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        <title>Frontiers in Electronics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/electronics</link>
        <description>RSS Feed for Frontiers in Electronics | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-04T06:53:24.609+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2026.1799771</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2026.1799771</link>
        <title><![CDATA[Metal penetration induced interfacial challenges and engineering strategies in OSV devices]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Dongfang Shi</author>
        <description><![CDATA[Spintronics has emerged as an important research topic in the field of information communication, and organic spin-valve (OSV) devices are fabricated for demonstration and research in this area. Metal penetration of the top ferromagnetic electrodes into organic spacer layers has long been a pervasive challenge in OSV devices. Originating from evaporative deposition and facilitated by the conventional spacer layers, such penetration can severely degrade device performance and even lead to a complete loss of magnetoresistance (MR) signals. In this review, we first summarize the characterization techniques, experimental signatures, physical origins, and effects of metal penetration. Then, we further review diverse strategies developed to suppress metal penetration along the development of spintronics, including interlayer insertion, spacer material selection, electrode preparation and transfer, spacer layer preparation, and junction-area engineering, underscoring their respective advantages and limitations in terms of robustness, reproducibility, purity, and scalability. Finally, we conclude emerging opportunities enabled by metal-organic frameworks (MOFs) as a next-generation spacer material. Owing to their intrinsic properties, MOFs can hierarchically suppress metal penetration via geometric regulation, physical robustness, and local chemical coordination, while preserving clean spin-injection interfaces. We also outlined future research directions towards scalable fabrication and practical implementation of MOF-based spacers, as such optimizations can comprehensively improve the device performance, based on the effective restraint of metal penetration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2026.1750707</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2026.1750707</link>
        <title><![CDATA[CBAM-enhanced lightweight CNN for wafer map defect classification]]></title>
        <pubdate>2026-03-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mst. Rokeya Khatun</author><author>Fahmid Al Farid</author><author>Sharith Dhar</author><author>Md. Saiful Islam</author><author>Jia Uddin</author><author>Hezerul bin Abdul Karim</author>
        <description><![CDATA[Automated interpretation of wafer maps is central to manufacturing quality monitoring. Identifying rare defects with less detailed wafer maps is a challenging task. Moreover, class imbalance, heavyweight backbones, and limited model transparency are constraints for the real-world deployment of defective wafer identification. However, a nine-class wafer-map classifier is required that maintains high accuracy under tight parameter and compute budgets and provides decision-level interpretability, despite long-tailed class distributions. To address this issue, a compact convolutional network is presented for wafer-map classification on standardized low-resolution inputs. The architecture uses two convolution–pooling stages, followed by a modified convolutional block attention module (CBAM). Channel attention is realized via a shared multilayer perceptron with batch normalization for stable reweighting, while spatial attention uses a multi-scale gate to emphasize ring-like, edge-localized, and streak patterns. A compact dense head with softmax produces nine class probabilities, with a total footprint of approximately 0.15M parameters. Class imbalance is mitigated through a training-only convolutional autoencoder that generates minority samples via latent feature variation, together with a controlled reduction in the dominant None class. Validation and test sets remain unchanged. A fixed-seed protocol ensures reproducibility, and performance is evaluated using accuracy and macro-averaged precision, recall, and F1. On a balanced benchmark derived from the WM-811K dataset, the model achieves 99.88% test accuracy with near-ceiling macro-F1 while using a small fraction of the parameters required by transfer learning and transformer baselines and consistently outperforming conventional convolutional neural network (CNN) backbones. Post-training interpretability analyses with Grad-CAM, integrated gradients (IG), and occlusion show alignment between salient regions and physically meaningful defect morphology. Ablation studies indicate complementary gains from latent feature augmentation and attention mechanisms, while robustness checks with input noise and reduced training support show graceful degradation. The resulting pipeline is accurate, lightweight, and transparent, making it suitable for inline screening scenarios.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2026.1743265</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2026.1743265</link>
        <title><![CDATA[Adiabatic capacitive neuron: an energy-efficient functional unit for artificial neural networks]]></title>
