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Jian Chen

Publications and source records attributed to Jian Chen.

At least 217 records · Page 12Linked to original sources

Direct Implicit and Explicit Energy-Conserving Particle-in-Cell Methods for Modeling of Capacitively-Coupled Plasma Devices

Achieving large-scale kinetic modelling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving Particle-In-Cell method suffers from restrictive stability constraints on spatial cell size and temporal time step, requiring resolution of the electron Debye length and electron plasma period respectively. This results in a very high computational cost, making the technique prohibitive for large volume device modeling. We investigate the Direct Implicit algorithm and the explicit Energy Conserving algorithm as alternatives to the standard approach, both of which can reduce computational cost with a minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated via 2D capacitively coupled plasma discharge simulations. The investigation revels that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in reduced runtime, while incurring only minor inaccuracies in plasma parameters. The Direct Implicit method also allows for time steps larger than the electron plasma period, however care must be taken to avoid numerical heating or cooling. It is demonstrated that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to an acceptable level.

physics.plasm-ph↗

SimSched: A tool for Simulating Autosar Implementaion in Simulink

AUTOSAR (AUTomotive Open System ARchitecture) is an open industry standard for the automotive sector. It defines the three-layered automotive software architecture. One of these layers is the application layer, where functional behaviors are encapsulated in Software Components (SW-Cs). Inside SW-Cs, a set of runnable entities represents the internal behavior and is realized as a set of tasks. To address AUTOSAR's lack of support for modeling behaviors of runnables, languages such as Simulink are employed. Simulink simulations assume Simulink block behaviors are completed in zero execution time, while real execution requires a finite execution time. This timing mismatch can result in failures to detect unexpected runtime behaviors during the simulation phase. This paper extends the Simulink environment to model the timing properties of tasks. We present a Simulink block that can schedule tasks with non-zero simulation times. It enables a more realistic analysis during model development.

cs.SE↗

On the Convergence of Newton-type Proximal Gradient Method for Multiobjective Optimization Problems

In a recent study, Ansary (Optim Methods Softw 38(3):570-590,2023) proposed a Newton-type proximal gradient method for nonlinear multiobjective optimization problems (NPGMO). However, the favorable convergence properties typically associated with Newton-type methods were not established for NPGMO in Ansary's work. In response to this gap, we develop a straightforward framework for analyzing the convergence behavior of the NPGMO. Specifically, under the assumption of strong convexity, we demonstrate that the NPGMO enjoys quadratic termination, superlinear convergence, and quadratic convergence for problems that are quadratic, twice continuously differentiable and twice Lipschitz continuously differentiable, respectively.

math.OC↗

Extremely thin perfect absorber by generalized multipole bianisotropic effect

Symmetry breaking plays a crucial role in understanding the fundamental physics underlying numerous physical phenomena, including the electromagnetic response in resonators, giving rise to intriguing effects such as directional light scattering, supercavity lasing, and topologically protected states. In this work, we demonstrate that adding a small fraction of lossy metal (as low as $1\times10^{-6}$ in volume), to a lossless dielectric resonator breaks inversion symmetry thereby lifting its degeneracy, leading to a strong bianisotropic response. In the case of the metasurface composed of such resonators, this effect leads to unidirectional perfect absorption while maintaining nearly perfect reflection from the opposite direction. We have developed more general Onsager-Casimir relations for the polarizabilities of particle arrays, taking into account the contributions of quadrupoles, which shows that bianisotropy is not solely due to dipoles, but also involves high-order multipoles. Our experimental validation demonstrates an extremely thin terahertz-perfect absorber with a wavelength-to-thickness ratio of up to 25,000, where the material thickness is only 2% of the theoretical minimum thickness dictated by the fundamental limit. Our findings have significant implications for a variety of applications, including energy harvesting, thermal management, single-photon detection, and low-power directional emission.

physics.optics↗

Barzilai-Borwein Proximal Gradient Methods for Multiobjective Composite Optimization Problems with Improved Linear Convergence

When minimizing a multiobjective optimization problem (MOP) using multiobjective gradient descent methods, the imbalances among objective functions often decelerate the convergence. In response to this challenge, we propose two types of the Barzilai-Borwein proximal gradient method for multi-objective composite optimization problems (BBPGMO). We establish convergence rates for BBPGMO, demonstrating that it achieves rates of $O(\frac{1}{\sqrt{k}})$, $O(\frac{1}{k})$, and $O(r^{k})(0<r<1)$ for non-convex, convex, and strongly convex problems, respectively. Furthermore, we show that BBPGMO exhibits linear convergence for MOPs with several linear objective functions. Interestingly, the linear convergence rate of BBPGMO surpasses the existing convergence rates of first-order methods for MOPs, which indicates its enhanced performance and its ability to effectively address imbalances from theoretical perspective. Finally, we provide numerical examples to illustrate the efficiency of the proposed method and verify the theoretical results.

