arXiv subjects

Xiaoyang Lin

Publications and source records attributed to Xiaoyang Lin.

At least 19 recordsLinked to original sources

arXiv2026

Morphology-Guided Deterministic Fabrication of Low-Noise High-Temperature Superconducting Quantum Interference Devices

Reproducible bicrystal high-temperature superconducting quantum interference devices remain limited by local variability along the grain boundaries that form the Josephson junctions. Here, we develop a site-selective fabrication workflow in which atomic force microscopy maps the intended junction region before lithography, quantifies an apparent grain-boundary width, rejects pore-rich segments, and writes a nearby registration mark for site-specific pattern alignment. The apparent grain-boundary width provides a practical morphology metric, with narrower regions consistently yielding larger critical currents and characteristic voltages. Iterative optimization within this workflow further improves junction and device performance, reaching a liquid-nitrogen-temperature field-noise level of 40 fT Hz^(-1/2). This strategy turns local grain-boundary heterogeneity from an uncontrolled source of variability into a basis for site-selective fabrication, providing a route towards scalable manufacturing of low-noise HTS SQUIDs with high uniformity.

cond-mat.mes-hall↗

arXiv2026

Momentum-Resolved Tunneling Modulation Induced Giant Multistate Resistance in Antiferroelectric Multiferroic Junction

Multiferroic tunnel junctions (MFTJs), integrating ferroelectric and ferromagnetic functionalities within a single nanoscale device, hold significant promise for non-volatile, multi-state memory and innovative computing paradigms. In conventional MFTJs, tunneling resistance modulation relies primarily on ferroelectric (FE) polarization switching, which alters interfacial electric fields and shifts the Fermi level of adjacent ferromagnetic electrodes. However, achieving high tunnelelectroresistance (TER) through this approach demands strong built-in electric fields, which simultaneously hinder FE polarization switching, creating an intrinsic trade-off between reliable data reading and efficient writing. Here, we propose a dual mechanism that combines antiferroelectric (AFE) phase-transition modulation of the evanescent decay states with interfacial spin filtering based on $Fe_3GaTe_2$/bilayer-$In_2Se_3$/$Fe_3GaTe_2$ heterostructure. Beyond altering the electrostatic potential as in AFE-FE switching, the transitions between head-type and tail-type AFE states preserve the centrosymmetric potential profile yet fundamentally modulate the momentum-resolved distribution of evanescent decay rates across the Brillouin zone. When integrated with perfect spin filtering at the $Fe_3GaTe_2$/$\alpha$-$In_2Se_3$ interface, this mechanism yields a giant TER (~$7.6\times10^3\%$), over 4 times that of conventional FE-based MFTJs, and a TMR exceeding $6.8\times10^5\%$, enhanced by two orders of magnitude over typical MFTJs. These mechanisms resolve the performance trade-off in MFTJs, enabling six distinct non-volatile resistance states at room temperature.

cond-mat.mes-hall↗

arXiv2025

Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering

The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements through refining the message passing schemes in GNN models or exploiting various data augmentation techniques to mitigate limited supervision. In real graphs, nodes often tend to form tightly-knit communities/clusters, which embody abundant signals for compensating label scarcity in semi-supervised node classification but are not explored in prior methods. Inspired by this, this paper presents NCGC that integrates self-supervised graph clustering and semi-supervised classification into a unified framework. Firstly, we theoretically unify the optimization objectives of GNNs and spectral graph clustering, and based on that, develop soft orthogonal GNNs (SOGNs) that leverage a refined message passing paradigm to generate node representations for both classification and clustering. On top of that, NCGC includes a self-supervised graph clustering module that enables the training of SOGNs for learning representations of unlabeled nodes in a self-supervised manner. Particularly, this component comprises two non-trivial clustering objectives and a Sinkhorn-Knopp normalization that transforms predicted cluster assignments into balanced soft pseudo-labels. Through combining the foregoing clustering module with the classification model using a multi-task objective containing the supervised classification loss on labeled data and self-supervised clustering loss on unlabeled data, NCGC promotes synergy between them and achieves enhanced model capacity. Our extensive experiments showcase that the proposed NCGC framework consistently and considerably outperforms popular GNN models and recent baselines for semi-supervised node classification on seven real graphs, when working with various classic GNN backbones.

