arXiv subjects

Lina Yang

Publications and source records attributed to Lina Yang.

At least 19 recordsLinked to original sources

arXiv2026

Ion Implantation Enhanced Nucleation Facilitates Heat Transport across Atomically-Sharp Semiconductor Interfaces

Overheating is a critical bottleneck limiting the performance and reliability of next-generation high-power and high-frequency electronics. Interfacial thermal resistance constitutes a significant portion of the total thermal resistance. In this study, we report an ultrahigh thermal boundary conductance (TBC) of approximately 800 MW/m2-K at the atomically-sharp AlN-SiC interface, achieved through an ion implantation-enhanced nucleation epitaxy technique. This value is among the highest TBC values reported for semiconductor interfaces, confirmed by structural characterizations which show an ultrahigh-quality interface. Atomistic Green Function calculations reveal that elastic phonon transmission dominates the interface, with nearly half of the acoustic modes (0-15 THz) exhibiting near-unity transmission due to the atomically sharp structure. Furthermore, using high-energy-resolution electron energy loss spectroscopy, we probe vibrational properties with nanometer spatial resolution and identify unique interfacial phonon modes connecting the mismatched phonon spectra, confirmed by molecular dynamics simulations. The ultrahigh TBC is attributed to both the high elastic phonon transmission due to the high quality interfaces and the inelastic phonon scattering channel due to interfacial phonon modes. These findings not only advance the fundamental understanding of interfacial thermal transport but also provide a pathway for effective thermal management in emerging electronic devices.

cond-mat.mes-hall↗

arXiv2026

AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization

Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.

cs.CL↗

arXiv2025

Non-uniform Thermal Conductivity in Nanoscale Multiple Hotspot Systems

Understanding nanoscale hotspot thermal transport is crucial in electronic devices. Contrary to common perception, recent experiments show that closely spaced nanoscale multiple hotspots can enhance heat dissipation. Here, the thermal transport in nanoscale multiple hotspot systems is investigated by solving the phonon Boltzmann transport equation. The local thermal conductivity is proposed to describe the non-uniform spatial distribution of heat transport capability in nanoscale multiple hotspot systems. The maximum value exceeds the uniform heating case by up to 27%, which is attributed to the spatially varying fraction of unscattered phonons emitted from hotspots. Moreover, the effects and mechanisms of hotspot spacing on thermal transport are investigated, showing that reducing the hotspot spacing can enhance the heat flux by up to 40%. This work challenges the conventional view that thermal transport capability is spatially uniform throughout the system and provides fundamental insights for thermal management in high-power-density integrated circuits.

cond-mat.mes-hall↗

arXiv2025

Effect of non-Fourier heat transport on temperature distribution in High Bandwidth Memory

High Bandwidth Memory (HBM), as a key development trend in future memory chip technology, significantly enhances computer performance. At the same time, the thermal challenges arising from its stacked architecture have drawn considerable attention. Most existing studies on HBM thermal management are based on Fourier's law, neglecting the non-Fourier effects introduced by the micro/nanoscale structures within HBM. In this study, the Monte Carlo method (MC) is employed to solve the phonon Boltzmann transport equation (BTE) and investigate the impact of non-Fourier heat transport on the thermal behavior of HBM structures. The results reveal that non-Fourier heat transport leads to a junction temperature that is 59.8 K higher than that predicted by Fourier's law. Furthermore, it is found that the phonon transmittance at the chip interlayers has a severe impact on heat dissipation, with the temperature variation reaching up to 56.6 K. These findings provide more accurate thermal insights, which are critical for the optimized design of HBM systems.

physics.app-ph↗

arXiv2025

A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting

Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Transformer predictor addresses constraints arising from restricted data availability. A spatiotemporal GAN produces realistic virtual samples by dynamically modeling spatial correlations through a graph convolutional network, and an enhanced Transformer captures local patterns with causal convolutions and long-term dependencies with self-attention,eliminating autoregressive decoding. A joint training strategy refines virtual sample generation based on predictor feedback to maintain robust performance under data-scarce conditions. Experimental evaluations on real-world daily and monthly tourism demand datasets indicate a reduction in average MASE by 18.37% compared to conventional Transformer-based models, demonstrating improved forecasting accuracy. The integration of adaptive spatiotemporal sample augmentation with a specialized Transformer can effectively address limited-data forecasting scenarios in tourism management.