math.OC↗

Oxidation kinetics and non-Marcusian charge transfer in dimensionally confined semiconductors

Electrochemical reactions represent essential processes in fundamental chemistry that foster a wide range of applications. Although most electrochemical reactions in bulk substances can be well described by the classical Marcus-Gerischer charge transfer theory, the realistic reaction character and mechanism in dimensionally confined systems remain unknown. Here, we report the multiparametric survey on the kinetics of lateral photooxidation in structurally identical WS2 and MoS2 monolayers, where electrochemical oxidation occurs at the atomically thin monolayer edges. The oxidation rate is correlated quantitatively with various crystallographic and environmental parameters, including the density of reactive sites, humidity, temperature, and illumination fluence. In particular, we observe distinctive reaction barriers of 1.4 and 0.9 eV for the two structurally identical semiconductors and uncover an unusual non-Marcusian charge transfer mechanism in these dimensionally confined monolayers due to the limit in reactant supplies. A scenario of band bending is proposed to explain the discrepancy in reaction barriers. These results add important knowledge into the fundamental electrochemical reaction theory in low-dimensional systems.

cond-mat.mtrl-sci↗

Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective

The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achieve excellent results in detecting bots. Recently, more and more GNN-based methods have been proposed for bot detection. However, the existing GNN-based bot detection methods only focus on low-frequency information and seldom consider high-frequency information, which limits the representation ability of the model. To address this issue, this paper proposes a Multi-scale with Signed-attention Graph Filter for social bot detection called MSGS. MSGS could effectively utilize both high and low-frequency information in the social graph. Specifically, MSGS utilizes a multi-scale structure to produce representation vectors at different scales. These representations are then combined using a signed-attention mechanism. Finally, multi-scale representations via MLP after polymerization to produce the final result. We analyze the frequency response and demonstrate that MSGS is a more flexible and expressive adaptive graph filter. MSGS can effectively utilize high-frequency information to alleviate the over-smoothing problem of deep GNNs. Experimental results on real-world datasets demonstrate that our method achieves better performance compared with several state-of-the-art social bot detection methods.

cs.CV↗

Intrinsic Electronic Properties of BN Encapsulated, van der Waals Contacted MoSe$_2$ FETs

Two-dimensional (2D) semiconductors have attracted considerable interest for their unique physical properties. Here, we report the intrinsic cryogenic electronic transport properties in few-layer MoSe$_2$ field-effect transistors (FETs) that are simultaneously van der Waals contacted with gold electrodes and are fully encapsulated in ultraclean hexagonal boron nitride dielectrics. The FETs exhibit electronically favorable channel/dielectric interfaces with low densities of interfacial traps ($10^{10}\,$cm$^{-2}$), which lead to outstanding device characteristics at room temperature, including a near-Boltzmann-limit subthreshold swings ($\lt65\,$mV/dec), a high carrier mobility ($68\,$cm$^{2}\cdot$V$^{-1}\cdot$s$^{-1}$), and a negligible scanning hysteresis ($\lt15\,$mV). The dependence of various contact-related quantities on temperature and carrier density are also systematically characterized to understand the van der Waals contacts between gold and MoSe$_2$. The results provide insightful information on the device physics in van der Waals contacted and encapsulated 2D FETs.

cond-mat.mtrl-sci↗

Clean BN encapsulated 2D FETs with lithography compatible contacts

Device passivation through ultraclean hexagonal BN encapsulation is proven one of the most effective ways for constructing high-quality devices with atomically thin semiconductors that preserves the ultraclean interface quality and intrinsic charge transport behavior. However, it remains challenging to integrate lithography compatible contact electrodes with flexible distributions and patterns. Here, we report the feasibility in straightforwardly integrating lithography defined contacts into BN encapsulated 2D FETs, giving rise to overall device quality comparable to the state-of-the-art results from the painstaking pure dry transfer processing. Electronic characterization on FETs consisting of WSe$_2$ and MoS$_2$ channels reveals an extremely low scanning hysteresis of ca. 2 mV on average, a low density of interfacial charged impurity of ca. $10^{11}\,$cm$^{-2}$, and generally high charge mobilities over $1000\,$cm$^{2}\cdot$V$^{-1}\cdot$s$^{-1}$ at low temperatures. The overall high device qualities verify the viability in directly integrating lithography defined contacts into BN encapsulated devices to exploit their intrinsic charge transport properties for advanced electronics.