cs.LG↗

arXiv2025

Effective Clustering for Large Multi-Relational Graphs

Multi-relational graphs (MRGs) are an expressive data structure for modeling diverse interactions/relations among real objects (i.e., nodes), which pervade extensive applications and scenarios. Given an MRG G with N nodes, partitioning the node set therein into K disjoint clusters (MRGC) is a fundamental task in analyzing MRGs, which has garnered considerable attention. However, the majority of existing solutions towards MRGC either yield severely compromised result quality by ineffective fusion of heterogeneous graph structures and attributes, or struggle to cope with sizable MRGs with millions of nodes and billions of edges due to the adoption of sophisticated and costly deep learning models. In this paper, we present DEMM and DEMM+, two effective MRGC approaches to address the limitations above. Specifically, our algorithms are built on novel two-stage optimization objectives, where the former seeks to derive high-caliber node feature vectors by optimizing the multi-relational Dirichlet energy specialized for MRGs, while the latter minimizes the Dirichlet energy of clustering results over the node affinity graph. In particular, DEMM+ achieves significantly higher scalability and efficiency over our based method DEMM through a suite of well-thought-out optimizations. Key technical contributions include (i) a highly efficient approximation solver for constructing node feature vectors, and (ii) a theoretically-grounded problem transformation with carefully-crafted techniques that enable linear-time clustering without explicitly materializing the NxN dense affinity matrix. Further, we extend DEMM+ to handle attribute-less MRGs through non-trivial adaptations. Extensive experiments, comparing DEMM+ against 20 baselines over 11 real MRGs, exhibit that DEMM+ is consistently superior in terms of clustering quality measured against ground-truth labels, while often being remarkably faster.

cs.LG↗

arXiv2025

Dimensionality Enhanced Out-of-Plane Spin Currents in NbIrTe$_4$ for Efficient Field-Free Switching of Perpendicular Magnetization

Efficient generation of out-of-plane (OOP) spin currents is crucial for advanced spintronic memory applications. However, the theoretical understanding and experimental implementation of robust OOP spin currents for high-density and low-power magnetization switching remain significant challenges of spintronics. Here, we demonstrate that transitioning NbIrTe$_4$ from a two-dimensional quantum spin Hall insulator to a three-dimensional type-II Weyl semimetal markedly enhances OOP spin current generation. The bulk topological Weyl semimetal nature of NbIrTe$_4$, characterized by its Weyl cone, significantly enhances the OOP spin Berry curvature, enabling an unprecedented OOP spin Hall conductivity exceeding $10^5\hbar/2e$ $\Omega^{-1}m^{-1} $. This enhancement, surpassing the in-plane component by more than fourfold, enables efficient and field-free spin-orbit torque (SOT) switching of perpendicular magnetization with a low current density of 1.4 MA/cm$^2$. The improved spin Hall conductivity reduces the overall power consumption by more than two orders of magnitude compared to existing systems, such as heavy metals. Our findings highlight the pivotal role of dimensionality in harnessing robust OOP spin currents in topological Weyl semimetals, paving the way for the development of high-density, low-power spintronic memory technologies.

cond-mat.mes-hall↗

arXiv2024

Spectral Subspace Clustering for Attributed Graphs

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency

cs.SI↗

arXiv2024

Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks

In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs follow the message-passing paradigm, where representations of each node are learned by recursively aggregating features of its neighbors. However, this mechanism brings severe over-smoothing and efficiency issues over high-degree graphs (HDGs), wherein most nodes have dozens (or even hundreds) of neighbors, such as social networks, transaction graphs, power grids, etc. Additionally, such graphs usually encompass rich and complex structure semantics, which are hard to capture merely by feature aggregations in GNNs. Motivated by the above limitations, we propose TADA, an efficient and effective front-mounted data augmentation framework for GNNs on HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. The former obtains augmented node features and enhanced model capacity by encoding the graph structure into high-quality structure embeddings with our highly-efficient sketching method. Further, by exploiting task-relevant features extracted from graph structures and attributes, the second module enables the accurate identification and reduction of numerous redundant/noisy edges from the input graph, thereby alleviating over-smoothing and facilitating faster feature aggregations over HDGs. Empirically, TADA considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification, while achieving efficient training and inference processes.

