arXiv subjects

Zhenxing Wang

Publications and source records attributed to Zhenxing Wang.

At least 19 recordsLinked to original sources

arXiv2026

Re-acceleration of Energetic Ions via Small-Scale Reconnection in Magnetic Fusion Plasmas

We report the first observation on the EXL-50U spherical torus that energetic particles injected by neutral beam injection (NBI) can be stably accelerated to significantly higher energies - reaching up to 2.5 times the injection energy, occurring without significant large-scale magnetohydrodynamic (MHD) bursts. Simulations based on EXL-50U parameters indicate that small-scale magnetic reconnection, mediated by multiple magnetic islands, fails to accelerate bulk thermal ions but efficiently energizes seed fast ions. Unlike global MHD events, such small-scale reconnection is ubiquitous in magnetic confinement devices and does not degrade core confinement. This mechanism offers a novel and potentially universal channel for auxiliary ion heating in future fusion reactors.

physics.plasm-ph↗

arXiv2026

Reconstructing Spiking Neural Networks Using a Single Neuron with Autapses

Spiking neural networks (SNNs) are promising for neuromorphic computing, but high-performing models still rely on dense multilayer architectures with substantial communication and state-storage costs. Inspired by autapses, we propose time-delayed autapse SNN (TDA-SNN), a framework that reconstructs SNNs with a single leaky integrate-and-fire neuron and a prototype-learning-based training strategy. By reorganizing internal temporal states, TDA-SNN can realize reservoir, multilayer perceptron, and convolution-like spiking architectures within a unified framework. Experiments on sequential, event-based, and image benchmarks show competitive performance in reservoir and MLP settings, while convolutional results reveal a clear space--time trade-off. Compared with standard SNNs, TDA-SNN greatly reduces neuron count and state memory while increasing per-neuron information capacity, at the cost of additional temporal latency in extreme single-neuron settings. These findings highlight the potential of temporally multiplexed single-neuron models as compact computational units for brain-inspired computing.

cs.NE↗

arXiv2026

Toward Efficient Spiking Transformers: Synapse Pruning Meets Synergistic Learning-Based Compensation

As a foundational architecture of artificial intelligence models, Transformer has been recently adapted to spiking neural networks with promising performance across various tasks. However, existing spiking Transformer(ST)-based models require a substantial number of parameters and incur high computational costs, thus limiting their deployment in resource-constrained environments. To address these challenges, we propose combining synapse pruning with a synergistic learning-based compensation strategy to derive lightweight ST-based models. Specifically, two types of tailored pruning strategies are introduced to reduce redundancy in the weight matrices of ST blocks: an unstructured $\mathrm{L_{1}P}$ method to induce sparse representations, and a structured DSP method to induce low-rank representations. In addition, we propose an enhanced spiking neuron model, termed the synergistic leaky integrate-and-fire (sLIF) neuron, to effectively compensate for model pruning through synergistic learning between synaptic and intrinsic plasticity mechanisms. Extensive experiments on benchmark datasets demonstrate that the proposed methods significantly reduce model size and computational overhead while maintaining competitive performance. These results validate the effectiveness of the proposed pruning and compensation strategies in constructing efficient and high-performing ST-based models.

cs.LG↗

arXiv2025

Non-Inductive Current Start-Up Using Multi-Harmonic Electron Cyclotron Wave and Current Ramp-Up Through Combined Electron Cyclotron Wave and Ohmic Heating in EXL-50U Spherical Torus

The non-inductive current start-up by multi-harmonic electron cyclotron wave has been systematically investigated in the EXL-50U spherical torus. Significant enhancements of the driven current with increasing number of resonance layers have been demonstrated by variation of the number of harmonic resonance layers of the ECW through adjustment of the magnetic field or plasma cross section. The critical role of multi-harmonic ECW in enhancing the driven current has been experimentally verified for the first time. To explain the related experimental observations, a physical mechanism involving multi-harmonic heating, multiple reflections, and multi-pass absorption - leading to the generation of high-energy electrons via X-mode wave or electron Bernstein wave has been proposed. The current drive capacity of the first harmonic extraordinary mode of the ECW has also been experimentally confirmed for the first time. After the application of Ohmic heating during the current ramp-up phase, the current drive efficiency of ECW is further enhanced. Leveraging the synergistic effect between ECW and Ohmic heating, EXL-50U achieved a plasma current of 1 MA, with the non-inductively driven current fraction reaching 70%.

