arXiv subjects

Vinay Sharma

Publications and source records attributed to Vinay Sharma.

At least 19 recordsLinked to original sources

arXiv2026

A Data-Free Symbolic Regression Approach for Solving Equations

Many equations arising in science currently cannot be solved by available analytical techniques and are therefore solved numerically, without yielding explicit symbolic expressions. Existing symbolic regression approaches can recover symbolic expressions, but require training data obtained from the underlying process, rather than the governing equation alone. We propose the Symbolic Equation Solver (SES), a framework that formulates equation solving as an optimization problem over differentiable symbolic models. SES constructs its objective from the equation together with initial or boundary conditions, eliminating the need for paired input-output data. The learned model is expressed in explicit symbolic form, enabling further analysis. We evaluate SES on representative algebraic and differential equations, including a system of algebraic equations, an equation with transcendental terms, an ordinary differential equation, and partial differential equations with different initial or boundary conditions. Across these settings, SES recovers compact symbolic expressions that match the corresponding analytical solutions.

cs.NE↗

arXiv2026

Evidence of a Hybridized Topological State in Weyl Semimetal/Topological Insulator Mn$_{3+x}$Sn$_{1-x}$/Bi$_{0.85}$Sb$_{0.15}$ Heterostructures

We report magnetotransport evidence of a hybridized Weyl semimetal (WSM) Fermi arc/topological insulator (TI) surface state at the interface of a ferromagnetic Mn$_{3+x}$Sn$_{1-x}$/Bi$_{0.85}$Sb$_{0.15}$ heterostructure. High target utilization sputtering (HiTUS) was used to grow polycrystalline Mn$_{3+x}$Sn$_{1-x}$ films and Mn$_{3+x}$Sn$_{1-x}$/Bi$_{0.85}$Sb$_{0.15}$ heterostructures on thermally oxidized Si/SiO$_2$ (100) substrates that exhibit the negative coefficient anomalous Hall effect (AHE) resulting from topological Weyl node transport. When various defects and impurities are introduced into these Mn$_{3+x}$Sn$_{1-x}$ films, a ferromagnetic (FM) phase develops that practically eliminates the topological Weyl node conduction. These FM Mn$_{3+x}$Sn$_{1-x}$ films exhibit large exchange bias effects below T=200 K that we attribute to the coexistence of a FM phase and the triangular antiferromagnetic (AFM) WSM phase. When Bi$_{0.85}$Sb$_{0.15}$ overlayers are grown on the FM Mn$_{3+x}$Sn$_{1-x}$, the magnetotransport signal of Weyl node topological transport is restored, an effect we do not observe when replacing the Bi$_{0.85}$Sb$_{0.15}$ TI with heavy metal overlayers. We attribute the restoration of the Weyl node topological transport to the formation of a hybridized topological state at the WSM/TI interface.

cond-mat.mtrl-sci↗

arXiv2026

WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning

Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, supervised localization remains weakly constrained when measurements are available from only a limited set of damage locations. Because exhaustive spatial coverage is impractical, models trained at observed locations may generalize poorly to unseen regions. We propose WaveGraphNet, an inverse-forward graph-learning framework for guided-wave damage localization in carbon-fiber-reinforced polymer (CFRP) plates. The sensing network is represented as a graph, with transducers as nodes and measured pitch-catch paths as edges. The inverse branch uses order-invariant message passing and a geometry-constrained decoder to map path-wise energy deviations to a damage coordinate. An independently trained forward branch predicts the energy-deviation pattern associated with a candidate coordinate. At test time, gradients propagated through the forward branch update only the inverse-predicted coordinate to reduce the discrepancy between the measured and predicted response patterns. The framework is evaluated on the OGW-1 benchmark using three spatial hold-out splits in which complete damage regions are excluded from training. Comparisons include non-graph and graph-learning baselines together with the training-free Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID). Under the validation-selected settings, refined WaveGraphNet achieves the lowest reported test mean absolute error on all three splits. Refinement reduces the standalone localization error by 23.6 percent-67.9 percent, with an average relative reduction of 50.4 percent. These results demonstrate that learned forward-response consistency can improve guided-wave localization under limited spatial training coverage.

