is developed to directly apply the prognostics and health management (PHM) concept using a piezoresistive silicon-based stress sensor. Additional hardware for safe and secure data transmission and data-processing is also developed, which is critically required for recording in situ and real-time data. On the data-management front, proper data-driven approaches have to be identified to handle the unique data set from the stress sensor employed in the study. The approaches effectively handle the massive amount of data that reveals the important information and automation of the prognostic process and thus to be able to detect, classify, locate and predict the failure. The statistical techniques for diagnostics and the machine learning (ML) algorithms for health assessment and prognostics are also determined to implement the approaches in a simple, fast but accurate way within the capacity of limited computing power. The proposed prognostics approach is implemented with actual microelectronics packages subjected to harsh accelerated testing conditions. The results corroborate the validity of the proposed prognostics approach.

A Hybrid Model for Prognostic and Health Management of Electronic Devices

“…In this sense, this work paved the way for the usage of PHM for die delamination. On the same research line, Prisacaru et al analyzed the delamination process in automotive electronic packages subject to delamination at the interface of the molding compound/copper pad and silicon die/die attachment [16]. In this case too, to collect sufficient run-to-failure data, an accelerated testing via thermal cycles was performed on a package monitored via stress sensors.…”

“…On the one hand, in applications with small power dissipation, just 10% delamination in the die attachment contact area can still assure good functionality in an application-specific integrated circuits. On the other hand, in an application with larger power dissipation, 20% delamination of the contact area can cause the end of life of the component [16]. To take into account this threshold, the delamination index, which we call DI PoF , is mapped to the delamination percentage as described in Equation (1) as follows:…”

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

“…ER i = RUL true (i) − RUL predicted (i) RUL true (i) (8) where x i = real power data, xi = predicted electric power data by the models, N = number of data points. ER i , shown in Equation ( 8) was additionally calculated to identify the RUL prediction error more intuitively.…”

“…Considering system health monitoring is one major foundation for PHM as it aims to achieve various contributions (anomaly detection, fault detection, and identification (FDI), degradation trends). Be as it may, there have been several FDI techniques, diagnostic and prognostic for power electronic devices with the sole aim of reducing failure by addressing the cause of faults in a bid to provide longer usage [7,8].…”