physics.plasm-ph↗

arXiv2025

ReFineG: Synergizing Small Supervised Models and LLMs for Low-Resource Grounded Multimodal NER

Grounded Multimodal Named Entity Recognition (GMNER) extends traditional NER by jointly detecting textual mentions and grounding them to visual regions. While existing supervised methods achieve strong performance, they rely on costly multimodal annotations and often underperform in low-resource domains. Multimodal Large Language Models (MLLMs) show strong generalization but suffer from Domain Knowledge Conflict, producing redundant or incorrect mentions for domain-specific entities. To address these challenges, we propose ReFineG, a three-stage collaborative framework that integrates small supervised models with frozen MLLMs for low-resource GMNER. In the Training Stage, a domain-aware NER data synthesis strategy transfers LLM knowledge to small models with supervised training while avoiding domain knowledge conflicts. In the Refinement Stage, an uncertainty-based mechanism retains confident predictions from supervised models and delegates uncertain ones to the MLLM. In the Grounding Stage, a multimodal context selection algorithm enhances visual grounding through analogical reasoning. In the CCKS2025 GMNER Shared Task, ReFineG ranked second with an F1 score of 0.6461 on the online leaderboard, demonstrating its effectiveness with limited annotations.

cs.IR↗

arXiv2025

Intermediate Resistive State in Wafer-Scale MoS${_2}$ Memristors through Lateral Silver Filament Growth for Artificial Synapse Applications

Memristors based on two-dimensional materials (2DMs) have garnered significant attention due to their fast resistive switching (RS) behavior and atomic-level thickness, which enables low power consumption, making them promising candidates for neuromorphic computing. Among these, memristors based on molybdenum disulfide (MoS${_2}$) have been extensively studied. Their RS has been attributed to the formation and rupture of conductive filaments (CFs). However, the underlying mechanism of filament formation remains underexplored, and the inherently stochastic nature of RS leads to high variability and limited reproducibility. Additionally, the lack of scalable fabrication techniques for 2DM-based memristors restricts their integration into standard semiconductor technology. Here, we demonstrate memristors based on metal-organic chemical vapor-deposited MoS${_2}$ on the wafer-scale. Our devices exhibit volatile and nonvolatile RS behavior, tunable by modulating the current compliance. Notably, we observe stable RS characteristics in an intermediate resistive state (IRS), featuring set and reset voltages within $\pm$1 V, an endurance exceeding 2500 cycles in direct current mode, and a state retention over 10${^6}$ s. The experimental data, complemented with simulations, suggest that the IRS originates from the lateral growth of the CF within the MoS${_2}$ layer. Furthermore, the devices successfully emulate synaptic plasticity with current responses on the microsecond timescale, highlighting their potential for large-scale integration in neuromorphic computing architectures.

physics.app-ph↗

arXiv2025

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives

The ongoing evolution of AI paradigms has propelled AI research into the agentic AI stage. Consequently, the focus of research has shifted from single agents and simple applications towards multi-agent autonomous decision-making and task collaboration in complex environments. As Large Language Models (LLMs) advance, their applications become more diverse and complex, leading to increasing situational and systemic risks. This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent's goals, preferences, and behaviors align with human values and societal norms. Addressing socio-governance demands through a Multi-level Value framework, this study comprehensively reviews value alignment in LLM-based multi-agent systems as the representative archetype of agentic AI systems. Our survey systematically examines three interconnected dimensions: First, value principles are structured via a top-down hierarchy across macro, meso, and micro levels. Second, application scenarios are categorized along a general-to-specific continuum explicitly mirroring these value tiers. Third, value alignment methods and evaluation are mapped to this tiered framework through systematic examination of benchmarking datasets and relevant methodologies. Additionally, we delve into value coordination among multiple agents within agentic AI systems. Finally, we propose several potential research directions in this field.

