Search Results (2,212)

To address contact-limited transport commonly encountered in two-dimensional semiconductors, this study fabricated few-layer two-dimensional molybdenum disulfide (MoS₂) films on sapphire substrates via controllable oxide-to-sulfide conversion. Combined sputtering deposition of molybdenum trioxide and precise chemical-vapor sulfidation afforded high-quality, high-uniformity, and thickness-tunable MoS₂. The resulting films exhibit distinct differences in the frequencies of the Raman modes, consistent elemental ratios, and uniform interlayer spacing of ~0.65 nm. The MoS₂-based devices exhibit robust photodetection with microampere-scale photocurrents. Bilayer MoS₂ exhibited negative photoconductivity under ambient atmosphere, which is hypothesized to be linked to environment-induced surface doping and molecular adsorption rather than permanent structural traps. Contact engineering via mild thermal annealing of Ni electrodes significantly enhanced the photocurrent by improving effective interfacial carrier injection. These findings underscore the oxide sulfidation strategy as a scalable approach for engineering the layer-dependent behavior of transition metal dichalcogenides for optoelectronic applications. Full article

This paper addresses the challenge of optimally controlling silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) to minimize switching losses while simultaneously reducing overshoots and voltage slew rates. A digitally controlled gate current source is used to drive the transistors, and its output waveform is defined by a set of parameters that must be optimized. To this end, a sequential lowest segment extraction (SLSE) method is proposed to identify parameter sets that generate trade-off curves that closely approximate the Pareto frontiers. These curves represent the lowest simultaneously achievable values of either switching loss and current/voltage overshoot, or switching loss and maximum voltage slew rate. The resulting boundary curves demonstrate a total switching loss reduction of up to 60% while maintaining nearly the same overshoot and slew rate values compared to a classical gate driver. The paper concludes with an analysis of the results and a summary of the key findings. Full article

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH₃), Acetaldehyde (CH₃CHO), Acetone ((CH₃)₂CO), Ethylene (C₂H₄), Ethanol (C₂H₅OH), Toluene (C₆H₅CH₃). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD–DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 ± 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD–DM–20 achieved 47.91 ± 18.79% mean accuracy over Batches 2–10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework—accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference—is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work. Full article

To minimize the influence of interface states and surface damage, by inserting a gate oxide layer, the photoelectrochemical oxidation method was utilized to directly grow the gate oxide layer while simultaneously creating the gate-recessed regions onto gallium nitride (GaN)-based single-gate and dual-gate metal-oxide-semiconductor high-electron-mobility transistors (MOS-HEMTs). Compared to the single-gate structure, the two-dimensional electron gas (2DEG) channel layer was also modulated by the auxiliary gate, in addition to being modulated by the main gate. Consequently, a wider transconductance range, larger saturation drain-source current, lower gate leakage current, and higher drain-source breakdown voltage were the benefits derived from the auxiliary gate functionality in the dual-gate devices. Moreover, the low-frequency noise characteristics of the GaN-based MOS-HEMTs could also be improved by the dual-gate structure. These experimental results demonstrated that incorporating a dual-gate structure and directly grown gate oxide layers onto GaN-based MOS-HEMTs is a promising alternative for GaN-based low-noise, high-power, and high-frequency applications. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Ternary heterojunction photocatalysts enhance the separation and transport of photogenerated charge carriers, thereby boosting their redox activity for use in environmental and sustainable energy applications. This study focuses on the synthesis of a TiO₂/Bi₂O₃/g-C₃N₄ heterojunction composite via a ceramic method with TiO₂ loadings of 80%, 85%, and 90% (denoted 80T-BC, 85T-BC, and 90T-BC, respectively) to investigate structure–property–performance relationships in photocatalytic dye degradation. The structural, optical, and morphological properties of the synthesised materials were characterised using X-ray diffraction (XRD), scanning electron microscopy (SEM), and diffuse reflectance UV–Vis spectroscopy (DRS). The photocatalytic performance was evaluated by measuring the degradation of Rhodamine B under visible light irradiation. Under optimised conditions (pH 6, initial RhB concentration of 5 mg/L, and a reaction time of 120 min), a degradation rate of 99% was achieved. Furthermore, the semiconductor demonstrated significant antibacterial activity against both Gram-negative (Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. This study presents a promising strategy for modifying TiO₂-based semiconductors by incorporating different metal oxides. The formation of the resulting heterojunction significantly enhances photocatalytic efficiency, demonstrating strong potential for practical environmental remediation. Full article

