Two-dimensional (2D) MXene nanomaterials exhibit considerable potential for electronic devices owing to their metal-like conductivity and abundant surface functional groups. However, utilizing the intrinsic properties of MXene in memristors remains challenging as MXene exhibits free-electron conduction behavior rather than semiconductor characteristics. In this work, a Cu/MXene/TaOx/ITO memristor was fabricated via heterostructure engineering, and its resistive switching (RS) performances were systematically compared with those of the monolayer Cu/MXene/ITO and Cu/TaOx/ITO memristors. Compared with the monolayer ones, the MXene/TaOx memristor exhibits a narrower switching voltage range, a higher on/off ratio exceeding 240, and extended resistance retention exceeding 104 s. Even after 15 months of storage, it maintains stable RS behavior with over 104 cycles of endurance. The enhanced device performance is attributed to the interaction between MXene’s surface functional groups and Cu2+ ions, coupled with the optimized interface Schottky barrier at the MXene/TaOx heterojunction. Furthermore, typical biological synaptic plasticity characteristics, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike time-dependent plasticity (STDP) were simulated with the proposed memristors. The MXene/TaOx memristor achieves excellent LTP/LTD behavior with the best nonlinearity of 0.39/3.15 and symmetricity of 6.57. By its enhanced conductance symmetry and linearity, an accuracy of up to 94.57% can be achieved in handwritten digit recognition within a constructed neuromorphic network. These results provide a feasible and effective strategy of integrating 2D materials with metal oxides to enhance memristor performance, highlighting their immense application potential in bio-inspired neuromorphic systems.

For the demand of wide range of metal pipe wall thickness measurement, this article first starts from the electromagnetic induction theory and clarifies that the amplitude of the axial component of the sum of the excitation magnetic field and the induced magnetic field has a highly linear relationship with the logarithm of the pipe wall thickness within a certain range. The theoretical mechanism for achieving wide-range adjustment of the measurement system by tuning the excitation magnetic field frequency is revealed. Furthermore, the relationship between the slope of the linear fitting function and the excitation frequency is expounded. The relationship between the excitation magnetic field frequency and the skin depth is used to match the pipe wall thickness and realize the accurate sensing of the thickness change. Then, the designed and prepared magnetoelectric (ME) sensor is used to construct a metal pipe wall thickness measurement system, and the system realizes the measurement of copper pipes with thickness ranging from 5.95 mu m to 5 mm, and the thickness measurement error is within +/- 4 %. The pipe wall thickness is determined by processing the amplitude of the ME sensor's output signal through the inversion algorithm based on the theoretical model. The proposed method has the advantages of simple principle, low cost, wide-range coverage, and noncontact measurement.

Aiming at the problem that it is difficult to achieve non-destructive and accurate evaluation of metal coating thickness, a new measurement model based on swept-frequency eddy current is proposed to realize the thickness measurement of metal coatings in the order of nanometers to millimeters. The model reveals that the total magnetic field has a linear relationship with the logarithm of coating thickness at a specific excitation frequency. At the same time, the slope of this linear relationship is also linearly correlated with the logarithm of excitation frequency at different substrate thicknesses. Consequently, the inversion process for coating thickness is simplified by circumventing the need to solve complex coupled parameters. Finally, a measurement system was constructed using a magnetoelectric (ME) sensor, achieving thickness measurement of aluminum coatings from 500 nm to 2 mm with a maximum relative error of less than 3%. This research provides a new method for the wide-range nondestructive testing of metal coatings.

Flexible electronic substrates, as the critical constituent of flexible electronic devices and systems, are essential for component interconnection and overall structural flexibility. During service, they are routinely subjected to diverse temperature-humidity environments and static bending conditions. These external factors progressively induce material ageing and deterioration of electrical interconnect performance. The actual occurrence of ageing in flexible electronic substrates was experimentally confirmed by subjecting them to accelerated ageing tests. Electrical resistance was employed as the ageing indicator. The influence mechanisms of temperature-humidity conditions, bending radius, and specimen specifications on the ageing process were systematically quantified. A physics-based characterization model for the ageing effect was subsequently derived. A hybrid physics-informed neural network (PINN) was then constructed and trained to predict resistance evolution. The model achieved a mean relative error of 0.02% against measured data, demonstrating its validity and high accuracy. This work provides a direct quantitative basis for reliability assessment and performance-degradation prediction of flexible-substrate-based electronic devices. It thus potentially offers valuable support for ensuring and enhancing product quality and operational lifetime.