cs.AI↗

arXiv2025

Wafer-scale Synthesis of Mithrene and its Application in 2D Heterostructure UV Photodetectors

Silver phenylselenide (AgSePh), known as mithrene, is a two-dimensional (2D) organic-inorganic chalcogenide (MOC) semiconductor with a wide direct band gap, narrow blue emission and in-plane anisotropy. However, its application in next-generation optoelectronics is limited by crystal size and orientation, as well as challenges in large-area growth. Here, we introduce a controlled tarnishing step on the silver surface prior to the solid-vapor-phase chemical transformation into AgSePh thin films. Mithrene thin films were prepared through thermally assisted conversion (TAC) at 100°C, incorporating a pre-tarnishing water (H${_2}$O) vapor pulse and propylamine (PrNH${_2}$) as a coordinating ligand to modulate Ag${^+}$ ion reactivity and facilitate the conversion of Ph${_2}$Se${_2}$ into an active intermediate. The AgSePh thin films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and grazing incidence wide-angle X-ray scattering (GIWAXS). The pre-tarnishing process, combined with organic ligands, resulted in large crystals exceeding 1 $μ$m and improved homogeneous in-plane orientation, while also enabling the selective, wafer-scale synthesis of mithrene on 100 mm wafers. Furthermore, the films were integrated on planar graphene field-effect phototransistors (GFETs) and demonstrated photoresponsivity beyond 100 A/W at 450 nm, highlighting mithrene's potential for blue light-detection applications.

cond-mat.mtrl-sci↗

arXiv2025

Unraveling the dynamics of conductive filaments in MoS${_2}$ based memristors by operando transmission electron microscopy

Advanced operando transmission electron microscopy (TEM) techniques enable the observation of nanoscale phenomena in electrical devices during operation. They can be used to study the switching mechanisms in two-dimensional (2D) materials-based memristive devices, which is crucial to tailor their operating regimes and improve reliability and variability. Here, we investigate lateral memristive devices composed of 2D layered molybdenum disulfide (MoS${_2}$) with palladium (Pd) and silver (Ag) electrodes. We visualized the formation and migration of Ag conductive filaments (CFs) between the two electrodes under external bias voltage and their complete dissolution upon reversing the bias voltage polarity. The CFs exhibited a wide range of sizes, ranging from several Ångströms to tens of nanometers, and followed diverse pathways: along the MoS${_2}$ surfaces, within the van der Waals gap between MoS${_2}$ layers, and through the spacing between MoS${_2}$ bundles. Notably, the Ag electrode functioned as a reservoir for the CFs, as evidenced by the shrinking and growing of the Ag electrode upon switching. Our method enabled correlating the current-voltage responses with real-time TEM imaging, offering insights into failed and anomalous switching behavior, and providing clarity on the cycle-to-cycle variabilities. Our findings provide solid evidence for the electrochemical metallization mechanism, elucidate the formation dynamics of CFs, and reveal key parameters influencing the switching performance. Our approach can be extended to investigate similar memristive devices.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) is an emerging information extraction (IE) task, aiming to simultaneously extract entity spans, types, and corresponding visual regions of entities from given sentence-image pairs data. Recent unified methods employing machine reading comprehension or sequence generation-based frameworks show limitations in this difficult task. The former, utilizing human-designed type queries, struggles to differentiate ambiguous entities, such as Jordan (Person) and off-White x Jordan (Shoes). The latter, following the one-by-one decoding order, suffers from exposure bias issues. We maintain that these works misunderstand the relationships of multimodal entities. To tackle these, we propose a novel unified framework named Multi-grained Query-guided Set Prediction Network (MQSPN) to learn appropriate relationships at intra-entity and inter-entity levels. Specifically, MQSPN explicitly aligns textual entities with visual regions by employing a set of learnable queries to strengthen intra-entity connections. Based on distinct intra-entity modeling, MQSPN reformulates GMNER as a set prediction, guiding models to establish appropriate inter-entity relationships from a optimal global matching perspective. Additionally, we incorporate a query-guided Fusion Net (QFNet) as a glue network to boost better alignment of two-level relationships. Extensive experiments demonstrate that our approach achieves state-of-the-art performances in widely used benchmarks.