cs.LG↗

arXiv2026

2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing

The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.

cs.AI↗

arXiv2026

Enhancing reasoning accuracy in large language models during inference time

Large Language Models (LLMs) often exhibit strong linguistic abilities while remaining unreliable on multi-step reasoning tasks, particularly when deployed without additional training or fine-tuning. In this work, we study inference-time techniques to improve the reasoning accuracy of LLMs. We systematically evaluate three classes of inference-time strategies: (i) self-consistency via stochastic decoding, where the model is sampled multiple times using controlled temperature and nucleus sampling and the most frequent final answer is selected; (ii) dual-model reasoning agreement, where outputs from two independent models are compared and only consistent reasoning traces are trusted; and (iii) self-reflection, where the model critiques and revises its own reasoning. Across all evaluated methods, we employ Chain-of-Thought (CoT) [1] prompting to elicit explicit intermediate reasoning steps before generating final answers. In this work, we provide a controlled comparative evaluation across three inference-time strategies under identical prompting and verification settings. Our experiments on LLM [2] show that self-consistency with nucleus sampling and controlled temperature value yields the substantial gains, achieving a 9% to 15% absolute improvement in accuracy over greedy single-pass decoding, well-suited for low-risk domains, offering meaningful gains with minimal overhead. The dual-model approach provides additional confirmation for model reasoning steps thus more appropriate for moderate-risk domains, where higher reliability justifies additional compute. Self-reflection offers only marginal improvements, suggesting limited effectiveness for smaller non-reasoning models at inference time.

cs.CL↗

arXiv2025

Mortgage Language Model: Domain-Adaptive Pretraining with Residual Instruction, Alignment Tuning, and Task-Specific Routing

Large Language Models (LLMs) demonstrate exceptional capabilities across general domains, yet their application to specialized sectors such as mortgage finance requires domain-specific knowledge augmentation while preserving instruction-following fidelity. We present MortgageLLM, a novel domain-specific large language model that addresses this dual challenge. It is developed using a dual-track specialization framework from a single base model (LLaMA-3.1-8B). We opted for this dual-expert approach as a single multi-task model suffers from performance trade-offs, where optimizing for structured tasks (via SFT) degrades conversational fidelity (via DPO). Our dual-track method solves this by creating two specialists, allowing each to be optimally trained for its distinct capability. Our approach applies the instruction residual technique to restore instruction-following capabilities post-domain adaptation without supervised fine-tuning. We contribute: (1) application of this residual technique to the highly specialized mortgage finance domain; (2) a dual-expert architecture combining a conversational Q&A model and a structured task model for classification and summarization; and (3) an intelligent task routing mechanism using few-shot classification performed by one of the expert models itself. We validate our approach on domain-specific benchmarks, where our final model (MLM v2) significantly outperforms the base LLaMA-3.1-8B-Instruct, achieving an LLM-as-a-Judge summarization score of 4.58 (vs. 3.99), a Q&A score of 4.09 (vs. 4.0), and a classification score of 2.6 (vs. 1.2). On semantic similarity, our model achieved a BERTScore of 0.77 for summarization (vs. 0.74), 0.68 for Q&A (vs. 0.58), and 0.75 for classification (vs. 0.73), substantially outperforming baseline approaches.

cs.CL↗

arXiv2025

From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM

Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and optimizing maintenance activities throughout an asset lifecycle. However, real-world PHM presents persistent challenges: sensor data is often noisy or incomplete, available labels are limited, and degradation behaviors and system interdependencies can be highly complex and nonlinear. Physics-informed machine learning has emerged as a promising approach to address these limitations by embedding physical knowledge into data-driven models. This review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions. Learning biases embed physical constraints into model training through physics-informed loss functions and governing equations, or by incorporating properties like monotonicity. Observational biases influence data selection and synthesis to ensure models capture realistic system behavior through virtual sensing for estimating unmeasured states, physics-based simulation for data augmentation, and multi-sensor fusion strategies. The review then examines how these approaches enable the transition from passive prediction to active decision-making through reinforcement learning, which allows agents to learn maintenance policies that respect physical constraints while optimizing operational objectives. This closes the loop between model-based predictions, simulation, and actual system operation, empowering adaptive decision-making. Finally, the review addresses the critical challenge of scaling PHM solutions from individual assets to fleet-wide deployment. Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques ...