cs.LG↗

arXiv2024

Effective Clustering on Large Attributed Bipartite Graphs

Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains, including social network analysis, recommendation systems, information retrieval, and bioinformatics. However, the majority of existing solutions towards k-ABGC either overlook attribute information or fail to capture bipartite graph structures accurately, engendering severely compromised result quality. The severity of these issues is accentuated in real ABGs, which often encompass millions of nodes and a sheer volume of attribute data, rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGC that achieves superb clustering performance on multiple real datasets. TPO obtains high clustering quality through two major contributions: (i) a novel formulation and transformation of the k-ABGC problem based on multi-scale attribute affinity specialized for capturing attribute affinities between nodes with the consideration of their multi-hop connections in ABGs, and (ii) a highly efficient solver that includes a suite of carefully-crafted optimizations for sidestepping explicit affinity matrix construction and facilitating faster convergence. Extensive experiments, comparing TPO against 19 baselines over 5 real ABGs, showcase the superior clustering quality of TPO measured against ground-truth labels. Moreover, compared to the state of the arts, TPO is often more than 40x faster over both small and large ABGs.

cs.SI↗

arXiv2024

Interlayer Dzyaloshinskii-Moriya interaction in synthetic ferrimagnets

The antisymmetric interlayer exchange interaction, i.e., interlayer Dzyaloshinskii-Moriya interaction (IL-DMI) has attracted significant interest since this long-range chiral spin interaction provides a new dimension for controlling spin textures and dynamics. However, the role of IL-DMI in the field induced and spin-orbit torque (SOT) induced switching of synthetic ferrimagnets (SFi) has not been uncovered. Here, we exploit interlayer chiral exchange bias fields in SFi to address both the sign and magnitude of the IL-DMI. Depending on the degree of imbalance between the two magnetic moments of the SFi, the amount of asymmetry, addressed via loop shifts of the hysteresis loops under an in-plane field reveals a unidirectional and chiral nature of the IL-DMI. The devices are then tested with SOT switching experiments and the process is examined via both transient state and steady state detection. In addition to field-free SOT switching, we find that the combination of IL-DMI and SOT give rise to multi-resistance states, which provides a possible direction for the future design of neuromorphic computing devices based on SOT. This work is a step towards characterizing and understanding the IL-DMI for spintronic applications.

physics.app-ph↗

arXiv2023

Ultralow power and shifting-discretized magnetic racetrack memory device driven by chirality switching and spin current

Magnetic racetrack memory has significantly evolved and developed since its first experimental verification and is considered as one of the most promising candidates for future high-density on-chip solid state memory. However, the lack of a fast and precise magnetic domain wall (DW) shifting mechanism and the required extremely high DW motion (DWM) driving current both make the racetrack difficult to commercialize. Here, we propose a method for coherent DWM that is free from above issues, which is driven by chirality switching (CS) and an ultralow spin-orbit-torque (SOT) current. The CS, as the driving force of DWM, is achieved by the sign change of DM interaction which is further induced by a ferroelectric switching voltage. The SOT is used to break the symmetry when the magnetic moment is rotated to the Bloch direction. We numerically investigate the underlying principle and the effect of key parameters on the DWM through micromagnetic simulations. Under the CS mechanism, a fast (102 m/s), ultralow energy (5 attojoule), and precisely discretized DWM can be achieved. Considering that skyrmions with topological protection and smaller size are also promising for future racetrack, we similarly evaluate the feasibility of applying such a CS mechanism to a skyrmion. However, we find that the CS only causes it to "breathe" instead of moving. Our results demonstrate that the CS strategy is suitable for future DW racetrack memory with ultralow power consumption and discretized DWM.