Modern scanning microscopes and robotic scanning systems increasingly use visual recognition and machine learning technologies to extract complex data from acquired images. This study examined sensor data fusion in optical imaging to detect and control the deviation of the position of the tool during various micro-manipulations for biologic and microscale engineering. The sensor data fusion study was performed using a scanning micro-robotic system with an integrated optical microscope and a vision sensor providing an image of the object’s bottom. The bottom vision sensor is a typical complementary metal–oxide–semiconductor sensor that is used to observe micrometer-sized semi-transparent objects. The challenge for sensor fusion in such a study is not only data fusion, but also the trajectory deviation inherent in directing the manipulator in the X and Y directions according to the selected trajectory. The data fusion method was applied to estimate deviations from the given trajectory of the scanning microscope. The unique novelty of this work is that an additional vision sensor is used to increase the accuracy of positioning determination of a scanning micro-robotic system, placed under the semi-transparent object, using the fusion of the obtained data, thus additionally controlling the objective deviations. By testing several known data fusion methods, a unique solution was achieved. The proposed sensor fusion method achieved a positioning accuracy of less than 0.5 μm at speeds up to 5 mm/s. Experimental results demonstrate that the system maintains high stability. This quantitative performance proves the system’s suitability for high-precision biological micro-manipulation, where mechanical drift was previously a limiting factor. Full article

The development of novel information-functional devices based on emergent physical phenomena is crucial for integrated circuit technology in the post-Moore era. Two-dimensional magnetic materials present an ideal platform for spintronic devices; however, regulating their room temperature magnetism poses significant challenges. Traditional methods like ionic liquid gating and strain control face issues such as poor stability and complex processes, complicating compatibility with standard silicon technology. Here, we demonstrate a straightforward and robust approach for dielectric layer-engineered room temperature ferromagnetism in 2D metallic magnets by leveraging metal–insulator–semiconductor (MIS) structures. Using surface-oxidized Fe₃GeTe₂ as a model system, we systematically investigate how SiO_x dielectric layer thickness (50–300 nm) modulates magnetic properties. Thin dielectric layers significantly enhance room temperature ferromagnetism through boosted interfacial charge transfer, whereas thick layers maintain the material near its intrinsic state due to dielectric screening effects. Furthermore, reversible optical modulation of magnetism is achieved under ultraviolet illumination, with photoresponse capability diminishing as dielectric thickness increases. This work establishes a scalable, silicon-compatible strategy for controlling 2D magnetism and provides critical insights for developing optically tunable spintronic devices and non-volatile memory applications. Full article

Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-access circuits face increasing power loss, parasitic effects, interconnect complexity, and sensitivity to process variations at the nanoscale. To address these limitations, this paper proposes a quantum-dot cellular automata (QCA)-based decoder-driven static random-access memory (SRAM)-access architecture for compact and energy-efficient IoT perception-layer memory. The proposed framework integrates three main components: a majority-logic RAM cell with feedback-based storage and non-destructive readout, a compact 2 × 4 decoder with enable and auxiliary asynchronous set/reset control, and a 1 × 4 SRAM array in which the decoder is embedded to reduce routing and clocking overhead. The circuit layouts were implemented and functionally verified using QCADesigner 2.0.3, while the energy behavior was evaluated using QCADesigner-E. Simulation results confirm correct write/read (W/R) and address-selection behavior. The proposed 2 × 4 decoder achieves 86 QCA cells, 0.08 µm² occupied area, and one clocking unit, reducing cell count, area, and clocking by 48.19%, 50.00%, and 20.00%, respectively, compared with the best selected decoder baseline. The integrated 1 × 4 SRAM array achieves 684 cells and 14 clocking units, improving timing by 30.00% compared with the closest SRAM-array baseline. These results demonstrate that the proposed QCA-based memory-access structure provides a compact and low-overhead solution for energy-constrained IoT communication systems. Full article