cs.LG↗

arXiv2025

Direct Comparison of Static and Dynamic Measurements of Spin Generation in a Topological Insulator Thin Film

The competition between intrinsic spin-orbit physics, magnetic phenomena, and the quality of materials and interfaces governs the charge-to-spin conversion processes that are essential to the implementation of spintronic devices. Direct comparisons of spin parameters, which serve as metrics of device quality, obtained by different measurement techniques are scarce, leading to uncertainty regarding discrepancies and the reliability of the methods. Here, we directly compare the spin Hall coefficient (${\theta}_{SH}$) in molecular beam epitaxy grown films of $(Bi_{1-x}Sb_{x})_{2}Te_{3-y}Se_{y}$ (BSTS, x = 0.58, y = 1) at room temperature using two complementary techniques: a static method using non-local voltage (NLV) measurements in BSTS Hall bars with DC charge current, and a dynamic method using spin-torque ferromagnetic resonance (ST-FMR) measurement in $BSTS/Ni_{80}Fe_{20}$ heterostructures at GHz frequencies. We obtain comparable spin Hall coefficients in angular-dependent ST-FMR (${\theta}_{SH}$=$4.7\pm1.1$) and in NLV (${\theta}_{SH}$=$2.8\pm0.6$). The complex effects of ferromagnetic interfaces while determining spin Hall coefficients using static or dynamic techniques becomes evident by contrasting our results to literature.

cond-mat.mtrl-sci↗

arXiv2025

Probing Magnetic Properties of RuO$_{2}$ Heterostructures Through the Ferromagnetic Layer

RuO$_{2}$ has been proposed as the prototypical altermagnetic material. However, several reports have recently questioned its intrinsic magnetic ordering, leading to conflicting findings, especially in thin film heterostructures pointing to possible interface effects being convoluted with supposed antiferromagnetic/altermagnetic signatures. Here, extensive magnetometry measurements were performed on two independently grown thin film heterostructures of RuO$_{2}$ interfaced with either NiFe or Fe acting as the ferromagnetic layer. Below about 15 K, both samples exhibit exchange bias fields when cooled to approximately 2 K in a $+$1 T field, and a spin transitional feature is observed around 31 K. Magneto-Raman measurements on RuO$_{2}$ thin films only reveal a magnon mode when there is a NiFe layer, suggesting that RuO$_{2}$ does not intrinsically possess long range magnetic ordering.. When in contact with a ferromagnet, RuO$_2$ displays effects that could be ascribed to antiferromagnetism. However, the lack of intrinsic magnon modes points toward possible diffusion between the layers or spin disorder at the interface as seen by density functional theory (DFT) calculations.

cond-mat.mtrl-sci↗

arXiv2025

Locating Extremal Periodic Orbits for the Planar Circular Restricted Three Body Problem using Polynomial Sum-of-Squares Optimization

With an increasing interest in the design of long and complex space missions, the search for orbits that require the least amount of fuel is of fundamental interest. This paper develops existing computational models for locating Unstable Periodic Orbits (UPOs) in polynomial dynamical systems using Sum-of-Squares (SOS) optimization technique and proposes a numerical framework to converge UPOs for the Planar Circular Restricted Three-Body Problem (PCR3BP) in astrodynamics. This is done by developing the polynomial SOS optimization technique with extension to systems with non-polynomial and Hamiltonian dynamics. First, we demonstrate and exploit the dependency of convergence of tight bounds on an observable of interest with varying scaling factors for large polynomial degrees. SOS optimization is then used to compute nonnegative polynomials, the minimization sublevel sets of which, approximately localise parts of the corresponding UPO. Improvements in current non-linear optimization techniques are suggested to compute a large number of points inside the relevant sublevel sets. Such points provide good initial conditions for UPO computations with existing algorithms. The distinguishing feature of such UPOs is that they optimize the long-time average of an input observable of interest which is a function of state variables. For the PCR3BP this means that such orbits in space can be traversed indefinitely in time without continuous fuel expenditure. As practical applications to space mission designs, we converge UPOs that minimise transmitted power required by satellites for the Earth-Moon system in a communication relay problem by minimizing the infinite-time average of sum of squares of distances of a satellite from Earth and the Moon.

