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 article proposes a thermomechanical 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 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 dataset from the stress sensor employed in this 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 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.

The piezoresistive silicon based stress sensor has the potential to be part of the Digital Twin implementation in automotive electronics. One solution to enforce reliability in digital twins is the use of Machine Learning (ML). One or more physical parameters are being monitored, while other parameters are projected with surrogate models, just like virtual sensors. Piezo-resistive stress sensors are employed to measure the internal stresses of electronic packages, an Acquisition Unit (AU) to read out sensor data and a Raspberry Pi to perform evaluation. Accelerated tests in air thermal chamber are performed to get time series data of the stress sensor signals, with which we can know better about how delamination develops inside the package. In this study stress measurements are performed in several electronic packages during the delamination. The delamination is detected by the stress sensor due to the continuous change of the stiffness and the local boundary conditions causing the stresses to change. Although, the stress change in multiple cells can give enough information if it is delaminated or not, its delamination area location is unknown. Surrogate models built upon Neural Networks (NN) and Finite Element Method (FEM) are developed to predict the out of plane stresses at the delaminated layer. FEM simulation models are calibrated with Moiré measurements and validated at the component and PCB level with stress difference measurements. Simulation delamination areas are constructed based on the Scanning Acoustic Microscope (SAM) images, and are also validated with the equivalent stress measurements. In the end the surrogate model is predicting the out of plane stress in the adhesive layer. The results show good correlation when compared to the SAM images.

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided.

Measurement uncertainties of a CMOS-based piezoresistive stress sensor are studied for low cycle thermal loading applications. After the fundamentals of the sensor are reviewed briefly, the random uncertainties associated with the data acquisition unit are evaluated first using raw current signals obtained from uniquely fabricated free-standing stress sensor chips. The free-standing sensor chips are tested further for systematic uncertainties associated with the manufacturing-induced residual stresses by subjecting them to a thermal cycle. Finally, the stress measurement accuracy of the sensor chip under an in-situ thermal loading is quantified by a numerical model verified by a sub-micron sensitivity optical technique while incorporating the quantified uncertainties.

The piezoresistive silicon based stress sensor has the potential to detect a precursor for the Prognostics and Health Management (PHM) implementation in automotive electronics. One solution to enforce reliability in automotive electronics is the use of Machine Learning (ML). One or more physical parameters are being monitored, and algorithms are used to illustrate the health state and predict remaining useful life based on the current and past health information. Piezo-resistive stress sensors are employed to measure the internal stresses of electronic packages, an Acquisition Unit (AU) to read out sensor data and a Raspberry Pi as PHM server to perform evaluation. Accelerated tests in air thermal chamber are performed to get time series data of the stress sensor signals, with which we can know better about how delamination develops inside the package. In this study stress measurements are performed in several electronic packages during the delamination. The delamination is detected by the stress sensor due to the continuous change of the stiffness and the local boundary conditions causing the stresses to change. Moreover, the stress change in multiple cells can give more information regarding the delamination such as the location and its state. Data preprocessing methods to remove outliers and filter raw measurement results, and feature extraction methods to capture only meaningful information by reducing the data are chosen and applied to raw data. A logical assumption is made regarding the data behavior and delamination state, based on data analytics and with Scanning Acoustic Microscope (SAM) images confirmed the delaminated area. FEM simulation are used to provide a qualitatively physical explanation of the stress change due to the delamination. A prognostic model using neural network is trained to estimate the degradation grade. Back Propagation Neural Networks are chosen to provide a fast and quick training for the mechanical stress data.