cs.IR↗

arXiv2024

Contact Resistance Optimization in MoS${_2}$ Field-Effect Transistors through Reverse Sputtering-Induced Structural Modifications

Two-dimensional material (2DM)-based field-effect transistors (FETs), such as molybdenum disulfide (MoS${_2}$)-FETs, have gained significant attention for their potential for ultra-short channels, thereby extending Moore's law. However, MoS${_2}$-FETs are prone to the formation of Schottky barriers at the metal-MoS${_2}$ interface, resulting in high contact resistance (R${_c}$) and, consequently, reduced transistor currents in the ON-state. Our study explores the modification of MoS${_2}$ to induce the formation of conductive 1T-MoS${_2}$ at the metal-MoS${_2}$ interface via reverse sputtering. MoS${_2}$-FETs exposed to optimized reverse sputtering conditions in the contact area show R${_c}$ values reduced to less than 50% of their untreated counterparts. This reduction translates into improvements in other electrical characteristics, such as higher ON-state currents. Since reverse sputtering is a standard semiconductor process that enhances the electrical performance of MoS${_2}$-FETs, it has great potential for broader application scenarios in 2DM-based microelectronic devices and circuits.

cond-mat.mtrl-sci↗

arXiv2024

High-yield large-scale suspended graphene membranes over closed cavities for sensor applications

Suspended membranes of monoatomic graphene exhibit great potential for applications in electronic and nanoelectromechanical devices. In this work, a "hot and dry" transfer process is demonstrated to address the fabrication and patterning challenges of large-area graphene membranes on top of closed, sealed cavities. Here, "hot" refers to the use of high temperature during transfer, promoting the adhesion. Additionally, "dry" refers to the absence of liquids when graphene and target substrate are brought into contact. The method leads to higher yields of intact suspended monolayer CVD graphene and artificially stacked double-layer CVD graphene membranes than previously reported. The yield evaluation is performed using neural-network-based object detection in SEM images, ascertaining high yields of intact membranes with large statistical accuracy. The suspended membranes are examined by Raman tomography and AFM. The method is verified by applying the suspended graphene devices as piezoresistive pressure sensors. Our technology advances the application of suspended graphene membranes and can be extended to other two-dimensional (2D) materials.

physics.app-ph↗

arXiv2024

Tunable Doping and Mobility Enhancement in 2D Channel Field-Effect Transistors via Damage-Free Atomic Layer Deposition of AlOX Dielectrics

Two-dimensional materials (2DMs) have been widely investigated because of their potential for heterogeneous integration with modern electronics. However, several major challenges remain, such as the deposition of high-quality dielectrics on 2DMs and the tuning of the 2DM doping levels. Here, we report a scalable plasma-enhanced atomic layer deposition (PEALD) process for direct deposition of a nonstoichiometric aluminum oxide (AlOX) dielectric, overcoming the damage issues associated with conventional methods. Furthermore, we control the thickness of the dielectric layer to systematically tune the doping level of 2DMs. The experimental results demonstrate successful deposition without detectable damage, as confirmed by Raman spectroscopy and electrical measurements. Our method enables tuning of the Dirac and threshold voltages of back-gated graphene and MoS${_2}$ field-effect transistors (FETs), respectively, while also increasing the charge carrier mobility in both device types. We further demonstrate the method in top-gated MoS${_2}$ FETs with double-stack dielectric layers (AlOX+Al${_2}$O${_3}$), achieving critical breakdown field strengths of 7 MV/cm and improved mobility compared with the back gate configuration. In summary, we present a PEALD process that offers a scalable and low-damage solution for dielectric deposition on 2DMs, opening new possibilities for precise tuning of device characteristics in heterogeneous electronic circuits.

physics.app-ph↗

arXiv2024

Multiparameter admittance spectroscopy for investigating defects in MoS${_2}$ thin film MOSFETs