cond-mat.mes-hall↗

Electrical contact properties between Yb and few-layer WS$_2$

Charge injection mechanism from contact electrodes into two-dimensional (2D) dichalcogenides is an essential topic for exploiting electronics based on 2D channels, but remains not well understood. Here, low-work-function metal ytterbium (Yb) was employed as contacts for tungsten disulfide (WS$_2$) to understand the realistic injection mechanism. The contact properties in WS$_2$ with variable temperature (T) and channel thickness (tch) were synergetically characterized. It is found that the Yb/WS$_2$ interfaces exhibit a strong pinning effect between energy levels and a low contact resistance ($R_\rm{C}$) value down to $5\,kΩ\cdotμ$m. Cryogenic electrical measurements reveal that $R_\rm{C}$ exhibits weakly positive dependence on T till 77 K, as well as a weakly negative correlation with tch. In contrast to the non-negligible $R_\rm{C}$ values extracted, an unexpectedly low effective thermal injection barrier of 36 meV is estimated, indicating the presence of significant tunneling injection in subthreshold regime and the inapplicability of the pure thermionic emission model to estimate the height of injection barrier.

cond-mat.mtrl-sci↗

Enhancement of Carrier Mobility in Semiconductor Nanostructures by Carrier Distribution Engineering

Two-dimensional (2D) van der Waals semiconductors are appealing for low-power transistors. Here, we show the feasibility in enhancing carrier mobility in 2D semiconductors through engineering the vertical distribution of carriers confined inside the ultrathin channels via symmetrizing gate configuration or increasing channel thickness. Through self-consistently solving the Schrödinger-Poisson equations, the shapes of electron envelope functions are extensively investigated by clarifying their relationship with gate configuration, channel thickness, dielectric permittivity, and electron density. The impacts of electron distribution variation on various carrier scattering matrix elements and overall carrier mobility are insightfully clarified. It is found that the carrier mobility can be generally enhanced in the dual-gated configuration due to the centralization of carrier redistribution in the nanometer-thick semiconductor channels and the rate of increase reaches up to 23% in HfO$_2$ dual-gated 10-layer MoS$_2$ channels. This finding represents a viable strategy for performance optimization in transistors consisting of 2D semiconductors.

cond-mat.mtrl-sci↗

Wave breaking in the unidirectional non-local wave model

In this paper we study wave breaking in the unidirectional non-local wave model describing the motion of a collision-free plasma in a magnetic field. By analyzing the monotonicity and continuity properties of a system of the Riccati-type differential inequalities involving the extremal slopes of flows, we show a new sufficient condition on the initial data to exhibit wave breaking. Moreover, the estimates of life span and wave breaking rate are derived.

math.AP↗

Proton Collective Quantum Tunneling Induces Anomalous Thermal Conductivity of Ice under Pressure

Proton tunneling is believed to be non-local in ice but has never been shown experimentally. Here we measured thermal conductivity of ice under pressure up to 50 GPa and found it to increase with pressure until 20 GPa but decrease at higher pressures. We attribute this anomalous drop of thermal conductivity to the collective tunneling of protons at high pressures, supported by large-scale quantum molecular dynamics simulations. The collective tunneling loops span several picoseconds in time and are as large as nanometers in space, which match the phonon periods and wavelengths, leading to strong phonon scattering at high pressures. Our results show direct evidence of collective quantum motion existing in high-pressure ice and provide a new perspective to understanding the coupling between phonon propagation and atomic tunneling.

physics.chem-ph↗

Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)-Assisted Vehicular Networks

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

cs.DC↗

Simultaneous Wireless Information and Power Transfer in Near-Field Communications

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing of the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve the near-optimal performance; and 2) in contract to the far-field SWIPT, single near-field beamformer can focus the energy on multiple locations.

cs.IT↗

NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings.

cs.AR↗

RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection

The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the performance of weak classifiers, it cannot utilize the interaction between accounts. This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model. Specifically, different subgraphs are constructed as different training sets through node sampling, feature selection, and edge dropout. Then, GNN base classifiers are trained using various subgraphs, and the remaining features are used for training Fully Connected Netural Network (FCN). The outputs of GNN and FCN are aligned in each branch. Finally, the outputs of all branches are aggregated to produce the final result. Moreover, RF-GNN is compatible with various widely-used GNNs for node classification. Extensive experimental results demonstrate that the proposed method obtains better performance than other state-of-the-art methods.

cs.LG↗

Physical Layer Security in Near-Field Communications

A near-field secure transmission framework is proposed. Employing the hybrid beamforming architecture, a multi-antenna base station (BS) transmits confidential information to a multi-antenna legitimate user (U) against a multi-antenna eavesdropper (E) in the near field. A two-stage algorithm is proposed to maximize the near-field secrecy capacity. Based on the fully-digital beamformers obtained in the first stage, the optimal analog beamformers and baseband digital beamformers can be alternatingly derived in the closed-form expressions in the second stage. Numerical results demonstrate that in contrast to the far-field secure communication relying on the angular disparity, the near-field secure communication mainly relies on the distance disparity between U and E.

cs.IT↗