Voltage-programmable transistor and diode operations are demonstrated within a single skyrmionic device structure via strain-mediated magnetoelectric coupling. On the basis of electromechanical and micromagnetic simulations, the spatial strain distributions are precisely tuned through gate voltages to achieve directional control and cutoff states of skyrmion motion. This mechanism allows both the operational modes and functional directionality to be programmed solely through electrical stimuli. The proposed approach eliminates Joule heating and achieves subfemtojoule energy consumption, effectively reducing device complexity by integrating multiple electronic functions into a single unit. These results highlight a promising route toward the development of ultralow-power, reconfigurable logic, and neuromorphic computing systems.

This paper proposes a compact axial-ratio-enhanced wideband circularly polarized rectenna for ambient RF energy harvesting. The proposed rectenna is designed to operate across the mainstream Wi-Fi (2.45 GHz) and 5G (2.6 GHz and 3.5 GHz) communication bands, achieving efficient RF energy capture and effective direct current (DC) conversion. From a design perspective, the proposed approach is developed based on parasitic-element-enabled current redistribution for broadband circular polarization and nonlinear-aware multi-stage impedance matching for wideband rectification. The receiving antenna is based on a crossed-dipole configuration integrated with quarter-ring elements. By employing techniques such as slotting and incorporating additional parasitic patches, a fractional 3-dB axial ratio bandwidth (ARBW) of 52.7% (2.39-4.10 GHz) is achieved, with a peak radiation efficiency of 90% and an average efficiency of 76% within the operating band. To realize wideband impedance matching with the receiving antenna, the rectifying circuit adopts a single-shunt diode half-wave topology, combining L-type and T-type matching networks to significantly extend the operating bandwidth. Experimental results demonstrate that at input power levels of 7 dBm, 7 dBm, and 9 dBm, the rectifier achieves peak conversion efficiencies of 56.7%, 59.8%, and 56.3% at the three target frequencies (2.45 GHz, 2.6 GHz, and 3.5 GHz), respectively. Furthermore, the rectifier exhibits stable rectification performance across a wide input power dynamic range from -15 dBm to 7 dBm. Consequently, the proposed rectenna holds significant application value for passive IoT nodes, low-power sensors, and self-sustainable electronic devices.

High-performance artificial synaptic devices capable of emulating biological synaptic functions are crucial for developing energy-efficient neuromorphic computing systems. Memristors, whose conductance can be modulated to mimic synaptic plasticity, offer a promising platform for such applications. In this work, we present an electric field-controlled memristor based on an FeGaB/PMN-PT multiferroic heterostructure, in which synaptic weights are continuously tuned by applied voltage pulses. The device was fabricated via magnetron sputtering and exhibits nonvolatile, multi-level resistance switching under pulsed electric field modulation, achieving 83 distinct and continuously adjustable resistance states. Key synaptic functionalities, including long-term potentiation/depression, paired-pulse facilitation, and spike-timing-dependent plasticity, are successfully demonstrated. When integrated into a recurrent neural network, the system achieves a recognition accuracy of 92.4% in a benchmark task. By quantifying the nonlinearity (NL) and asymmetry of weight updates and directly linking them to accuracy performance, we further reveal that device-level NL critically governs system-level computational accuracy. This insight provides a clear optimization pathway for future artificial spintronic synapses, underscoring their potential for high-speed, nonvolatile, and adaptive neuromorphic hardware.

