arXiv subjects

Zhi Yao

Publications and source records attributed to Zhi Yao.

36 records · Page 2Linked to original sources

arXiv2025

Magnon thermal conductivity in multiferroics with spin cycloids

Multiferroic materials, characterized by the occurrence of two or more ferroic properties, hold potential in future technological applications and also exhibit intriguing phenomena caused by the interplay of multiple orders. One such example is the formation of spin cycloid structures within multiferroic materials, which we investigate in this work by focusing on their magnon excitations and transport based on a general multiferroic Hamiltonian with an antiferromagnetic order. More specifically, we identify the ground state and explore the dynamics of magnon modes, revealing distinct in-plane and out-of-plane modes with anisotropic dispersion relations.The magnon modes include a massless excitation, known as the Goldstone boson, originating from the spontaneous breaking of the translational symmetry by the formation of the cycloid structures. By employing the Boltzmann transport formalism, the magnonic thermal conductivity with spin cycloids and low-temperature anisotropic behaviors is discussed. This work provides pathways to envision the spin-textured multiferroics, which may serve as a fertile ground to look for novel thermal and spin transport with the rich interplay of quasiparticles such as magnons and phonons.

cond-mat.mtrl-sci↗

arXiv2024

Symmetry-based phenomenological model for magnon transport in a multiferroic

Magnons carriers of spin information can be controlled by electric fields in the multiferroic BiFeO$_3$ (BFO), a milestone that brings magnons closer to application in future devices. The origin of magnon-spin currents in BFO, however, is not fully understood due to BFO's complicated magnetic texture. In this letter, we present a phenomenological model to elucidate the existence of magnon spin currents in generalized multiferroics by examining the symmetries inherent to their magnetic and polar structures. This model is grounded in experimental data obtained from BFO and its derivatives, which informs the symmetry operations and resultant magnon behavior. By doing so, we address the issue of symmetry-allowed, switchable magnon spin transport in multiferroics, thereby establishing a critical framework for comprehending magnon transport within complex magnetic textures.

cond-mat.mtrl-sci↗

arXiv2024

Design of Programmable Temperature Platform and its Pyroelectrocatalytic applications

The Si based TiO2 thin films were prepared via the combination both of Sol-Gel and Spin-Coating method. The films were sintered at 850 degrees Celsius for half an hour, and the resulting films were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) for their phase composition and microstructure. It was found that the films contained silicon, anatase phase, and unknown impurities. There were holes and micro-cracks on the surface of TiO2 films. A program-controllable hot-cold test chamber was successfully developed and used to study the catalytic performance of the thin film for the first time. The results showed that TiO2 thin films had the ability to degrade Rhodamine B dyes. The highest degradation rate of Rhodamine B achieved 37% after 48 cold-hot cycles. Our design and the experimental results presented in this paper strongly highlight the bright prospects of the thermoelectric properties of TiO2 and water environmental disinfection applications.

cond-mat.mtrl-sci↗

arXiv2024

Optical Neural Engine for Solving Scientific Partial Differential Equations

Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, there is no demonstration of utilizing them for solving PDEs. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell's equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

physics.optics↗

arXiv2024

ELEQTRONeX: A GPU-Accelerated Exascale Framework for Non-Equilibrium Quantum Transport in Nanomaterials

Non-equilibrium electronic quantum transport is crucial for the operation of existing and envisioned electronic, optoelectronic, and spintronic devices. The ultimate goal of encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulation approach has traditionally been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively-parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green's function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve orders of magnitude improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors. We also demonstrate that ELEQTRONeX is suitable for complex device/material geometries where periodic approaches are not feasible, such as modeling of arrays of misaligned carbon nanotubes requiring fully 3D simulations.

physics.comp-ph↗

arXiv2024

VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs.

cs.AI↗

arXiv2024

Non-volatile spin transport in a single domain multiferroic

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic and polar order imposes yet another boundary condition on spin transport. Thus, understanding the fundamentals of spin transport in such systems requires a single domain, a single crystal. We show that through Lanthanum(La) substitution, a single ferroelectric domain can be engineered with a stable, single-variant spin cycloid, controllable by an electric field. The spin transport in such a single domain displays a strong anisotropy, arising from the underlying spin cycloid lattice. Our work shows a pathway to understand the fundamental origins of spin transport in such a single domain multiferroic.

cond-mat.mtrl-sci↗

arXiv2024

3D ferroelectric phase field simulations of polycrystalline multi-phase hafnia and zirconia based ultra-thin films