stat.AP↗

arXiv2025

Tunable Thermal Conductivity and Mechanical Properties of Metastable Silicon by Phase Engineering

The extensive applications of cubic silicon in flexible transistors and infrared detectors are much hindered by its intrinsic properties. Metastable silicon phases, such as Si-III, IV and XII prepared using extreme pressure method, provide a unique "genetic bank" with diverse structures and exotic characteristics, however, exploration on their inherent physical properties remains immature. Herein, we demonstrate the phase engineering strategy to modulate the thermal conductivity and mechanical properties of metastable silicon. The thermal conductivity obtained via Raman optothermal approach presents the broad tunability across various Si-I, III, XII and IV phases. The hardness and Young's modulus of Si-IV are remarkably greater than those of Si-III/XII mixture, confirmed by nanoindentation technique. Moreover, it was found that the pressure-induced structural defects can substantially degrade the thermal and mechanical properties of silicon. This systematic investigation can offer feasible route to design novel semiconductors and further advance their desirable applications in advanced nanodevices and mechanical transducers.

cond-mat.mtrl-sci↗

arXiv2024

Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts

Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students' explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students' levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students' knowledge state. Finally, through personalized diagnostic enhancement, the framework integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics. This approach enables precise inference of students' knowledge state. Research shows that this framework not only reasonably constrains changes in students' knowledge states to align with real educational settings, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis.

cs.CY↗

arXiv2024

Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather

Solar energy is one of the most promising renewable energy resources. Forecasting photovoltaic power generation is an important way to increase photovoltaic penetration. However, the difficulty in qualifying the uncertainty of PV power generation, especially during hazy weather, makes forecasting challenging. This paper proposes a novel model to address the issue. We introduce a modified entropy to qualify uncertainty during hazy weather while clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy, respectively. Hyperparameters were adjusted using an optimization algorithm. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy compared to existing models.

cs.LG↗

arXiv2024

Tuning the thermal conductivity of Si membrane using nanopillars: from crystalline to amorphous pillars

Tuning thermal transport in nanostructures is essential for many applications, such as thermal management and thermoelectrics. Nanophononic metamaterials (NPM) have shown great potential for reducing thermal conductivity by introducing local resonant hybridization. In this work, the thermal conductivity of NPM with crystalline Si (c-Si) pillar, crystalline Ge (c-Ge) pillar and amorphous Si (a-Si) pillar are systematically investigated by molecular dynamics method. The analyses of phonon dispersion and spectral energy density show that phonon dispersions of Si membrane are flattened due to local resonant hybridization induced by both crystalline and amorphous pillar. In addition, a-Si pillar can cause larger reduction of thermal conductivity compared with c-Si pillar. Specifically, when increasing the atomic mass of atoms in pillars, the thermal conductivity of NPMs with crystalline pillar is increased because of the weakened phonon hybridization, however, the thermal conductivity of NPMs with amorphous pillar is almost unchanged, which indicates that the phonon transports are mainly affected by the scatterings at the interface between amorphous pillar and Si membrane. The results of this work can provide meaningful insights on controlling thermal transport in NPMs by choosing the materials and atomic mass of pillars for specific applications.

cond-mat.mtrl-sci↗

arXiv2024

Vibronics of multi-material nanopillared membranes and impact on the thermal conductivity

Atomic motion in nanopillars standing on the surface of a silicon membrane generates vibrons, which are wavenumber-independent phonons that act as local resonances. These vibrons couple with heat-carrying phonons traveling along the base membrane causing a reduction in the in-plane lattice thermal conductivity. In this work, we examine isolated silicon and gallium nitride nanopillars and for each compare the vibrons density of states (DOS) to those of phonons in an isolated uniform silicon membrane. We show that while the phonon-vibron DOS conformity across the full spectrum is a key factor in reducing the thermal conductivity of the assembled nanostructure, the presence of an intense vibron population at more dominant low frequencies plays a competing role. We report predictions from molecular dynamics simulations showing lower thermal conductivities for a silicon membrane with gallium-nitride nanopillars compared to a silicon membrane with silicon nanopillars.