        <pubdate>2026-02-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sachin Maheshwari</author><author>Mike Smart</author><author>Himadri Singh Raghav</author><author>Themis Prodromakis</author><author>Alexander Serb</author>
        <description><![CDATA[This paper presents a highly energy-efficient adiabatic capacitive neuron (ACN) hardware implementation of an artificial neuron (AN), with improved energy efficiency, robustness, and scalability over previous work. A single-neuron ACN with 12 one-bit capacitive synapses is implemented in 0.18 μm CMOS technology, supporting both positive and negative synaptic weights. A novel threshold logic (TL) circuit is introduced to realize the binary AN activation function, explicitly designed to minimize input-referred offset and ensure robust decision making under dynamic adiabatic operation. The TL performance is evaluated across three process corners and five temperatures ranging from –55 °C to 125 °C. Post-layout simulations show that the proposed TL achieves a maximum rising and falling offset voltage of 9 mV, compared to 27 mV (rising) and 5 mV (falling) for a conventional TL implementation across process and temperature variations. The proposed ACN achieves over 90% total synapse energy savings (over 12× improvement) relative to an equivalent non-adiabatic CMOS capacitive neuron (CCN) over operating frequencies from 500 kHz to 100 MHz. A 1000-sample Monte Carlo analysis incorporating process variation and mismatch confirms consistent energy savings exceeding 90% in the synapse energy profile. Supply voltage scaling further demonstrates sustained energy savings above 90%, except for the all-zero input condition, without loss of functionality. These results demonstrate that adiabatic charge recovery, combined with a robust low-offset threshold logic design, enables substantial energy reduction while maintaining reliable neuron operation across wide operating conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2026.1773991</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2026.1773991</link>
        <title><![CDATA[A power coordinated control strategy for an electrically–hydrogen coupled DC microgrid based on fuzzy control and variable-parameter droop]]></title>
        <pubdate>2026-02-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yan Wang</author>
        <description><![CDATA[IntroductionPhotovoltaic hydrogen production is a promising approach to improving renewable energy utilization and reducing grid impact. However, integrating hydrogen energy storage into DC microgrids presents significant challenges: pronounced power fluctuations from photovoltaic sources and loads, large variations in hydrogen storage state of hydrogen (SoH), and frequent start–stop cycling of hydrogen equipment triggered by SoH limit violations.MethodsTo address these issues, this paper proposes a comprehensive power coordinated control strategy for electrically–hydrogen coupled DC microgrids. First, a fuzzy logic algorithm is developed to optimize dynamic power allocation between hydrogen energy storage and lithium battery storage, enabling intelligent adaptation to varying operating conditions. Second, microgrid operating states are classified into normal and extreme conditions based on hydrogen SoH thresholds, providing a basis for differentiated control strategies. Third, a variable‐parameter droop control strategy for hydrogen energy storage is introduced, which dynamically regulates the hydrogen tank’s SoH and suppresses the rate of SoH movement toward overcharge and overdischarge regions through adaptive control parameters. This hierarchical framework enhances microgrid regulation capability while maintaining system stability.ResultsSimulation results obtained in MATLAB/Simulink demonstrate the effectiveness and superiority of the proposed strategy, confirming significant improvements in voltage regulation, hydrogen storage management, and equipment protection compared to conventional methods.DiscussionThe proposed strategy achieves comprehensive optimization of voltage stability, energy storage lifetime, equipment protection, and system efficiency through the synergistic integration of fuzzy power allocation and adaptive droop control, confirming its applicability to practical electrically–hydrogen coupled DC microgrid implementations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1704891</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1704891</link>
        <title><![CDATA[Efficient communication channel for smart contact lens with resonant magnetoquasistatic coupling]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sukriti Shaw</author><author>Mayukh Nath</author><author>Arunashish Datta</author><author>Shreyas Sen</author>
        <description><![CDATA[Magnetic resonant coupling is widely used for wireless power transfer in wearables but is typically employed in the strongly coupled regime, where the separation is smaller than or comparable to the device size. This work instead exploits resonant magnetoquasistatic (MQS) coupling to realize a wireless communication link between a necklace-mounted transmitter (Tx) coil and a receiver (Rx) coil embedded in a smart contact lens (SCL). A 15-cm Tx coil and an 8-mm peripheral Rx coil, operating at approximately 26.8 MHz at axial separations of ≈15 cm and lateral offsets ≥9 cm, form a weakly coupled but robust near-field channel. Finite-element simulations show only ∼10 dB path-loss variation across misalignments and a ∼5 Mbps channel capacity over 1 MHz bandwidth, sufficient for compressed 480p/15 fps video and multi-sensor telemetry. Because ocular and facial tissues have μr≈1 below 30 MHz, their presence causes negligible additional attenuation. A benchtop prototype with a 20-cm single-turn Tx coil and 1-cm four-turn Rx coil tuned near 26 MHz shows ∼60 dB of channel loss over necklace–eye distances and weak sensitivity to a tissue phantom, supporting the MQS-based analysis. Together, these results establish resonant MQS coupling as a viable high-data-rate communication backbone for future smart contact lenses.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1680502</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1680502</link>