math.DS↗

arXiv2025

Differentiating anomalous and topological Hall effects using first-order reversal curve measurements

Next generation magnetic memories rely on novel magnetic phases for information storage. Novel spin textures such as skyrmions provide one possible avenue forward due to their topological protection and controllability via electric fields. However, the common signature of these spin textures, the topological Hall effect (THE), can be mimicked by other trivial effects. Competing anomalous Hall effect (AHE) components can produce a peak in the Hall voltage similar to that of the THE, making clear identification of the THE difficult. By applying the first-order reversal curve (FORC) technique to the Hall effect in candidate topological Hall systems we can clearly distinguish between the THE and AHE. This technique allows for quantitative investigation of the THE and AHE in magnetic materials and heterostructures with topologically non-trivial spin textures. We demonstrate the technique and apply it to several examples.

cond-mat.mes-hall↗

arXiv2025

Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems

Accurate real-time modeling of multi-body dynamical systems is essential for enabling digital twin applications across industries. While many data-driven approaches aim to learn system dynamics, jointly predicting internal loads and system trajectories remains a key challenge. This dual prediction is especially important for fault detection and predictive maintenance, where internal loads-such as contact forces-act as early indicators of faults, reflecting wear or misalignment before affecting motion. These forces also serve as inputs to degradation models (e.g., crack growth), enabling damage prediction and remaining useful life estimation. We propose Equi-Euler GraphNet, a physics-informed graph neural network (GNN) that simultaneously predicts internal forces and global trajectories in multi-body systems. In this mesh-free framework, nodes represent system components and edges encode interactions. Equi-Euler GraphNet introduces two inductive biases: (1) an equivariant message-passing scheme, interpreting edge messages as interaction forces consistent under Euclidean transformations; and (2) a temporal-aware iterative node update mechanism, based on Euler integration, to capture influence of distant interactions over time. Tailored for cylindrical roller bearings, it decouples ring dynamics from constrained motion of rolling elements. Trained on high-fidelity multiphysics simulations, Equi-Euler GraphNet generalizes beyond the training distribution, accurately predicting loads and trajectories under unseen speeds, loads, and configurations. It outperforms state-of-the-art GNNs focused on trajectory prediction, delivering stable rollouts over thousands of time steps with minimal error accumulation. Achieving up to a 200x speedup over conventional solvers while maintaining comparable accuracy, it serves as an efficient reduced-order model for digital twins, design, and maintenance.

cs.LG↗

arXiv2025

Anomalous and Planar Hall Effects in Cobalt-Holmium Thin Films Near Magnetic Sublattice Compensation

Metallic amorphous ferrimagnets derived from alloying 3d transition metals with 4f electron rare earths host fascinating effects of compensation between the 3d and 4f magnetic sublattices. Here, a detailed study of anisotropic magnetoresistance (AMR), planar Hall effect (PHE) and anomalous Hall effect (AHE) are reported on a series of CoHo thin films over a wide field temperature phase space. Close to magnetic compensation temperature, the AHE loops show a double sign reversal and signatures of spin flop transition at higher fields. The AMR and PHE also display strong deviations from the classical angular dependence seen in soft ferromagnets like permalloy as the angle between in-plane current and magnetic field is scanned from 0 to 360 degrees. It is argued that the non zero orbital angular momentum of Ho ions in the lattice and stabilization of bubble domains below magnetic saturation may be responsible for such features. Direct imaging of magnetic textures with X ray photoelectron microscopy shows formation of stripe domain patterns in the regime of sublattice compensation. Such stripes are likely to transform into magnetic bubbles before full saturation is reached in a large magnetic field.