cond-mat.mes-hall↗

arXiv2022

Spin Manipulation by Giant Valley-Zeeman Spin-Orbit Field in Atom-Thick WSe2

The phenomenon originating from spin-orbit coupling (SOC) provides energy-efficient strategies for spin manipulation and device applications. The broken inversion symmetry interface and resulting electric field induce a Rashba-type spin-orbit field (SOF), which has been demonstrated to generate spin-orbit torque for data storage applications. In this study, we found that spin flipping can be achieved by the valley-Zeeman SOF in monolayer WSe2 at room temperature, which manifests as a negative magnetoresistance in the vertical spin valve. Quantum transmission calculations based on an effective model near the K valley of WSe2 confirm the precessional spin transport of carriers under the giant SOF, which is estimated to be 650 T. In particular, the valley-Zeeman SOF-induced spin dynamics was demonstrated to be tunable with the layer number and stacking phase of WSe2 as well as the gate voltage, which provides a novel strategy for spin manipulation and can benefit the development of ultralow-power spintronic devices.

cond-mat.mes-hall↗

arXiv2020

Picosecond Switching of Optomagnetic Tunnel Junctions

Perpendicular magnetic tunnel junctions are one of the building blocks for spintronic memories, which allow fast nonvolatile data access, offering substantial potentials to revolutionize the mainstream computing architecture. However, conventional switching mechanisms of such devices are fundamentally hindered by spin polarized currents4, either spin transfer torque or spin orbit torque with spin precession time limitation and excessive power dissipation. These physical constraints significantly stimulate the advancement of modern spintronics. Here, we report an optomagnetic tunnel junction using a spintronic-photonic combination. This composite device incorporates an all-optically switchable Co/Gd bilayer coupled to a CoFeB/MgO-based perpendicular magnetic tunnel junction by the Ruderman-Kittel-Kasuya-Yosida interaction. A picosecond all-optical operation of the optomagnetic tunnel junction is explicitly confirmed by time-resolved measurements. Moreover, the device shows a considerable tunnel magnetoresistance and thermal stability. This proof-of-concept device represents an essential step towards ultrafast spintronic memories with THz data access, as well as ultralow power consumption.

cond-mat.mes-hall↗

arXiv2019

Universal transfer and stacking technique of van der Waals heterostructures for spintronics

The key to achieving high-quality van der Waals heterostructure devices made from various two-dimensional (2D) materials lies in the control over clean and flexible interfaces. However, existing transfer methods based on different mediators possess insufficiencies including the presence of residues, the unavailability of flexible interface engineering, and the selectivity towards materials and substrates since their adhesions differ considerably with the various preparation conditions, from chemical vapor deposition (CVD) growth to mechanical exfoliation. In this paper, we introduce a more universal method using a prefabricated polyvinyl alcohol (PVA) film to transfer and stack 2D materials, whether they are prepared by CVD or exfoliation. This peel-off and drop-off technique promises an ideal interface of the materials without introducing contamination. In addition, the method exhibits a micron-scale spatial transfer accuracy and meets special experimental conditions such as the preparation of twisted graphene and the 2D/metal heterostructure construction. We illustrate the superiority of this method with a WSe2 vertical spin valve device, whose performance verifies the applicability and advantages of such a method for spintronics. Our PVA-assisted transfer process will promote the development of high-performance 2D-material-based devices.

physics.app-ph↗

arXiv2019

Phase-Change Control of Interlayer Exchange Coupling

Changing the interlayer exchange coupling between magnetic layers in-situ is a key issue of spintronics, as it allows for the optimization of properties that are desirable for applications, including magnetic sensing and memory. In this paper, we utilize the phase change material VO2 as a spacer layer to regulate the interlayer exchange coupling between ferromagnetic layers with perpendicular magnetic anisotropy. The successful growth of ultra-thin (several nanometres) VO2 films is realized by sputtering at room temperature, which further enables the fabrication of [Pt/Co]2/VO2/[Co/Pt]2 multilayers with distinct interfaces. Such a magnetic multilayer exhibits an evolution from antiferromagnetic coupling to ferromagnetic coupling as the VO2 undergoes a phase change. The underlying mechanism originates from the change in the electronic structure of the spacer layer from an insulating to a metallic state. As a demonstration of phase change spintronics, this work may reveal the great potential of material innovations for next-generation spintronics.