        <title><![CDATA[Research on insulator contamination component identification based on neural network]]></title>
        <pubdate>2025-12-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yi Luo</author><author>Jin Liu</author><author>Yanyi Wang</author><author>Zijie Mei</author><author>Xuandong Liu</author>
        <description><![CDATA[Glass suspension insulators in power transmission lines are vulnerable to surface contamination over time, especially in harsh environments like metallurgical plants. Analysis of such contamination revealed significant metal deposits, primarily iron particles sized between 2 μm and 20 μm. To study the impact of this metallic contamination on flashover behavior, researchers created artificial pollution using NaCl, diatomaceous earth, and iron powder. Leakage current tests demonstrated that metal content fundamentally alters the current waveform, causing it to exhibit AC superimposed impulses. Key findings include: metal lowers the voltage threshold for impulse inception, shortens the impulse rise and fall times, and increases critical impulse parameters (frequency, maximum amplitude, and discharge magnitude) as the metal proportion rises. Furthermore, a ResNet18-SA deep learning model was developed, integrating a self-attention mechanism. This architecture demonstrates exceptional robustness in interpreting pulsed current signals while accurately classifying levels of metallic contamination, providing a reliable and automated solution for insulator condition assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1675666</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1675666</link>
        <title><![CDATA[Loss evaluation and performance modelling of power electronics for fault management and renewable energy integration]]></title>
        <pubdate>2025-11-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Stelios Ioannou</author><author>Alexis Polycarpou</author><author>Nicholas Christofides</author><author>Michael Chrysostomou</author><author>Mohamed Darwish</author><author>Christos C. Marouchos</author>
        <description><![CDATA[This work presents the performance and efficiency analysis of solid-state power electronic devices in two complementary applications: fault current limiting and renewable energy integration. A solid-state Fault Current Limiting and Interrupting Device (FCLID) based on a Switched Capacitor (SC) circuit is evaluated for its ability to perform power factor correction and voltage regulation during normal grid operation. Particular focus is given to switching losses in semiconductors, analysed using the PSIM Thermal Module. The 90° phase shift observed between current and voltage in SC circuits is contrasted with in-phase behaviour in DC-DC converters. IGBT losses are calculated and shown to closely align with simulation and literature-based estimates. The second part of the study investigates a grid-connected photovoltaic (PV) system with power smoothing capability, designed to mitigate output fluctuations due to environmental variability. A bidirectional DC-DC converter and a partially controlled lithium-ion battery are used to reduce voltage flicker and improve grid stability. PSIM simulations incorporate MPPT control, inverter modelling, and real-world component characteristics. Losses are primarily concentrated in switching transistors, diodes, and inductors. Across both systems, efficiency is critically evaluated as a primary determinant of performance and economic viability. The simulated and analytical loss results show agreement within 1%, thereby validating the modelling approach. The findings indicate that lower switching frequencies consistently yield overall system efficiencies above 96%, irrespective of whether MOSFETs or IGBTs are employed. However, the study also reveals that reverse recovery losses become negligible compared to conduction losses only at low switching frequencies (<10 kHz) and low current slew rates (di/dt < 100 A/µs). Finally, the analysis demonstrates that practical implementation factors can increase total power losses by up to 21%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1672188</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1672188</link>
        <title><![CDATA[Adaptive active frequency support strategy for receiving-end MMC-HVDC stations based on rate of change of frequency]]></title>
        <pubdate>2025-11-17T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Baoye Tian</author><author>Junjie Li</author><author>Hefeng Zhai</author><author>Ye Zhang</author>
