Wearable sensors are gaining attention in human health monitoring applications, even if their usability is limited due to battery need. Flexible nanogenerators (NGs) converting biomechanical energy into electrical energy offer an interesting solution, as they can supply the sensors or extend the battery lifetime. Herein, flexible generators based on lead-free barium titanate (BaTiO₃) and a polydimethylsiloxane (PDMS) polymer have been developed. A comparative study was performed to investigate the impact of multiwalled carbon nanotubes (MWCNTs) via structural, morphological, electrical, and electromechanical measurements. This study demonstrated that MWCNTs boosts the performance of the NG at the percolation threshold. This enhancement is attributed to the enhanced conductivity that promotes charge transfer and enhanced mechanical property and piezoceramics particles distribution. The nanogenerator delivers a maximum open-circuit voltage (V_OC) up to 1.5 V and output power of 40 nW, which is two times higher than NG without MWCNTs. Additionally, the performance can be tuned by controlling the composite thickness and the applied frequency. Thicker NG shows a better performance, which enlarges their potential use for harvesting biomechanical energy efficiently up to 11.22 V under palm striking. The voltage output dependency on temperature was also investigated. The results show that the output voltage changes enormously with the temperature.

The vision of a deep learning-empowered non-destructive evaluation technique aligns perfectly with the goal of zero-defect manufacturing, enabling manufacturers to detect and repair defects actively. However, the dearth of data in manufacturing is one of the biggest obstacles to realizing an intelligent defect detection system. This work presents a framework for bridging the data gap in manufacturing using the potential of synthetic datasets generated using the finite element method-based digital twin. The non-destructive technique under consideration is pulse infrared thermography. A large number of synthetic thermographic measurements were generated using 2D axisymmetric transient thermal simulations. The representativeness of synthetic data was thoroughly investigated at various steps of the framework, and the image segmentation model was trained separately on experimental and synthetic datasets. The study results reveal that when carefully rendered, synthetic datasets represent the experimental data well. When evaluated on real-world experimental samples, the segmentation model pre-trained on synthetic datasets generalizes well to the experimental samples. Furthermore, another advantage of synthetic datasets is the ease of labelling a large amount of data. Finally, the robustness assessment of the model was done on two new datasets: one where the complete experimental setup was changed, and the other was an open-source infrared thermography dataset

Active power cycling (APC) is a standardized and well-established method for reliability assessment and product qualification in power electronics (PEs) technologies. Repetitive pulses of load current are applied to cause cyclic thermal swings in the p–n junction and in the whole semiconductor device. They induce thermo-mechanical stresses, which ultimately leads to the typical interconnect failure in the “devices under test.” However, these tests are insensitive with respect to new automotive system architectures, in which PEs devices are exposed to additional loads besides the intrinsic thermal swings. The trends in PEs toward miniaturization, higher power density, heterogeneous system integration, and the deployment of PEs in harsher environments combined with longer lifetime and higher uptime requirements strongly increase the reliability demands in general and the need for more improved reliability assessment methodologies in particular. The new testing methods shall be more comprehensive and more efficient, i.e., they shall simultaneously cover the real service conditions better and reduce testing time. One promising approach is the combination of loading factors—such as the superposition of active power cycling by passive thermal cycles (TC). Both loading factors are well known to cause most relevant failure mechanisms in PEs. In reality, the PE devices are exposed to both factors simultaneously. Hence, this load case should also be replicated in the test. The paper will report a systematic investigation of such superimposed test schemes, which cover the case of self-heating and passive heating (from neighboring elements) of the PEs devices under real service conditions. Typical discrete PEs components in TO-200 packages are selected as test vehicles as they are likewise relevant for the domains of consumer or automotive electronics. The paper details the test concept and discusses the quantitative and qualitative test results.

Heat dissipation from three-dimensional (3D) chip stacks can cause large thermal gradients due to the accumulation of dissipated heat and thermal interfaces from each integrated die. To reduce the overall thermal resistance and thereby the thermal gradients, this publication will provide an overview of several studies on the formation of sequential thermal underfills that result in percolation and quasi-areal thermal contacts between the filler particles in the composite material. The quasi-areal contacts are formed from nanoparticles self-assembled by capillary bridging, so-called necks. Thermal conductivities of up to 2.5 W/m K and 2.8 W/m K were demonstrated experimentally for the percolating and the neck-based underfills, respectively. This is a substantial improvement with respect to a state-of-the-art capillary thermal underfill (0.7 W/m K). Critical parameters in the formation of sequential thermal underfills will be discussed, such as the material choice and refinement, as well as the characteristics and limitations of the individual process steps. Guidelines are provided on dry versus wet filling of filler particles, the optimal bimodal nanosuspension formulation and matrix material feed, and the over-pressure cure to mitigate voids in the underfill during backfilling. Finally, the sequential filling process is successfully applied on microprocessor demonstrator modules, without any detectable sign of degradation after 1500 thermal cycles, as well as to a two-die chip stack. The morphology and performance of the novel underfills are further discussed, ranging from particle arrangements in the filler particle bed, to cracks formed in the necks. The thermal and mechanical performance is benchmarked with respect to the capillary thermal and mechanical underfills. Finally, the thermal improvements within a chip stack are discussed. An 8 - or 16-die chip stack can dissipate 46% and 65% more power with the optimized neck-based thermal underfill than with a state-of-the-artcapillary thermal underfill.

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.