cond-mat.mes-hall↗

arXiv2025

Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems

Accurate, interpretable, and real-time modeling of multi-body dynamical systems is essential for predicting behaviors and inferring physical properties in natural and engineered environments. Traditional physics-based models face scalability challenges and are computationally demanding, while data-driven approaches like Graph Neural Networks (GNNs) often lack physical consistency, interpretability, and generalization. In this paper, we propose Dynami-CAL GraphNet, a Physics-Informed Graph Neural Network that integrates the learning capabilities of GNNs with physics-based inductive biases to address these limitations. Dynami-CAL GraphNet enforces pairwise conservation of linear and angular momentum for interacting nodes using edge-local reference frames that are equivariant to rotational symmetries, invariant to translations, and equivariant to node permutations. This design ensures physically consistent predictions of node dynamics while offering interpretable, edge-wise linear and angular impulses resulting from pairwise interactions. Evaluated on a 3D granular system with inelastic collisions, Dynami-CAL GraphNet demonstrates stable error accumulation over extended rollouts, effective extrapolations to unseen configurations, and robust handling of heterogeneous interactions and external forces. Dynami-CAL GraphNet offers significant advantages in fields requiring accurate, interpretable, and real-time modeling of complex multi-body dynamical systems, such as robotics, aerospace engineering, and materials science. By providing physically consistent and scalable predictions that adhere to fundamental conservation laws, it enables the inference of forces and moments while efficiently handling heterogeneous interactions and external forces.

cs.LG↗

arXiv2023

Graph Neural Networks for Dynamic Modeling of Roller Bearing

In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability and interpretability, having the potential for scalable use in real-time operational digital twin systems for monitoring the health state of rotating machines. By representing the bearing's components as nodes in a graph, the GNN can effectively model the complex relationships and interactions among them. We utilize a dynamic spring-mass-damper model of a bearing to generate the training data for the GNN. In this model, discrete masses represent bearing components such as rolling elements, inner raceways, and outer raceways, while a Hertzian contact model is employed to calculate the forces between these components. We evaluate the learning and generalization capabilities of the proposed GNN framework by testing different bearing configurations that deviate from the training configurations. Through this approach, we demonstrate the effectiveness of the GNN-based method in accurately predicting the dynamics of rolling element bearings, highlighting its potential for real-time health monitoring of rotating machinery.

cs.LG↗

arXiv2023

Planar Hall effect and Anisotropic Magnetoresistance in Thin Films of Chiral Antiferromagnet Mn3Sn

Antiferromagnetic Weyl semimetals with spin chirality offer excellent platforms to address the Berry phase physics, which manifests prominently in several of their electro-optical and electro-magnetic responses including as a large anomalous Hall effect (AHE) and spin Hall conductivity. Here, we report measurements of magneto-transport in c-axis textured Mn3Sn thin films grown on the [111] plane of single crystal MgO. At room temperature, these films display a weak uncompensated magnetic moment of \approx 0.12 \micro_{B}/f.u. in the basal plane and a longitudinal resistivity (ρ_{xx}) close to \approx 3.8 \microΩ.m. A residual resistivity ration (ρ_{xx} (300 K)/ρ_{xx} (2 K)) of \approx 3.92 further indicates the high quality of the films. While at 300 K a weak AHE together with field-linear Hall resistivity (ρ_{xy}) is observed in magnetic fields (H) applied perpendicular to the Kagome planes, the temperature (T) dependence of ρ_{xy} shows prominent signatures of three magnetic phases in the temperature regime of 2 to 300 K. The ρ_{xy} also derives a non-trivial topological contribution (\r{ho}THE \approx 1nΩ.m) in the spin glass phase which appears at T \geq 100 K. Our measurements of anisotropic magnetoresistance (AMR) and planar Hall effect (PHE) over a wide H-T phase space reveal the hitherto unseen effects in the three magnetic phases of Mn3Sn. While the AMR and PHE are negative in the inverse triangular spin phase (250 K \geq T \geq TN), the helical phase (100 \geq T \geq 250 K) is devoid of anisotropic in-plane resistivity, and the spin glass phase shows a sign reversal of AMR with the increasing magnetic field. The origin of this sign change in AMR/PHE is attributed to the emergence of topologically protected spin textures like skyrmions where the fictitious effective magnetic field is estimated to be \approx 4.4 tesla.