        <description><![CDATA[As the proportion of HVDC infeed power increases in the eastern receiving-end power grid, inertia and frequency regulation capability decrease. Utilizing modular multilevel converter (MMC) stations at the receiving-end to provide active frequency support can effectively address this challenge. To this end, this paper first introduces conventional control methods for MMC-based HVDC systems participating in frequency regulation. Subsequently, it establishes a critical mapping relationship between the initial rate of change of frequency (RoCoF) and the subsequent maximum frequency deviation at the converter bus. Building upon this relationship, an adaptive strategy for dynamically adjusting the frequency regulation parameters (virtual inertia and damping coefficient) of the MMC is proposed. This strategy enables the MMC to provide prioritized inertia support during the initial inertial stage and switch to damping support during the recovery stage. Finally, simulations conducted on a modified IEEE 39 bus system validate the effectiveness of the proposed adaptive frequency regulation strategy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1656864</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1656864</link>
        <title><![CDATA[A high-precision fault diagnosis method for photovoltaic arrays considering the effect of missing data]]></title>
        <pubdate>2025-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Di Liu</author><author>Xiaojuan Zhu</author><author>Changyu Du</author>
        <description><![CDATA[With the increasing penetration of photovoltaic (PV) systems into power grids, the accurate diagnosis of PV array health has become critical for ensuring the stable operation of power systems. To address the problem of missing data collected from PV arrays and reduced diagnostic accuracy when compound faults occur, we propose a high-precision fault diagnosis model for PV arrays based on Tucker decomposition-sparrow search algorithm (SSA)-Informer-MSCNet. First, a tensor Tucker decomposition-based method is proposed to complete the missing data. Then, an informer network is employed to fully extract the global features. Next, an MSCNet model is proposed to extract multi-scale key features. The SSA is then used to optimize the model’s global parameters. We use the fault dataset to realize the missing data completion and fault diagnosis tests of PV arrays. The results show that the complementary algorithm thus designed has some accuracy. The proposed fault diagnostic model is able to achieve 98.73% and 97.46% accuracy in case of single and compound faults in PV arrays, respectively, and maintains 96.12% accuracy at 30 dB noise.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1693752</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1693752</link>
        <title><![CDATA[Correction: A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium-ion batteries]]></title>
        <pubdate>2025-10-27T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Tianren Zhao</author><author>Yanhui Zhang</author><author>Minghao Wang</author><author>Wei Feng</author><author>Shengxian Cao</author><author>Gong Wang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1697449</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1697449</link>
        <title><![CDATA[Printed RFID systems for sustainable IoT: synergistic advances in conductive inks, antenna architectures, and scalable manufacturing]]></title>
        <pubdate>2025-10-23T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Xintai Wang</author><author>Maksim Kuznetcov</author><author>Wenfeng Jiang</author><author>Zhongyu Tang</author><author>Zhangchenyu Wei</author><author>Aili Zhang</author><author>Naixu Wei</author><author>Xiaoying Li</author>
        <description><![CDATA[This review investigates the revolutionary potential of printed RFID technology in enabling next-generation IoT systems through sustainable manufacturing. The analysis systematically evaluates emerging conductive ink formulations, including metallic nanoparticles, carbon-based nanomaterials, MXenes, and hybrid composites, while assessing their performance trade-offs in electrical conductivity, environmental stability, and printing compatibility. Fundamental design strategies for high-performance antennas are examined, focusing on impedance matching optimization, radiation pattern control, and substrate-material synergy. Advances in printing methodologies such as inkjet deposition, screen printing, and direct ink writing are comparatively analyzed, with particular attention to the trade-off between performance and efficiency in high-resolution patterning versus industrial-scale production. Technical bottlenecks restricting commercial application are critically evaluated, emphasizing material property limitations and performance variations induced by the printing process. Finally, the study proposes three synergistic innovation pathways: intelligent material discovery through machine learning algorithms, multi-parameter simulation-guided antenna design, and hybrid manufacturing integrating multiple printing technologies. These integrated approaches aim to accelerate the transition from prototype development to industrial deployment of printed RFID systems. This comprehensive assessment provides actionable insights for advancing eco-friendly, mass-producible RFID solutions that meet the escalating demands of ubiquitous IoT connectivity across various smart environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1668332</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1668332</link>
        <title><![CDATA[Effective connectivity-based recognition of mental fatigue patterns using functional near-infrared spectroscopy]]></title>
        <pubdate>2025-10-15T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Neda Abdollahpour</author><author>N. Sertac Artan</author>