cond-mat.mes-hall↗

arXiv2024

Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework

Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields. Due to location differences, growth in data size, and centralized server storage constraints, these data are usually stored under different databases across regions/countries. However, privacy laws and national security concerns constrain researchers from accessing these sensitive remote sensing images for further analysis. Additionally, low-resource remote sensing devices encounter challenges in terms of communication overhead and efficiency when dealing with the ever-increasing data and model scales. To solve the above challenges, this paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. The proposed framework allows each client to learn global and local knowledge to enhance the local representation of private data in environments with extreme statistical heterogeneity (non. Independent and Identically Distributed, IID). Thus, it provides highly customized models to clients with differentiated data distributions. Moreover, the framework minimizes communication overhead and improves efficiency while ensuring satisfactory performance, thereby enhancing robustness and practical applicability under resource-scarce conditions. We demonstrate the effectiveness of the proposed PRFL on the classical TFGC task by leveraging four public datasets.

cs.LG↗

arXiv2023

Enhancing interfacial thermal conductance of amorphous interface by optimized interfacial mass distribution

Interfacial thermal resistance arises challenges for the thermal management as the modern semiconductors are miniatured to nanoscale. Previous studies found that graded mass distribution in interface can maximumly enhance the interfacial thermal conductance of crystalline interface, however, whether this strategy is effective for amorphous interface is less explored. In this work, graded mass distribution in the amorphous interface between crystalline Si and crystalline Ge is optimized to increase the interfacial thermal conductance by the extended atomistic Greens function method.

cond-mat.mtrl-sci↗

arXiv2023

Epitaxial {\alpha}-Ta (110) film on a-plane sapphire substrate for superconducting qubits on wafer scale

Realization of practical superconducting quantum computing requires many qubits of long coherence time. Compared to the commonly used Ta deposited on c-plane sapphire, which occasionally form {\alpha}-Ta (111) grains and \b{eta}-tantalum grains, high quality Ta (110) film can grow epitaxial on a-plane sapphire because of the atomic relationships at the interface. Well-ordered {\alpha}-Ta (110) film on wafer-scale a-plane sapphire has been prepared. The film exhibits high residual resistance ratio. Transmon qubits fabricated using these film shows relaxation times exceeding 150 {\mu}s. The results suggest Ta film on a-plane sapphire is a promising choice for long coherence time qubit on wafer scale.

quant-ph↗

arXiv2023

Stable and low loss oxide layer on {\alpha}-Ta (110) film for superconducting qubits

The presence of amorphous oxide layers can significantly affect the coherent time of superconducting qubits due to their high dielectric loss. Typically, the surface oxides of superconductor films exhibit lossy and unstable behavior when exposed to air. To increase the coherence time, it is essential for qubits to have stable and low dielectric loss oxides, either as barrier or passivation layers. In this study, we highlight the robust and stable nature of an amorphous tantalum oxide layer formed on {\alpha}-Ta (110) film by employing chemical and structural analyses. Such kind of oxide layer forms in a self-limiting process on the surface of {\alpha}-Ta (110) film in piranha solution, yielding stable thickness and steady chemical composition. Quarter wavelength coplanar waveguide resonators are made to study the loss of this oxide. One resonator has a Qi of 3.0x10^6 in the single photon region. The Qi of most devices are higher than 2.0x10^6. Moreover, most of them are still over 1x10^6 even after exposed to air for months. Based on these findings, we propose an all-tantalum superconducting qubit utilizing such oxide as passivation layers, which possess low dielectric loss and improved stability.

cond-mat.mtrl-sci↗

arXiv2023

Deformation insensitive thermal conductance of the designed Si metamaterial

The thermal management have been widely focused due to broad applications. Generally, the deformation can largely tune the thermal transport. The main challenge of flexible electronics/ materials is to maintain thermal conductance under large deformation. This work investigates the thermal conductance of a nano-designed Si metamaterial constructed with curved nanobeams by molecular dynamics simulation. Interestingly, it shows that the thermal conductance of the nano-designed Si metamaterial is insensitive under a large deformation (strain~-41%). The new feature comes from the designed curved nanobeams which makes a quasi-zero stiffness. Further calculations show that, when under a large deformation, the average stress in nanobeam is ultra-small (<151 MPa) and its phonon density of states are little changed. This work provides valuable insights on multifunction, such as both stable thermal and mechanical properties, of nano-designed metamaterials.