cond-mat.mtrl-sci↗

arXiv2019

Energy-efficient domain wall motion governed by the interplay of helicity-dependent optical effect and spin-orbit torque

Spin-orbit torque provides a powerful means of manipulating domain walls along magnetic wires. However, the current density required for domain wall motion is still too high to realize low power devices. Here we experimentally demonstrate helicity-dependent domain wall motion by combining synchronized femtosecond laser pulses and short current pulses in Co/Ni/Co ultra-thin film wires with perpendicular magnetization. Domain wall can remain pinned under one laser circular helicity while depinned by the opposite circular helicity. Thanks to the all-optical helicity-dependent effect, the threshold current density due to spin-orbit torque can be reduced by more than 50%. Based on this joint effect combining spin-orbit torque and helicity-dependent laser pulses, an optoelectronic logic-in-memory device has been experimentally demonstrated. This work enables a new class of low power spintronic-photonic devices beyond the conventional approach of all-optical switching or all-current switching for data storage.

cond-mat.mes-hall↗

arXiv2019

Domain-wall motion induced by spin transfer torque delivered by helicity-dependent femtosecond laser

In magnetic wires with perpendicular anisotropy, moving domain with only current or only circularly polarized light requires a high power. Here, we propose to reduce it by using both short current pulses and femtosecond laser pulses simultaneously. The wires were made out of perpendicularly magnetized film of Pt/Co/Ni/Co/Pt. The displacement of the domain wall is found to be dependent on the laser helicity. Based on a quantitative analysis of the current-induced domain wall motion, the spin orbit torque contribution can be neglected when compared to the spin transfer torque contribution. The effective field of the spin transfer torque is extracted from the pulsed field domain wall measurements. Finally, our result can be described using the Fatuzzo-Labrune model and considering the effective field due to the polarized laser beam, the effective field due to spin transfer torque, and the Gaussian temperature distribution of the laser spot.

cond-mat.mes-hall↗

arXiv2018

Optical control of magnetism in NiFe/VO2 heterostructures

Optical methods for magnetism manipulation have been considered as a promising strategy for ultralow-power and ultrahigh-speed spin switches, which becomes a hot spot in the field of spintronics. However, a widely applicable and efficient method to combine optical operation with magnetic modulation is still highly desired. Here, the strongly correlated electron material VO2 is introduced to realize phase-transition based optical control of the magnetism in NiFe. The NiFe/VO2 bilayer heterostructure features appreciable modulations in electrical conductivity (55%), coercivity (60%), and magnetic anisotropy (33.5%). Further analyses indicate that interfacial strain coupling plays a crucial role in this modulation. Utilizing this optically controlled magnetism modulation feature, programmable Boolean logic gates (AND, OR, NAND, NOR, XOR, NXOR and NOT) for high-speed and low-power data processing are demonstrated based on this engineered heterostructure. As a demonstration of phase-transition spintronics, this work may pave the way for next-generation electronics in the post-Moore era.

cond-mat.mtrl-sci↗

arXiv2018

Large phase-transition-induced magnetic anisotropy change in (Co/Pt)2/VO2 heterostructure

We report the phase-transition controlled magnetic anisotropy modulation in the (Co/Pt)2/VO2 heterostructure, where VO2 is introduced into the system to applied an interfacial strain by its metal-insulator transition. A large reversible modulation of the perpendicular magnetic anisotropy (PMA) reaching 38 kJ/m3 is observed during this process. The calculated energy density variation of interfacial anisotropy reaches 100 mJ/m2, which shows significant advantage over traditional modulation strategies. Further experimental results including magnetization change versus temperature, strain buffered modulation and pre-strained sample comparison prove that the interfacial coupling between VO2 and PMA layers plays a crucial role in this modulation. This work, demonstrating the great potential of phase-transition material in efficient magnetic anisotropy modulation, would benefit the exploration for low-power consumption devices.

cond-mat.mes-hall↗