This article presents a fast-locking phase-locked loop (PLL) that incorporates a low-power extended phase frequency detector (LPEPFD) and a discriminator-aided phase detector (DAPD) to simultaneously address cycle slippage and frequency overshoot issues during frequency and phase acquisition, respectively. Specifically, the proposed LPEPFD introduces a novel finite state machine architecture that extends the linear range of a conventional PFD without requiring a power-hungry counter, thereby eliminating cycle slippage and reducing the time required for frequency acquisition while maintaining switching activity and power consumption comparable to those of the conventional design. Moreover, after frequency convergence, the DAPD quantizes the accumulated phase error, which is corrected by adaptively tuning the programmable delay lines without causing significant frequency overshoot seen in conventional PLLs, resulting in improved settling time. Fabricated using a 28 nm complementary metal oxide semiconductor (CMOS) process, the proposed fast-locking PLL occupies an area of 0.36 mm² and operates over a frequency range of 2.6 to 3.2 GHz. Experimental results demonstrate a 0.84-μs settling time for a frequency hop from 2.6 to 3.1 GHz. The designed PLL consumes 5.6 mW of power from a supply of 1 V with an integral root-mean-square jitter of 1.27 ps from 1 kHz to 100 MHz. Full article

Josephson junctions are key elements in superconducting qubits. Their efficient wafer-scale characterization is crucial for process control and optimization, motivating analysis approaches that extend beyond conventional cryogenic measurements. In this work, we demonstrate that room temperature (RT) capacitance and current–voltage measurements, combined with appropriate data analysis, enable extraction of relevant junction parameters such as oxide thickness, tunnel coefficient, and interfacial defect density. Furthermore, different charge transport mechanisms can be identified from detailed current–voltage analysis. We evaluate our characterization technique using tunnel junctions fabricated on 200 mm wafers in a complementary metal–oxide–semiconductor (CMOS)-compatible subtractive process. The results show a homogeneous average oxide thickness across the wafer with a variation below 3%. A dependence of the tunnel coefficient on oxide thickness indicates a stoichiometry gradient within the oxide. Additionally, low interfacial defect densities in the range of 70–5000 defects/cm² are observed in our junctions, increasing with decreasing oxide thickness, suggesting that wet etching used for thickness control introduces interfacial trap states. Our study highlights the importance of advanced RT characterization for extracting tunnel junction parameters on the wafer scale, enabling effective process monitoring and optimization in industrial superconducting qubit manufacturing. Full article

Freestanding graphene exhibits exceptional mechanical flexibility and electrical conductivity, making it well suited for varying capacitance applications. For example, when suspended above a fixed electrode, graphene will move in response to an applied bias voltage, thereby forming a varactor or voltage-controlled capacitor. In this work, we present a very detailed and scalable fabrication process for building graphene-based variable capacitor device structures. Starting with commercially available 100 mm silicon wafers with a thick thermal oxide layer, we fabricate thousands of individually accessible freestanding graphene variable capacitors using standard semiconductor methods. The process begins with metal deposition to establish alignment crosshairs, then oxide etching to create trenches, a second metal deposition to form electrodes and bonding pads, followed by large-area graphene transfer, then patterning the graphene via oxygen plasma etching, critical point drying for suspension, and finally wire bonding our devices into a package. We use optical and atomic force microscopy characterization to confirm our design specifications were met. Electrical characterization confirms successful graphene suspension through voltage-dependent capacitance measurements. The procedure presented here successfully suspends both pure multilayer graphene as well as graphene with a thick layer of PMMA. Full article