        <description><![CDATA[Mental Fatigue (MF) impairs cognitive performance and alters brain function, yet its underlying neurophysiological mechanisms remain insufficiently understood. While prior functional Near-Infrared Spectroscopy (fNIRS) studies have focused primarily on signal-level changes or undirected connectivity, few have explored how MF modulates causal interactions within cortical networks. In this study, we employed an Effective Connectivity (EC) framework based on generalized partial directed coherence (GPDC) to investigate directional brain dynamics during a cognitively demanding Stroop task. Using a publicly available dataset comprising continuous fNIRS recordings from 21 healthy adults, we modeled EC across six temporal segments to capture the evolving structure of brain networks. Our results revealed a transition from distributed, flexible connectivity patterns to more rigid and stereotyped configurations, particularly within prefrontal and motor regions. These findings were supported by significant changes in EC intensity in key channels over time. Together, our approach highlights the utility of directional connectivity analysis for identifying neural signatures of MF and contributes toward developing more sensitive biomarkers for real-time fatigue monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1686130</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1686130</link>
        <title><![CDATA[Beyond ideal models: non-idealities in TCAD simulations of dielectric-modulated FETs for label-free biosensing]]></title>
        <pubdate>2025-10-09T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Rupam Goswami</author><author>Vivek Menon U</author><author>Suman Kumar Mitra</author><author>Deepjyoti Deb</author><author>Prachuryya Subash Das</author><author>Hirakjyoti Choudhury</author><author>Raja Vipul Gautam</author>
        <description><![CDATA[Dielectric modulation in field-effect transistors (FETs) for label-free biosensing have been extensively explored to date, mostly due to the availability of semiconductor device technology computer-aided design (TCAD) tools. Of these works, many reports have revolved around TCAD simulations and focused on ideal or slightly deviated-from-ideal conditions, rather than on the inclusion of non-idealities to create actual biosensing test scenarios. This perspective presents a status of label-free dielectric-modulated biosensing in FETs. It highlights the five most important but rarely used or missing non-idealities in semiconductor TCAD tools, viz., multispecies representation, biomolecular kinematics, cavity profile, hybridization, and transient response. To better align TCAD frameworks with experimental studies, this article recommends adopting method-specific TCAD-integrated modeling (MSTIM) approaches.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1633951</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1633951</link>
        <title><![CDATA[Overshoot-tolerant primary frequency control of battery energy storage system for battery aging mitigation]]></title>
        <pubdate>2025-09-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tingyun Gu</author><author>Yu Wang</author><author>Yiheng Liu</author><author>Qihui Feng</author><author>Qiao Peng</author>
        <description><![CDATA[Battery energy storage systems (BESSs) are required to provide frequency support to the grid in some cases, which increases the charge-discharge cycles of battery and accelerates its aging, especially in primary frequency control (PFC). However, the conventional PFC of BESS mainly focuses on the frequency support performance without adequately considering battery health. This paper proposes an adaptive PFC of BESS for battery aging mitigation, which adopts a novel overshoot-tolerant principle to recover the state of energy (SOE) of battery. Once the frequency support demand aligns with the SOE recovery demand, the BESS responds to the frequency deviation in a reverse way. Then, the battery can be charged or discharged more vigorously, and the SOE of battery can be adequately maintained at an ideal level. A multi-objective online optimization model is proposed to update the optimal PFC coefficient, which is solved by the non-dominated sorting genetic algorithm (NSGA-II). The simulation results verify the proposed method, which can effectively recover the SOE of battery with an improved frequency support performance. Moreover, the case study results also validate that the aging of battery can be mitigated by recovering the SOE.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1648721</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1648721</link>
        <title><![CDATA[Analytical prediction of thermomechanical shear strain in solder joints with FEA validation in electronic packaging]]></title>
        <pubdate>2025-09-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Utkarsha Bhetuwal</author><author>Jiang Zhou</author><author>Xuejun Fan</author>
