Machine learning (ML) has become popular in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to facilitate this deployment and management process. One of the MLOps challenges is understanding how ML models reason, which is key to trust and acceptance. Here, explainable AI (XAI) can help. Better error identification and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed when model performance or explanations do not meet user expectations. In this paper, we provide a novel reference architecture to address the challenge of integrating explanations and feedback capabilities into MLOps. Our architecture is implemented in a series of industrial use cases in the project EXPLAIN. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.

Industrial assembly lines are the heartbeat of modern manufacturing, where precision and efficiency are paramount. This paper introduces a novel hybrid Explainable artificial intelligence (XAI) approach to enhance monitoring and analysis in industrial assembly. By fusing the power of vision anomaly detection models with the clarity of the gradient tree boosting algorithm, this framework not only boosts defect detection accuracy but also provides transparent, actionable insights. This synergy transforms how operators and engineers interact with AI, fostering trust and enhancing operational excellence.

A triboelectric nanogenerator (TENG) is a kinetic energy transducer with small and time-varying internal capacitance, which increases the difficulties of extracting harvested energy. In this paper, an efficient rectifier, hybridizing synchronized electric charge extraction (SECE) and bias-flipping techniques, is proposed. The two techniques alternatively operate at opposite voltage polarities of the TENG. By taking advantage of the varying capacitance, the proposed synchronized extraction and flipping (SEF) rectifier shows significantly improved energy extraction performance. The design is implemented in a 180-nm high-voltage BCD technology, and the results show a 7.4X energy extraction enhancement, 65-V voltage tolerance, and 35-nA quiescent current.

The stringent reliability requirements of electronic packages for safety-critical automotive applications have spurred developments in real-time monitoring of electronic components. A key aspect of these advancements is the availability of physical health sensing elements and failure-predicting algorithms that can be embedded within the integrated circuit. In this paper, 4-wire resistance measurement features are embedded in Quad-flat no-leads (QFN) packages to detect physical damages at the printed circuit board (PCB)-solder interconnect interface. Additionally, several Artificial Intelligent (AI) algorithms are assessed, and the most suitable one is implemented to predict the in-situ resistance changes over time under vibration loads. The time series failure forecast from this algorithm correlates well to the experimentally determined lifetime of solder joints. This method opens avenues for investigating the physics of degradation in board level reliability.

The objective of this article is to investigate the thermal-fatigue properties of a commercially available lead-free solder alloy (SnBiAgCu) under the use of different types of potting compounds. Solder alloys with lower silver content are expected to substitute the conventional solder alloys SAC305 (Sn-3.0Ag-0.5Cu). First, the tensile behavior and creep behavior of the SnBiAgCu solder alloys were studied at three temperatures (25, 75, 125). Results show that this type of solder alloys presented higher tensile strength and creep deformation endurance than conventional SAC305 solder alloys. Second, a dynamic mechanical analysis was performed to get the storage modulus and glass transition temperature of three types of potting compounds, which were used in the thermal-fatigue simulation. Third, the experimentally determined material data was used for the averaged strain energy density increment calculated by the finite element method. This simulation approach was selected as damage metrics to evaluate solder interconnect reliability under different combinations of materials. It is found that the application of potting compounds will increase strain energy density significantly when compared with the strain energy density calculated without potting compound, which means that potting compounds will deteriorate the thermal-fatigue reliability of solder interconnects. These accurate data-driven simulation models can in the future form the basis for compact digital twins for predicting useful remaining lifetime.

Building on many discoveries and inventions, electronics started affecting people’s everyday lives in a significant fashion following the invention of the first solid state transistor in late 1940s. The miniaturization paved way for the mass electronics production and later the digital revolution, the outcomes of which are visible to all members of the public today. After about a two-decade-long swing around 2000s from hardware towards software regarding what affects lives more, a point has now been reached where electronics is more important to all and its use is more ubiquitous and crucial than ever before. In most if not all of end user or industrial applications, the capability and quality of electronics hardware are the key determining factors.The European electronics components and systems (ECS) industry has traditionally had a high base line for electronics innovation. However, the industry is now compelled, partly due to competition and partly due customer demand, to manufacture even more reliable electronics products than before. Guaranteeing the reliability of electronics hardware entails the entire ECS value chain to undergo a paradigm shift to holistically address reliability as a key issue. The European ECS industry previously adopted overseas outsourcing considerably, however it is now taking steps to reshape itself into a more coherent value chain with the aim of having not only the electronics designs but also the electronics manufacturing made in Europe.H2020-ECSEL programme successfully funds highly competitive projects in the area of electronics components and systems. We present here a prologue to a similarly funded project entitled Intelligent Reliability 4.0 ("iRel40"), by providing a background to the topic of ECS, project objectives, and the methodologies and implementations we plan to undertake during the 36-month period of this ongoing project.

The ARTEMIS 1 project DEWI ("Dependable Embedded Wireless Infrastructure") focusses on the area of wireless sensor / actuator networks and wireless communication. With its four industrial domains (Aeronautics, Automotive, Rail, and Building) and 21 clearly industry-driven use cases / applications, DEWI will provide and demonstrate key solutions for wireless seamless connectivity and interoperability in smart cities and infrastructures, by considering everyday physical environments of citizens in buildings, cars, trains and airplanes. It will add clear cross-domain benefits in terms of re-usability of techno-logical building bricks and architecture, processes and methods. DEWI currently is one of the largest funded European R&D projects, comprising 58 renowned industrial and research partners from 11 European countries. (For further details see www.dewi-project.eu)