HfO$_2$- and ZrO$_2$-based ferroelectric thin films have emerged as promising candidates for the gate oxides of next generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o-) phase mixture with partially in-plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi-phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here we apply 3D phase field simulations of HfO$_2$- and ZrO$_2$-based mixed-phase ultra-thin films on silicon to understand the necessary and beneficial conditions for NC stabilization. We find that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out-of-plane direction enhances the NC effect. Furthermore, we show that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, we conclude that topological domain walls play a critical role in experimentally observed NC phenomena in HfO$_2$- and ZrO$_2$-based ferroelectrics.

cond-mat.mes-hall↗

arXiv2024

Two-dimensional Closed-Form Analytical Model of Laterally-Excited Film Bulk Acoustic Wave Resonator Multiferroic Antennas

To overcome the physical limitations of electrically small antennas, strain-mediated magnetoelectric antennas have been studied experimentally and theoretically. However, current closed-form analytical models include solely one-dimensional approaches. This paper proposes a two-dimensional closed-form analytical model of a laterally-excited multiferroic antenna. To model thickness-shear vibrations, we propose an extended Mason model to two dimensions. This theory solves unidirectionally-coupled elastodynamics-magnetization dynamics-electrodynamics. Without using the infinitesimal dipole approximation, this model provides a closed-form solution of the radiated electromagnetic field with an explicit dependency of the antenna response on the physical parameters, enabling their optimization. Furthermore, we compare the efficiency-bandwidth product of different multiferroic antennas with Chu's limit in the Akhiezer regime, which delimits the highest achievable mechanical quality factor for acoustic resonators at GHz frequencies. We show that phonon-phonon coupling of Akhiezer damping is most likely to limit multiferroic antenna radiation, not Chu's limit. Moreover, judicious choice of materials can enhance the maximum efficiency by more than an order of magnitude.

physics.app-ph↗

arXiv2024

Designed spin-texture-lattice to control anisotropic magnon transport in antiferromagnets

Spin waves in magnetic materials are promising information carriers for future computing technologies due to their ultra-low energy dissipation and long coherence length. Antiferromagnets are strong candidate materials due, in part, to their stability to external fields and larger group velocities. Multiferroic aniferromagnets, such as BiFeO$_3$ (BFO), have an additional degree of freedom stemming from magnetoelectric coupling, allowing for control of the magnetic structure, and thus spin waves, with electric field. Unfortunately, spin-wave propagation in BFO is not well understood due to the complexity of the magnetic structure. In this work, we explore long-range spin transport within an epitaxially engineered, electrically tunable, one-dimensional (1D) magnonic crystal. We discover a striking anisotropy in the spin transport parallel and perpendicular to the 1D crystal axis. Multiscale theory and simulation suggests that this preferential magnon conduction emerges from a combination of a population imbalance in its dispersion, as well as anisotropic structural scattering. This work provides a pathway to electrically-reconfigurable magnonic crystals in antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2023

Manipulating chiral-spin transport with ferroelectric polarization

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics.

physics.app-ph↗

arXiv2023

Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework

In this work, we implement a new London equation module for superconductivity in the GPU-enabled ARTEMIS framework, and couple it to a finite-difference time-domain solver for Maxwell's equations. We apply this two-fluid approach to model a superconducting coplanar waveguide (CPW) resonator. We validate our implementation by verifying that the theoretical skin depth and reflection coefficients can be obtained for several superconductive materials, with different London penetration depths, over a range of frequencies. Our convergence studies show that the algorithm is second-order accurate in both space and time, except at superconducting interfaces where the approach is spatially first-order. In our CPW simulations, we leverage the GPU scalability of our code to compare the two-fluid model to more traditional approaches that approximate superconducting behavior and demonstrate that superconducting physics can show comparable performance to the assumption of quasi-infinite conductivity as measured by the Q-factor.

physics.comp-ph↗

arXiv2022

Characterization of Transmission Lines in Microelectronics Circuits using the ARTEMIS Solver