A method for assessing the quality of electronic material properties of thin-film metal oxide semiconductor field-effect transistors (MOSFETs) is presented. By investigating samples with MOCVD-grown MoS${_2}$ channels exposed to atmospheric conditions, the existence of electron traps in MoS${_2}$ and at the interface between the gate insulator and the thin-film MoS${_2}$ are revealed. Differential conductance and capacitance data of the transistor channels are plotted as 3D surfaces on a base plane spanned by the measurement frequency versus the gate voltage. The existence of defects is confirmed by comparison with ideal results from a theoretical model.

physics.app-ph↗

arXiv2024

CVD Graphene Contacts for Lateral Heterostructure MoS${_2}$ Field Effect Transistors

Intensive research is carried out on two-dimensional materials, in particular molybdenum disulfide, towards high-performance transistors for integrated circuits. Fabricating transistors with ohmic contacts is challenging due to the high Schottky barrier that severely limits the transistors' performance. Graphene-based heterostructures can be used in addition or as a substitute for unsuitable metals. We present lateral heterostructure transistors made of scalable chemical vapor-deposited molybdenum disulfide and chemical vapor-deposited graphene with low contact resistances of about 9 k$Ω$$μ$m and high on/off current ratios of 10${^8}$. We also present a theoretical model calibrated on our experiments showing further potential for scaling transistors and contact areas into the few nanometers range and the possibility of a strong performance enhancement by means of layer optimizations that would make transistors promising for use in future logic circuits.

cond-mat.mes-hall↗

arXiv2023

Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication demands. This position paper makes the case for wireless in-package nanonetworking as the enabler of efficient and versatile wired-wireless interconnect fabrics for massive heterogeneous processors. To that end, the use of graphene-based antennas and transceivers with unique frequency-beam reconfigurability in the terahertz band is proposed. The feasibility of such a nanonetworking vision and the main research challenges towards its realization are analyzed from the technological, communications, and computer architecture perspectives.

cs.ET↗

arXiv2023

Variability and Reliability of Graphene Field-Effect Transistors with CaF2 Insulators

Graphene is a promising material for applications as a channel in graphene field-effect transistors (GFETs) which may be used as a building block for optoelectronics, high-frequency devices and sensors. However, these devices require gate insulators which ideally should form atomically flat interfaces with graphene and at the same time contain small densities of traps to maintain high device stability. Previously used amorphous oxides, such as SiO2 and Al2O3, however, typically suffer from oxide dangling bonds at the interface, high surface roughness and numerous border oxide traps. In order to address these challenges, here we use for the first time 2nm thick epitaxial CaF2 as a gate insulator in GFETs. By analyzing device-to-device variability for over 200 devices fabricated in two batches, we find that tens of them show similar gate transfer characteristics. Our statistical analysis of the hysteresis up to 175C has revealed that while an ambient-sensitive counterclockwise hysteresis can be present in some devices, the dominant mechanism is thermally activated charge trapping by border defects in CaF2 which results in the conventional clockwise hysteresis. We demonstrate that both the hysteresis and bias-temperature instabilities in our GFETs with CaF2 are comparable to similar devices with SiO2 and Al2O3. In particular, we achieve a small hysteresis below 0.01 V for equivalent oxide thickness (EOT) of about 1 nm at the electric fields up to 15 MV/cm and sweep times in the kilosecond range. Thus, our results demonstrate that crystalline CaF2 is a promising insulator for highly-stable GFETs.

physics.app-ph↗

arXiv2023

Development of high accurate family-use digital refractometer based on CMOS

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice, which is an important factor affecting human health. While laboratory-grade refractometers are expensive and unsuitable for personal use, existing low-cost commercial options lack stability and accuracy. To address this gap, we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor. By analyzing the output waveform pattern of the CMOS sensor, we achieve high precision with a personal-use-appropriate accuracy of 0.1%. We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples, and the results demonstrate its reliability and consistency. Running on a 48 MHz ARM processor, the algorithm can acquire data within 0.2 seconds. Our low-cost refractometer is suitable for personal health management and small-scale production, providing an affordable and reliable method for measuring sucrose concentration in fruit juice. It improves upon the existing low-cost options by offering better stability and accuracy. This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.

eess.SY↗