This study proposes a spatial multiplexing wireless communication method based on coding metasurfaces, achieving efficient electromagnetic wave manipulation through optimized coding matrices to enable independent information transmission in different directions. By employing the weighting theorem, we optimize the reflection characteristics of the metasurface, suppressing sidelobe gain and enhancing the gain difference between the main and sidelobes. This improves beam directionality and reduces energy leakage in non-target directions. In the experiment, we constructed a dual-channel direct digital transmission system, where an FPGA dynamically modulates the coding metasurface to generate specific reflected beams based on input data, enabling independent transmission of a 6.1 GHz carrier signal in two asymmetric directions. Experimental results show that, with Chebyshev-weighted optimized coding sequences, the gain difference between the main lobe and the strongest sidelobe increased by 4.99 dB, 1.5 dB, and 7.33 dB at three positive angles (+ 18 degrees, + 28 degrees, and + 45 degrees), and by 4.94 dB, 4.03 dB, and 4.22 dB at three negative angles (-15 degrees, -35 degrees, and -40 degrees), respectively. Furthermore, a wireless communication system was constructed based on the metasurface, enabling simultaneous transmission of digital information at two asymmetric angles, -15 degrees and + 18 degrees. The proposed spatial multiplexing strategy can be further extended to multi-channel wireless transmission. This study verifies the feasibility of using metasurfaces to manipulate communication signals, providing a foundational reference for future expansion and applications in complex channel environments.

This article addresses the limitations of standard chips in processing large-scale datasets by leveraging neuromorphic architectures, particularly spike neural networks (SNNs), to simulate the pulsed signals of biological brains for enhanced power efficiency and performance. Initially, we developed a complementary metal-oxide-semiconductor (CMOS) circuit based on the integrate-and-fire (IF) neuron model, which generates spike signals with the spatiotemporal dynamics typical of neurons. This approach provides a novel perspective for neuromorphic chip design. Next, we introduce the partial element equivalent circuit (PEEC) method to establish a unit circuit model for the memristor crossbar array, accounting for all resistance-inductance-capacitance (RLC) parasitic couplings. This model enables precise signal integrity (SI) analysis, offering deeper insights into the signal transmission mechanisms within memristor crossbar arrays and serving as a foundation for optimizing neuromorphic chip performance. Building upon this model, we designed a 24 x 24 crossbar array hardware prototype, experimentally validating the reliability and feasibility of the circuit. Finally, we perform SI analysis to demonstrate that the performance of the memristor crossbar array can be enhanced by optimizing the rise time, adjusting the memristor's resistance state, and adding inductance at the output. These findings provide both a theoretical foundation and a technical pathway for improving the performance of neuromorphic chips in practical applications.

Magnetic induction tomography (MIT) has the advantages of being noninvasive, noncontact, and low cost, and it is widely used in multiple fields and the biomedical field. However, most traditional MIT systems use detection coils as magnetic field sensors, which have low detection sensitivity, magnetoelectric (ME) sensors composed of magnetostrictive materials and piezoelectric materials with the advantages of high sensitivity, wide bandwidth, and small volume. In this article, a double excitation coil eddy current magnetic induction detection model used for MIT system is established. The variations in the excitation magnetic field, the induced magnetic field, and the phase shift with the thickness and conductivity of the saline are numerically calculated. A low conductivity medium detection module with a minimum magnetic field detection limit lower than 20 pT in the range of 15-30 MHz is constructed using an ME sensor, its phase shift noise is lower than $(6 \times 10<^>{-5})<^>{\circ }$ , and the conductivity and volume changes in the saline are detected. This double excitation coil eddy current magnetic induction detection model based on ME sensors has high sensitivity, low phase shift noise, and small volume, providing a fresh perspective for MIT biomedical imaging.

As a popular artificial composite material emerging in recent years, metasurfaces are one of the most likely devices to break through the volume limitation of conventional optical components due to their compact structure, flexible materials, and high modulation resolution of the beam. With a unique arrangement of units or made of special materials, the metasurface can effectively modulate the incident light's amplitude, phase, polarization, and frequency, thus realizing applications such as communication, imaging, sensing, and beam steering. The interaction of high‐resolution structure, periodic arrangement, and unique constituent materials makes it possible to realize these applications, so researchers should choose the appropriate micro‐nano processing technologies when designing and preparing the metasurface. This review will present micro‐nano processing technologies related to the preparation of metasurfaces, such as electron beam lithography (EBL), femtosecond laser processing, focused ion beam lithography (FIB), additive manufacturing, nanoimprinting, and self‐assembly, respectively. In addition, classical lithography techniques such as wet lithography, plasma lithography, deep reactive ion etching (DRIE), and photolithography will be introduced. Their development history and functions are described in detail, and examples of these techniques in preparing micro‐nano‐structures in different branches are presented, as well as some examples of metasurface preparation using these techniques. In addition, this paper has produced several tables describing these technologies, outlining their resolution, processing materials, advantages and disadvantages, and so on. Hopefully, this review will provide researchers with options and ideas for preparing metasurfaces.