In the automotive industry, epoxy-based molding compounds (EMCs) are often used to protect not only single IC packages but also entire electronic control units (ECUs). The EMC undergoes thermal aging during the operation-lifecycle of its parent electronic package. The thermal aging oxidizes the EMC, which can alter its mechanical properties significantly. Understanding the oxidation phenomenon of EMC and its effect on property changes is critically required to predict the reliability of ECUs subjected to harsh environments. In this study, the oxidation phenomenon of EMC is characterized experimentally by measuring oxidation growth rate and the mechanical properties of oxidized EMC. In the first task, EMC samples are subjected to three different high temperature storage (HTS) conditions - 170 °C, 200 °C and 230 °C. The thicknesses of the oxidized layers are measured as a function of storage time (from 0 to 1500 hours) using a fluorescent microscope. The oxidation growth rates at the storage temperatures are determined from the thickness measurements, and they are subsequently used to determine the activation energy of the growth rate. In the second task, thin samples (300 μm thick) are subjected to a HTS condition until they are fully oxidized. Then, critical thermo-mechanical properties of oxidized EMC, including coefficient of thermal expansion (CTE), glass transition temperature, and modulus of elasticity, are measured using digital image correlation (DIC) and dynamic mechanical analysis (DMA), respectively. The detailed procedures of the experimental characterization are presented together with the test results. Their implications on the ECU reliability is also discussed.

As autonomous driving is becoming reality, more advanced solutions to enhance reliability of safety relevant systems are demanded. One of such solutions is to consolidate prognostics and health management concepts. It can be accomplished by understanding the failure, recognizing it from the field signals, and eventually predicting it. This study focuses on the second step, namely recognizing the failure from sensor signals. The test vehicle used in the study is a Thin Quad Flat Package (TQFP) mounted on a printed circuit board (PCB). The package contains 8 piezoresistive stress sensors which replaces the functional die. The test vehicle is subjected to liquid thermal shock testing conditions, and the stress state changes inside the package are recorded by stress sensors. Each sensor contains 60 stress-measuring cells, which provides the internal stress state of the package with high resolution and accuracy. The internal stress state is used to identify the failure times and locations.

Recent advancements in automotive technologies, most notably autonomous driving, demand electronic systems much more complex than those realized in the past. The automotive industry has been forced to adopt advanced consumer electronics to satisfy the demand, and thus it becomes more challenging to assess system reliability while adopting the new technologies. The system-level reliability can be enforced by implementing a process called condition monitoring. In this paper, a piezoresistive silicon-based stress sensor is implemented to recognize in situ failure in outer molded electronic control units subjected to reliability testing conditions. The test vehicle consists of six double decawatt package power packages and three stress sensors mounted on a printed circuit board. A unique algorithm is proposed and implemented to handle the data obtained from the piezoresistive stress-sensing cells. The accuracy of measured data is examined by finite-element method, and the physical changes are validated with scanning acoustic microscope. One-class support vector machines are used to autonomously classify data based on a training set of measurements from healthy state, and the reported results confirm that robust classification is possible based on data from the silicon stress sensor.

Recent advancements in automotive technologies, most notably autonomous driving, demand electronic systemsmuch more complex than realized in the past. The automotiveindustry has been forced to adopt advanced consumerelectronics to satisfy the demand, and thus it becomes morechallenging to assess system reliability while adopting the newtechnologies. The system level reliability can be enforced byimplementing a process called condition monitoring. In thispaper, a piezoresistive silicon based stress sensor isimplemented to detect physical damages in outer moldedelectronic control units (ECU) subjected to reliability testingconditions. The test vehicle consists of six DPAK powerpackages and three stress sensors mounted on a PrintedCircuit Board (PCB). A unique algorithm is proposed andimplemented to handle the data obtained from thepiezoresistive stress sensing cells. The accuracy of measureddata is examined by Finite Element method (FEM), and thephysical changes are validated with Scanning AcousticMicroscope (SAM).

Miniaturization of electronics, reduction of time to market and new functionalities in the current context of autonomous driving, electrification and connectivity, are bringing new reliability challenges. Prognostics and Health Management (PHM) can be used effectively to address some of the key challenges, in particular new challenges associated with the transfer of consumer electronics to automotive industry. The concept of PHM is not new, but its application to electronic systems is relatively new. It is expected that the PHM demand for electronic systems would continuously increase as autonomous driving is being realized. This paper attempts to summarize the recent studies in the system-level PHM of electronic systems. Condition monitoring (CM) techniques and prognostics methods used for the PHM of electronic systems are reviewed first. Various implementation examples are followed using different system classifications. The findings from this review is expected to offer a technical summary of accomplishments and challenges during the course of applying PHM for electronic systems, and to identify future research tasks to be performed to make the PHM a more viable tool for reliability assessment of electronic systems.