High-entropy perovskite oxides attract considerable attention due to their outstanding properties and extensive applications. In this work, the lattice distortion and the mechanical, thermal and electronic structure properties of high-entropy (Ca_0.2Sr_0.2Ba_0.2La_0.2Pb_0.2)TiO₃ (CSBLPT) are investigated through first-principles calculations. The results suggest that the influence of O atoms on lattice distortion is predominant, and the effect of overall A-site atoms plays a distinctly greater role than that of the B-site atoms. The mechanical results show that the high-entropy CSBLPT has a lower Young’s modulus and higher fracture toughness than ternary SrTiO₃. The Debye temperature also indirectly indicates that the thermal expansion coefficient of the studied high-entropy perovskite is greater than that of SrTiO₃. As for thermal conductivity, the obtained result of CSBLPT is also appreciably lower than that of SrTiO₃, and the lowest thermal conductivity is along the [100] direction. The Fermi level of high-entropy CSBLPT is transferred to the conduction band, exhibiting a degenerate n-type semiconductor behavior with metallic-like characteristics, and the Bader charge values are also related to the local lattice distortion, which may cause differences in thermomechanical properties between high-entropy CSBLPT and SrTiO₃. Above all, high-entropy CSBLPT is a preferable TBC material with excellent performance under working conditions compared to SrTiO₃. Full article

Self-powered photodetectors have attracted widespread attention in Internet of Things applications due to their low power consumption and high sensitivity. In this study, plasmonic self-powered poly(3-hexylthiophene)/zinc oxide (P3HT/ZnO) heterojunction photodetectors incorporating silver triangular nanoplates (AgTNPs) were fabricated using sol–gel and spin-coating techniques. The experimental results demonstrate that the incorporation of AgTNP nanostructures significantly enhances the photoelectric conversion efficiency of the plasmonic P3HT/AgTNPs/ZnO photodetectors across both the ultraviolet and visible spectral regions. The responsivity enhancement ratio of the plasmonic devices reached its maximum under illumination at a wavelength of 525 nm. Compared with the reference P3HT/ZnO device, the responsivity values of the P3HT/AgTNPs-1/ZnO and P3HT/AgTNPs-2/ZnO devices increased by factors of 3.24 and 4.21, respectively. The optimal P3HT/AgTNPs-2/ZnO device exhibited responsivity values of 9.49, 10.80, and 10.47 mA/W under irradiation at wavelengths of 440 nm, 460 nm, and 525 nm, respectively. The mechanism of performance enhancement induced by the plasmonic AgTNPs is also discussed. This work demonstrates that embedding triangular plasmonic metal nanoplates within semiconductor heterojunctions constitutes an effective strategy for performance enhancement, providing new insights for the rational design of high-performance optoelectronic devices. Full article

In this study, Zn-doped Co₃O₄ nanorods and nanosheets with controlled Zn/Co molar ratios were synthesized via a hydrothermal strategy to clarify the respective roles of morphology and elemental doping in ethylbenzene sensing. The gas-sensing performance is strongly influenced by morphology, and the radially oriented nanorod structure significantly enhances sensing response compared with nanosheet structures. Zn doping effectively enhances the gas-sensing performance of Co₃O₄. As a result, the optimized Zn-doped nanorod sensor exhibits high sensitivity to ethylbenzene, a low detection limit, rapid response and recovery, and excellent operational stability. Density functional theory calculations reveal that the predominantly exposed facets of the nanorod structure possess stronger adsorption affinity and pronounced charge transfer toward ethylbenzene, providing theoretical support for the morphology-dominated sensing behavior. At the same time, Zn incorporation further adjusts the band structure and surface reactivity. Overall, this work elucidates a morphology-dominated and doping-assisted enhancement mechanism, offering clear design principles for high-performance Co₃O₄-based ethylbenzene sensors. Full article