With the rapid increase in integrated circuit chip frequencies, the complexity of electromagnetic interference issues and the time-consuming nature of traditional pin mapping (pinmap) design methods have become increasingly evident. This article leverages advanced machine learning techniques to accurately and efficiently predict the maximum 3-m radiated electric field of pinmap packages. Among the various models tested, the convolutional neural network (CNN) demonstrated the best performance. When combined with the Adam optimizer, the CNN achieved an average relative error of less than 1.5% across the 6 to 20 GHz frequency range. The trained CNN model’s prediction speed is several orders of magnitude faster than full-wave simulation methods. Furthermore, this article proposes a structural optimization method to minimize radiation in pinmap packages. By integrating ground ball position optimization with the trained CNN model, the method achieves optimal radiation suppression. Validation across three different cases demonstrated a reduction in radiation by 4.85 to 10.37 dB (mV/m), confirming the effectiveness and applicability of the proposed approach.

Neuromorphic chips based on spiking neural networks (SNNs) differ from traditional artificial neural networks chips in that they encode information as spike trains. These unique spike train signals introduce new challenges related to signal integrity (SI) and electromagnetic radiation in 3-D packaging. In this article, we modeled the structure of through-silicon vias (TSVs) and redistribution layers (RDLs) in 3-D packaging and analyzed the effects of 3-D packaging structural parameters on the transmission characteristics of spiking signals. The results showed that increase in the length of the RDL, the conductivity of the silicon substrate, and the radius and pitch of the TSV exacerbated waveform distortion and signal loss of the spiking signal within certain appropriate ranges. Additionally, high-frequency structure simulator (HFSS) simulation results indicated that crosstalk between two spiking signals was minimal when they were transmitted through adjacent TSV-RDL paths. However, when a spiking signal and a digital signal were transmitted through adjacent TSV-RDL paths, the spiking signal exhibited significant waveform distortion, which worsened as the amplitude and frequency of the digital signal increased. Furthermore, we discovered that when spiking and digital signals of the same voltage and frequency are transmitted in 3-D packaging, the spiking signal generated a more severe near-field electric field. Finally, we proposed a novel interconnect structure comprising two metal shielding (MS) layers directly contacting the silicon substrate and an insulating wall (IW). The equivalent circuit of the proposed structure was derived and validated through time-domain transient simulations. The proposed structure reduced transmission loss by 21 mV and attenuated the near-field electric field by approximately 160 times compared to the conventional TSV-RDL.

High-performance artificial synaptic devices that emulate the functions of biological synapses are crucial for advancing energy-efficient brain-inspired computing systems. Current studies predominantly focus on memristive devices, which achieve synaptic functions through nonvolatile electric current-assisted carrier modulation. However, these methods often suffer from excessive energy consumption. Here, a type of low-energy-consumption artificial synapse based on strain-mediated electric-field control of magnetic skyrmion's radius is demonstrated, where the energy consumption is 10 fJ per state and the non-volatility is achieved by local ferroelectric domain switching under bipolar electric fields. The proposed skyrmion-based synaptic device can replicate essential synaptic behaviors, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, paired-pulse depression, and spiking-time-dependent plasticity, aligning it closely with the biological synaptic system. The synaptic weight change and non-linearity of the artificial synapse are emulated by modulating the magnetic skyrmion's radius through precisely engineering the applied electric-field pulses. Simulation using the Modified National Institute of Standards and Technology database reveals that the pattern recognition rate decreases exponentially with increasing LTP/LTD non-linearity, quantifying the effect of the LTP/LTD non-linearity on the pattern recognition rate. This work underscores the potential of strain-mediated electric-field control of single skyrmion's radius as a groundbreaking approach for developing high density and low-energy consumption artificial synaptic devices.