physics.app-ph↗

arXiv2023

Semiconductor thermal and electrical properties decoupled by localized phonon resonances

Thermoelectric materials convert heat into electricity through thermally driven charge transport in solids, or vice versa for cooling. To be competitive with conventional energy-generation technologies, a thermoelectric material must possess the properties of both an electrical conductor and a thermal insulator. However, these properties are normally mutually exclusive because of the interconnection of the scattering mechanisms for charge carriers and phonons. Recent theoretical investigations on sub-device scales have revealed that silicon membranes covered by nanopillars exhibit a multitude of local phonon resonances, spanning the full spectrum, that couple with the heat-carrying phonons in the membrane and collectively cause a reduction in the in-plane thermal conductivity$-$while, in principle, not affecting the electrical properties because the nanopillars are external to the pathway of voltage generation and charge transport. Here this effect is demonstrated experimentally for the first time by investigating device-scale suspended silicon membranes with GaN nanopillars grown on the surface. The nanopillars cause up to 21 % reduction in the thermal conductivity while the electrical conductivity and the Seebeck coefficient remain unaffected, thus demonstrating an unprecedented decoupling in the semiconductor's thermoelectric properties. The measured thermal conductivity behavior for coalesced nanopillars and corresponding lattice-dynamics calculations provide further evidence that the reductions are mechanistically tied to the phonon resonances. This finding breaks a longstanding trade-off between competing properties in thermoelectricity and paves the way for engineered high-efficiency solid-state energy recovery and cooling.

cond-mat.mes-hall↗

arXiv2022

Interfacial Thermal Conductance Spectrum in Nonequilibrium Molecular Dynamics Simulations Considering Anharmonicity, Non-homogeneity and Quantum Effects

Interfacial thermal transport is critical for many thermal-related applications such as heat dissipation in electronics. While the total interfacial thermal conductance (ITC) can be easily measured or calculated, the ITC spectral mapping has been investigated only recently and is not fully understood. By combining nonequilibrium molecular dynamics simulations and atomistic Green's function method, we systematically investigate the ITC spectrum across an ideal interface, i.e., the argon-heavy argon interface. Our results show that the ITC spectrum increases gradually with temperature as more phonons and anharmonic scattering channels are activated, e.g., the vibrations with frequencies larger than 1 THz can contribute 5% (15%) to the total ITC at 2 K (40 K) through anharmonic phonon scatterings channels. We further find that the ITC spectrum from the left interfacial Hamiltonian is quite different from that of the right interfacial Hamiltonian, which stems from the asymmetry of anharmonic phonon scatterings caused by the dissimilar vibrational property of the two interfacial contacts. While all the phonons are involved in the anharmonic scatterings for the heavy argon interfacial Hamiltonian, these phonons involved in the anharmonic phonon scatterings from the argon interfacial Hamiltonians are mainly these vibrations with frequency smaller than 1 THz (i.e., the cut-off frequency of heavy argon). Finally, we find the quantum effect is important for the ITC spectrum at low temperatures, e.g., below 30 K in our systems. Our results here systematically investigate the influence of anharmonicity, non-homogeneity, and quantum effects on the ITC spectrum, which is critical for designing and optimizing the interfaces with better performance.

cond-mat.mtrl-sci↗

arXiv2022

Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation

With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel classes appear incrementally along with few labeling due to its newly emergence or lack of exploration. In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer. Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype. Prototype is a vector representing a class in the metric space. With the pop-up of novel classes, Geometer learns and adjusts the attention-based prototypes by observing the geometric proximity, uniformity and separability. Teacher-student knowledge distillation and biased sampling are further introduced to mitigate catastrophic forgetting and unbalanced labeling problem respectively. Experimental results on four public datasets demonstrate that Geometer achieves a substantial improvement of 9.46% to 27.60% over state-of-the-art methods.

cs.AI↗