cond-mat.mtrl-sci↗

arXiv2023

Crystal orientation dependent spin pumping in Bi0.1Y2.9Fe5O12/Pt interface

Ferromagnetic resonance (FMR) based spin pumping is a versatile tool to quantify the spin mixing conductance and spin to charge conversion (S2CC) efficiency of ferromagnet/normal metal (FM/NM) heterostructure. The spin mixing conductance of FM/NM interface can also be tuned by the crystal orientation symmetry of epitaxial FM. In this work, we study the S2CC in epitaxial Bismuth substituted Yttrium Iron Garnet (Bi0.1Y2.9Fe5O12) thin films Bi-YIG (100 nm) interfaced with heavy metal platinum (Pt (8 nm)) deposited by pulsed laser deposition process on different crystal orientation Gd3Ga5O12 (GGG) substrates i.e. [100] and [111]. The crystal structure and surface roughness characterized by X-Ray diffraction and atomic force microscopy measurements establish epitaxial Bi-YIG[100], Bi-YIG[111] orientations and atomically flat surfaces respectively. The S2CC quantification has been realized by two complimentary techniques, (i) FMR-based spin pumping and inverse spin Hall effect (ISHE) at GHz frequency and (ii) temperature dependent spin Seebeck measurements. FMR-ISHE results demonstrate that the [111] oriented Bi-YIG/Pt sample shows significantly higher values of spin mixing conductance ((2.31+-0.23)x10^18 m^-2) and spin Hall angle (0.01+-0.001) as compared to the [100] oriented Bi-YIG/Pt. A longitudinal spin Seebeck measurement reveals that the [111] oriented sample has higher spin Seebeck coefficient (106.40+-10 nV mm-1 K-1). This anisotropic nature of spin mixing conductance and spin Seebeck coefficient in [111] and [100] orientation has been discussed using the magnetic environment elongation along the surface normal or parallel to the growth direction. Our results aid in understanding the role of crystal orientation symmetry in S2CC based spintronics devices.

cond-mat.mtrl-sci↗

arXiv2022

Growth parameters of Bi0.1Y2.9Fe5O12 thin films for high frequency applications

The growth and characterization of Bismuth (Bi) substituted YIG (Bi-YIG, Bi0.1Y2.9Fe5O12) thin films are reported. Pulsed laser deposited (PLD) films with thicknesses ranging from 20 to 150 nm were grown on Gadolinium Gallium Garnet substrates. Two substrate orientations of (100) and (111) were considered. The enhanced distribution of Bi3+ ions at dodecahedral site along (111) is observed to lead to an increment in lattice constant from 12.379 angstrom in (100) to 12.415 angstrom in (111) oriented films. Atomic force microscopy images showed decreasing roughness with increasing film thickness. Compared to (100) grown films, (111) oriented films showed an increase in ferromagnetic resonance linewidth and consequent increase in Gilbert damping. The lowest Gilbert damping values are found to be (1.06) * 10E-4 for (100) and (2.30) * 10E-4 for (111) oriented films with thickness of 150 nm. The observed values of extrinsic linewidth, effective magnetization, and anisotropic field are related to thickness of the films and substrate orientation. In addition, the in-plane angular variation established four-fold symmetry for the (100) deposited films unlike the case of (111) deposited films. This study prescribes growth conditions for PLD grown single-crystalline Bi-YIG films towards desired high frequency and magneto-optical device applications.

cond-mat.mtrl-sci↗