arXiv subjects

Fanxin Liu

Publications and source records attributed to Fanxin Liu.

6 recordsLinked to original sources

arXiv2026

Sliding Ferroelectricity Driven Spin-Layertronics in Altermagnetic Multilayers

The synergy of ferroicity with altermagnetism offers a novel platform for designing multifunctional altermagnetic-spintronic device technology. In this work, we propose a mechanism to achieve nonvolatile electrical manipulation of spin and layer degrees of freedom in an altermagnetic bilayer via sliding ferroelectricity. Using first-principles calculations, we show that an interlayer translation can induce a switchable out-of-plane ferroelectric polarization in bilayer CuF2, which directly couples to and reverses the d-wave altermagnetic spin splitting. Notably, the altermangetic spin splitting is layer-locked, the sliding ferroelectricity-driven switching thus embodying a nonvolatile spin-layertronics functionality that couples spin-polarized transport and layer degree of freedom in a single platform. We show that in quadrilayer CuF2, four polarization states are identified which may offer multi-state logic device applications. These findings establish sliding ferroelectricity as a versatile tool for designing voltage-controlled, high-speed and energy-efficient spin-layertronic devices based on altermagnets.

cond-mat.mtrl-sci↗

arXiv2026

Exact dynamics and bound states of a cavity coupled to a two-dimensional reservoir

We demonstrate a robust scheme for quantum information storage based on bound states in a two-dimensional coupled-cavity array. When a target cavity is tuned to resonance with the array, a bound state in the continuum (BIC) emerges, coexisting with two conventional bound states outside the band. The resulting dynamics reflects a delicate interplay between these bound states, which can be fully captured through exact analytical solutions. In the weak-coupling regime, the BIC dominates, enabling perfect and persistent information storage. At stronger coupling, all bound states contribute, leading to oscillatory behavior and reduced storage fidelity. These results, valid at both zero and finite reservoir temperatures and further supported by a single-particle framework, reveal distinctive non-Markovian features in continuous-variable systems and highlight the potential of photonic lattices for scalable all-optical decoherence-free quantum memory platforms.

quant-ph↗

arXiv2020

Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the Convolutional Neural Networks (CNN) and Light Gradient Boosting Machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au+Au collisions with the impact parameter of 0$\leq$$b$$\leq$10 fm at intermediate energies ($E_{\rm lab}$=$0.2$-$1.0$ GeV$/$nucleon) are simulated with the ultrarelativistic quantum molecular dynamics (UrQMD) model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments.

nucl-th↗

arXiv2019

Linear Dichroism Conversion in Quasi One-Dimensional Perovskite Chalcogenide

Anisotropic photonic materials with linear dichroism are crucial components in many sensing, imaging and communication applications. Such materials play an important role as polarizers, filters and wave-plates in photonic devices and circuits. Conventional crystalline materials with optical anisotropy typically show unidirectional linear dichroism over a broad wavelength range. The linear dichroism conversion phenomenon has not been observed in crystalline materials. Here, we report the investigation of the unique linear dichroism conversion phenomenon in quasi-one-dimensional (quasi-1D) hexagonal perovskite chalcogenide BaTiS3. The material shows record level of optical anisotropy within the visible wavelength range. In contrast to conventional anisotropic optical materials, the linear dichroism polarity in BaTiS3 makes an orthogonal change at an optical wavelength corresponding to the photon energy of 1.78 eV. First principle calculations reveal that this anomalous linear dichroism conversion behavior originates from different selection rules of the optical transitions from the parallel bands in the BaTiS3 material. Wavelength dependent polarized Raman spectroscopy further confirms this phenomenon. Such material with linear dichroism conversion property can facilitate new ability to control and sense the energy and polarization of light, and lead to novel photonic devices such as polarization-wavelength selective detectors and lasers for multispectral imaging, sensing and optical communication applications.

cond-mat.mtrl-sci↗

arXiv2019

Machine Learning Approach for Device-Circuit Co-Optimization of Stochastic-Memristive-Device-Based Boltzmann Machine

A Boltzmann machine whose effective "temperature" can be dynamically "cooled" provides a stochastic neural network realization of simulated annealing, which is an important metaheuristic for solving combinatorial or global optimization problems with broad applications in machine intelligence and operations research. However, the hardware realization of the Boltzmann stochastic element with "cooling" capability has never been achieved within an individual semiconductor device. Here we demonstrate a new memristive device concept based on two-dimensional material heterostructures that enables this critical stochastic element in a Boltzmann machine. The dynamic cooling effect in simulated annealing can be emulated in this multi-terminal memristive device through electrostatic bias with sigmoidal thresholding distributions. We also show that a machine-learning-based method is efficient for device-circuit co-design of the Boltzmann machine based on the stochastic memristor devices in simulated annealing. The experimental demonstrations of the tunable stochastic memristors combined with the machine-learning-based device-circuit co-optimization approach for stochastic-memristor-based neural-network circuits chart a pathway for the efficient hardware realization of stochastic neural networks with applications in a broad range of electronics and computing disciplines.

cs.ET↗

arXiv2017

Atomically-thin Femtojoule Filamentary Memristor

The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical towards reducing the operating current, voltage and energy consumption in filamentary type memristors. Previously, the thickness of this filament layer has been limited to above a few nanometers due to processing constraints, making it challenging to further suppress the on-state current and the switching voltage. Here, we study the formation of conductive filaments in a material medium with sub-nanometer thickness, formed through the oxidation of atomically-thin two-dimensional boron nitride. The resulting memristive device exhibits sub-nanometer filamentary switching with sub-pA operation current and femtojoule per bit energy consumption. Furthermore, by confining the filament to the atomic scale, we observe current switching characteristics that are distinct from that in thicker medium due to the profoundly different atomic kinetics. The filament morphology in such an aggressively scaled memristive device is also theoretically explored. These ultra-low energy devices are promising for realizing femtojoule and sub-femtojoule electronic computation, which can be attractive for applications in a wide range of electronics systems that desire ultra-low power operation.

physics.app-ph↗