Modeling and characterization of electromagnetic wave interactions with microelectronic devices to derive network parameters has been a widely used practice in the electronic industry. However, as these devices become increasingly miniaturized with finer-scale geometric features, computational tools must make use of manycore/GPU architectures to efficiently resolve length and time scales of interest. This has been the focus of our open-source solver, ARTEMIS (Adaptive mesh Refinement Time-domain ElectrodynaMIcs Solver), which is performant on modern GPU-based supercomputing architectures while being amenable to additional physics coupling. This work demonstrates its use for characterizing network parameters of transmission lines using established techniques. A rigorous verification and validation of the workflow is carried out, followed by its application for analyzing a transmission line on a CMOS chip designed for a photon-detector application. Simulations are performed for millions of timesteps on state-of-the-art GPU resources to resolve nanoscale features at gigahertz frequencies. The network parameters are used to obtain phase delay and characteristic impedance that serve as inputs to SPICE models. The code is demonstrated to exhibit ideal weak scaling efficiency up to 1024 GPUs and 84% efficiency for 2048 GPUs, which underscores its use for network analysis of larger, more complex circuit devices in the future

physics.comp-ph↗

arXiv2022

FerroX : A GPU-accelerated, 3D Phase-Field Simulation Framework for Modeling Ferroelectric Devices

We present a massively parallel, 3D phase-field simulation framework for modeling ferro-electric materials based scalable logic devices. We self-consistently solve the time-dependent Ginzburg Landau (TDGL) equation for ferroelectric polarization, Poisson equation for electric potential, and charge equation for carrier densities in semiconductor regions. The algorithm is implemented using the AMReX software framework, which provides effective scalability on manycore and GPU-based supercomputing architectures. We demonstrate the performance of the algorithm with excellent scaling results on NERSC multicore and GPU systems, with a significant (15x) speedup on the GPU using a node-by-node comparison. We further demonstrate the applicability of the code in simulations of ferroelectric domain-wall induced negative capacitance (NC) effect in Metal-Ferroelectric-Insulator-Metal (MFIM) and Metal-Ferroelectric-Insulator-Semiconductor-Metal (MFISM) devices. The charge (Q) v.s. applied voltage (V) responses for these structures clearly indicates stabilized negative capacitance.

cs.CE↗

arXiv2021

Enhanced Planar Antenna Efficiency Through Magnetic Thin-Films

This work proposes to use magnetic material as the substrate of planar antennas to overcome the platform effect caused by the conducting ground plane. The upper bound of the radiation efficiency of an electric-current-driven low-profile antenna is theoretically derived, which is inversely proportional to the Gilbert damping factor of the magnetic material. Meanwhile, the improvement of radiation due to the use of magnetic material is demonstrated by a three-dimensional (3D) multiphysics and multiscale time-domain model. The simulation results match the theoretical derivation, showing 25% radiation efficiency from a planar antenna backed by a FeGaB thin film with 2.56 um thickness. Furthermore, for conductive ferromagnetic materials, it is shown that the eddy current loss can be well suppressed by laminating the thin film into multiple layers. The radiation efficiency of the modeled antenna with a conductive ferromagnetic substrate is improved from 2.2% to 11.8% by dividing the substrate into 10 layers, with a ferromagnetic material fill factor of 93%.

cond-mat.mtrl-sci↗

arXiv2021

A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver

We present a new, high-performance coupled electrodynamics-micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain (FDTD) approach for Maxwell's equations to a magnetization model described by the Landau-Lifshitz-Gilbert (LLG) equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, primarily developed for plasma wakefield accelerator modeling. Our novel temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59x) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.

physics.comp-ph↗

arXiv2020

Resonant Precession Modulation Based Magnetic Field Receivers

Antenna technology provides the crucial interface between electronic devices and electromagnetic waves required for wireless communications. This technology has operated under largely the same general principles for over a century, resulting in stagnation of advancements in modern times and fundamentally limiting the extent to which potential applications of wireless communications may be realized. Herein we propose a novel concept for antenna operation employing the nonlinear precession dynamics of subatomic spins. An electromagnetic wave incident on a precessing spin will serve to modulate the spin frequency of precession. Antennas operating based on a detection of these spin dynamics are then able to fully characterize the incident wave while achieving substantial amplifications, higher sensitivities, and smaller form factors as compared to conventional antennas. A preliminary experimental implementation of the proposed concept establishes its feasibility and is already able to demonstrate significant amplification and size benefits.

physics.app-ph↗

arXiv1999

(2+1)-Dimensional Fermion Determinant in a Constant Background Field at Finite Temperature and Density

We evaluate exactly both the non-relativistic and relativistic fermion determinant in 2+1 dimensions in a constant background field at finite temperature. The effect of finite chemical potential is also considered. In both cases, the systems are decoupled into an infinite number of 1+1 fermions by Fourier transformation in the $β$-variable. The total effective actions demonstrate non-extensiveness in the $β$ dimension.

hep-th↗