        <description><![CDATA[This paper presents a closed-form analytical model for predicting shear strain in chip-on-board assemblies with an array of solder balls. While the classical analytical formula estimates shear strain based on a configuration with a single solder joint at each end of the chip, it fails to account for the distributed nature of real assemblies. By applying compatibility conditions along the chip/solder ball and PCB/solder ball interfaces, and employing beam theory, the proposed model incorporates key geometric and material parameters, including chip and PCB dimensions, solder ball diameter, height, pitch, and elastic moduli, enabling accurate prediction of mechanical response under thermal loading. Results show that the classical model overestimates shear strain by more than 50 times compared to finite element analysis (FEA), whereas the proposed method yields results consistent with FEA. Hence, the proposed analytical solution presented in the paper demonstrates a significant improvement over the classical formula in prediction of shear strain. The new model reveals that in a fully populated array layout, the maximum shear strain at the outermost solder joint remains nearly constant with increasing chip size. The analysis also indicates that inner solder joints contribute minimally to mechanical support, suggesting that depopulated array designs may not compromise reliability. Additional parametric studies demonstrate that reducing the thickness or stiffness of the chip or PCB decreases overall strain levels. These findings are validated by finite element simulations. The paper concludes with a discussion of future work to address normal strain effects and inelastic behaviors in solder joints.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1645594</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1645594</link>
        <title><![CDATA[An overview of advanced instruments for magnetic characterization and measurements]]></title>
        <pubdate>2025-09-01T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Junbiao Zhao</author><author>Ligang Bai</author><author>Shen Li</author><author>Zhiqiang Cao</author><author>Yi Peng</author><author>Jinrui Bai</author><author>Xudong Cai</author><author>Xinmin Shi</author><author>Xiaoyang Lin</author><author>Guodong Wei</author><author>Xueying Zhang</author>
        <description><![CDATA[Magnetic materials play a pivotal role in emerging fields such as new energy, information technology, and biomedicine, where accurate magnetic characterization is essential for material innovation and device engineering. Notably, with the burgeoning development of nanomaterials and spintronics, the importance of magnetic characterization has grown significantly, accompanied by increasingly higher requirements for precision and multi-dimensional analysis. This paper elaborates on the working principles and structural components of static magnetic measurement techniques—including Vibrating Sample Magnetometer (VSM), Alternating Gradient Magnetometer (AGM), Magneto-Optical Kerr Effect (MOKE) Microscope, Magnetic Force Microscope (MFM) and Superconducting Quantum Interference Device (SQUID) Magnetometer, as well as dynamic magnetic measurement techniques such as Alternating Current (AC) susceptometry and Ferromagnetic Resonance (FMR). In addition, this review also introduces emerging techniques relevant to spintronics, including Magnetometer based on negatively charged nitrogen-vacancy (NV−) centers in diamond, Spin-polarized Scanning Tunneling Microscope (SP-STM), Lorentz Transmission Electron Microscope (LTEM), and Soft X-ray-based techniques, highlighting their principles and applications in quantum sensing, magnetic imaging, and element-specific spin analysis. This overview emphasizes the unique capabilities and measurement principles of each magnetic characterization instrument, providing users with practical guidance to identify the most appropriate tool based on specific research objectives, material properties, and experimental requirements, thereby improving characterization efficiency and accuracy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1654344</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1654344</link>
        <title><![CDATA[A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium-ion batteries]]></title>
        <pubdate>2025-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tianren Zhao</author><author>Yanhui Zhang</author><author>Minghao Wang</author><author>Wei Feng</author><author>Shengxian Cao</author><author>Gong Wang</author>
        <description><![CDATA[With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of battery management systems. To address the challenges posed by the high nonlinearity and long-term dependency in battery degradation modeling, this paper proposes a deep hybrid architecture that integrates Long Short-Term Memory networks with Transformer mechanisms, aiming to improve the accuracy and robustness of RUL prediction. Firstly, time-series samples are constructed from raw battery data, and physically consistent temperature-derived features—including average temperature, temperature range, and temperature fluctuation—are engineered. Data preprocessing is performed using standardization and Yeo-Johnson transformation. The model employs LSTM modules to capture local temporal patterns, while the Transformer modules extract global dependencies through multi-head self-attention mechanisms. These complementary features are fused to enable joint modeling of battery health states. The regression task is optimized using the Mean Squared Error loss function and trained with the Adam optimizer. Experimental results on the MIT battery dataset demonstrate the proposed model achieves excellent performance in a 7-step multi-point prediction task, with a Root Mean Square Error of 0.0085, Mean Absolute Percentage Error of 0.0200, and a coefficient of determination of 0.9902. Compared with alternative models such as MC-LSTM and XGBoost-LSTM, the proposed model exhibits superior accuracy and stability. Residual analysis and visualization further confirm the model’s unbiased and stable predictive capability. This study shows that the LSTM-Transformer hybrid architecture offers significant potential in modeling complex battery degradation processes and enhancing RUL prediction accuracy, providing effective technical support for the development of intelligent battery health management systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1613402</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1613402</link>