The 3-D stacking technology of crossbar arrays in neuromorphic chips offers significant potential for future storage and computing advancements. To reduce RC delay and increase interconnect density, this article proposes a novel double-layer resistive random access memory (RRAM) crossbar array structure with monolithic vias (MIVs), comprising two identical RRAM crossbar arrays on the top and bottom layers, vertically connected by four parallel MIVs. First, we extracted the equivalent circuit of the structure in ANSYS Q3D software, including resistance, parasitic inductance, and coupling capacitance. Subsequently, the spiking signal is added to the RRAM crossbar array to analyze the signal integrity problem from three perspectives: array size, coupling capacitance, and the presence or absence of parasitic inductance. The findings reveal that the increase in array size and coupling capacitance, and the presence of parasitic inductance can lead to signal integrity issues such as IR drop, crosstalk, and ripple. Furthermore, an electromagnetic coupling analysis is conducted to explore interactions between parallel and vertical interconnects during signal transmission. The findings highlight that adjusting the structure-to-ground distance significantly suppresses the coupling electric field, reducing its intensity from 10(<^>)6 to 10(<^>)4, thus improving system stability.

With the rapid growth of unmanned aerial vehicles (UAVs) and IoT users, spectrum resources are becoming increasingly scarce, making cognitive radio (CR) technology a key approach to improving spectrum utilization. However, traditional antennas are difficult to meet the lightweight, compact, and low-drag requirements of small UAVs due to spatial constraints. This paper proposes a tri-mode frequency reconfigurable flexible antenna that can be conformally integrated onto UAV wing arms to enable CR dynamic frequency communication. The antenna uses a polyimide (PI) substrate and has compact dimensions of 31.4 × 58 × 0.05 mm3. A microstrip line-based frequency-selective network is designed, incorporating PIN and varactor diodes to realize three operation modes, dual-band (2.25~3.55 GHz, 5.6~6.75 GHz), single-band (3.35~5.3 GHz), and continuous tuning (4.3~6.1 GHz), covering WLAN, WiMAX, and 5G NR bands. Test results show that the antenna maintains stable performance under conformal conditions, with frequency shifts less than 4%, gain (3.65~4.77 dBi), and radiation efficiency between 67.2% and 82.9%. The tuning ratio reaches 38.8% in the continuous mode. This design offers a new solution for CR communication in compact UAV platforms and shows promising application potential.

This article proposes an enhanced equivalent dipole model, integrating convolutional neural networks and fully connected networks, to predict near-field magnetic fields in neuromorphic chips. Traditional dipole models struggle with complex electromagnetic interactions, such as multiple reflections and diffractions, which occur in dense neuromorphic circuits. To address these challenges, the proposed method constructs a coefficient matrix based on the spatial relationships between scanning points and dipoles. This matrix is used as input to the neural network, which predicts the radiation field magnitude as output. The model is trained to accurately predict magnetic fields beyond the scanned area. The approach is validated through both numerical simulations and experimental measurements, showing a relative error of approximately 5% between predicted and measured values, indicating high accuracy.

Metasurfaces offer exceptional capabilities for controlling electromagnetic waves, enabling the realization of unique electromagnetic properties. As communication technology continues to evolve, metasurfaces present promising applications in wireless communications. This paper reviews the latest advancements in metasurface research within the communication sector, explores metasurface-based wireless relay technologies, and summarizes various wireless communication methods employing different types of metasurfaces across diverse modulation schemes. This paper provides a detailed discussion on the design of wireless communication systems based on coding metasurfaces to simplify transmitter architecture, as well as the development of intelligent coding metasurfaces in the communication field. It also elaborates on the application of vector vortex light fields in metasurface communication. Finally, it offers a forward-looking perspective on wireless communication systems that incorporate coded metasurfaces. This review aims to furnish researchers with a thorough understanding of the current state and future directions of coded metasurface applications in communications.