        <title><![CDATA[Exploring the performance of GaN trench CAVETs from cryogenic to elevated temperatures]]></title>
        <pubdate>2025-08-12T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>X. Wen</author><author>K. Lee</author><author>H. Kasai</author><author>M. Noshin</author><author>C. Meng</author><author>S. Chowdhury</author>
        <description><![CDATA[Fabricated GaN trench current aperture vertical electron transistors (CAVETs) were characterized across a wide temperature range for the first time, including in situ cryogenic measurements down to 10 K and ex situ thermal shock testing at elevated temperatures of 773 K and 1073 K. The device featured a highly conductive AlGaN/GaN channel regrown on p-GaN following trench etching. As the temperature decreased, the field-effect mobility in the regrown two-dimensional electron gas (2DEG) channel increased from 1886 cm2/(V∙s) at 296 K to 3577 cm2/(V∙s) at 10 K. The device maintained a stable threshold voltage (VTH). The subthreshold slope (SS) decreased from 98.32 mV/dec to 51.31 mV/dec, and the Ion/Ioff ratio increased from 3 × 109 to 9 × 1010 over the same temperature range. The specific on-state resistance (Ron,sp) decreased from 1.02 mΩ cm2 at 296 K to 0.586 mΩ cm2 at 10 K. Furthermore, 1-min thermal shock testing was conducted as a preliminary method to assess the resilience of trench CAVET at elevated temperatures. The device maintained field effect transistor (FET) functionality after exposure to 773 K, albeit with reduced current. Testing at 1073 K resulted in more significant performance degradation, including a sharp increase in Ron,sp and failure to achieve pinch-off due to a pronounced surge in gate leakage.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1651937</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1651937</link>
        <title><![CDATA[Future prospect of anisotropic 2D tin sulfide (SnS) for emerging electronic and quantum device applications]]></title>
        <pubdate>2025-07-28T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Abdus Salam Sarkar</author>
        <description><![CDATA[The family of anisotropic two-dimensional (2D) emerging materials is rapidly evolving due to their low crystal symmetry and in-plane structural anisotropy. Among these, 2D tin sulfide (SnS) has gained significant attention because of its distinctive crystalline symmetry and the resulting extraordinary anisotropic physical properties. This perspective explores recent developments in anisotropic 2D SnS. In particular, it highlights advances in isolating high-quality SnS monolayers (1L-SnS) and in applying advanced techniques for anisotropic characterization. The discussion continues with an overview of the anisotropic optical and electronic properties of SnS, followed by recent progress in emerging electronic device applications, including energy conversion and storage, neuromorphic (synaptic) systems, spintronics and quantum technologies. In addition to presenting significant research findings on SnS, this perspective outlines current limitations and discusses emerging opportunities and future prospects for its application in quantum devices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/felec.2025.1469802</guid>
        <link>https://www.frontiersin.org/articles/10.3389/felec.2025.1469802</link>
        <title><![CDATA[Quantized convolutional neural networks: a hardware perspective]]></title>
        <pubdate>2025-07-03T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Li Zhang</author><author>Olga Krestinskaya</author><author>Mohammed E. Fouda</author><author>Ahmed M. Eltawil</author><author>Khaled Nabil Salama</author>
        <description><![CDATA[With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with classic machine learning techniques. On the other hand, the rise of the Internet of Things (IoT) and edge computing set a demand on executing those complex tasks on corresponding devices. As the name suggested, deep neural networks are sophisticated models with complex structures and millions of parameters, which overwhelm the capacity of IoT and edge devices. To facilitate the deployment, quantization, as one of the most promising methods, is proposed to alleviate the challenge in terms of memory usage and computation complexity by quantizing both the parameters and data flow in the DNN model into formats with shorter bit-width. Consistently, dedicated hardware accelerators are developed to further boost the execution efficiency of DNN models. In this work, we focus on Convolutional Neural Network (CNN) as an example of DNNs and conduct a comprehensive survey on various quantization and quantized training methods. We also discuss various hardware accelerator designs for quantized CNN (QCNN). Based on the review of both algorithm and hardware design, we provide general software-hardware co-design considerations. Based on the analysis, we discuss open challenges and future research directions for both algorithms and corresponding hardware designs of quantized neural networks (QNNs).]]></description>
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