arXiv subjects

Yutao Li

Publications and source records attributed to Yutao Li.

6 recordsLinked to original sources

arXiv2026

Design and fabrication of guiding patterns for topography-based searching of 2D devices for scanning tunneling microscopy measurements

We report the design and fabrication of guiding patterns for topography-based searching of two-dimensional (2D) devices for scanning tunneling microscopy (STM) measurements. Sub-micron geometric coordinate markers were etched into SiO2/Si wafers, serving as both substrates for 2D device integration and guiding maps for sample navigation. Here, we used a monolayer graphene/h-BN device with an active area of smaller than 20 um x 20 um as a model system and demonstrated that the device could be reliably located in STM solely through topographic imaging of the guiding patterns and in situ stage calibration, without reliance on optical viewports or capacitive sensing. Atomically resolved topographic imaging and tunneling spectroscopy were also obtained. Our proposed navigation strategy is fully compatible with standard device fabrication procedures and requires no hardware modification to existing STM setups. As such, it offers a practical alternative for locating miniaturized devices in STM, shedding light on studying emerging quantum phenomena in 2D systems.

cond-mat.str-el↗

arXiv2025

A Practical Flake Segmentation and Indexing Pipeline for Automated 2D Material Stacking

A cost-effective and robust image-processing pipeline is presented for the detection and characterization of exfoliated two-dimensional (2D) material flakes in optical microscope images, designed to facilitate automation in van der Waals heterostructure assembly. The system combines shallow machine learning (ML)-based material classification with a precision-first flake detection algorithm driven by edge morphology and color discontinuity. Step edges are resolved when supported by optical contrast, while spurious features such as dust and background texture are reliably rejected. Each identified flake is exported in a structured format that includes centroid coordinates, bounding geometries, average RGB color, and estimated optical thickness, enabling seamless integration into automated pick-up and stacking workflows. The pipeline is hardware-light and operates without the need for deep learning models or nanoscale ground-truth labels, making it practical for scalable front-end wafer processing at a hardware cost of under 30,000 USD. In contrast to prior approaches that focus solely on detection accuracy, the proposed system unifies flake segmentation with indexing, filtering, and blueprint-driven stacking, forming a closed-loop workflow from image acquisition to device planning. Its low annotation requirement and flexible implementation enable rapid deployment across diverse 2D material systems and imaging conditions.

cond-mat.mes-hall↗

arXiv2025

UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment

Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal. Despite distinct learning objectives, they have underlying interconnectedness due to consistent human assessment perception. In this paper, we propose Unified vision-language pre-training of Quality and Aesthetics (UniQA}), to extract useful and common representations from two tasks, thereby benefiting them simultaneously. However, the lack of text in the IQA datasets and the textual noise in the IAA datasets pose severe challenges for multimodal pre-training. To address this, we (1) utilize multimodal large language models (MLLMs) to generate high-quality text descriptions; (2) use the generated text for IAA as metadata to purify noisy IAA data. To effectively adapt the pre-trained UniQA to downstream tasks, we further propose a lightweight adapter that utilizes versatile cues to fully exploit the extensive knowledge of the pre-trained model. UniQA demonstrates high competitiveness in various image assessment tasks, including classical IQA and IAA tasks, few-label IQA, and other downstream tasks, showing promise as a foundational assessment model. Codes are available at https://github.com/zht8506/UniQA.

cs.CV↗

arXiv2024

Electronic interactions in Dirac fluids visualized by nano-terahertz spacetime interference of electron-photon quasiparticles

Ultraclean graphene at charge neutrality hosts a quantum critical Dirac fluid of interacting electrons and holes. Interactions profoundly affect the charge dynamics of graphene, which is encoded in the properties of its electron-photon collective modes: surface plasmon polaritons (SPPs). Here we show that polaritonic interference patterns are particularly well suited to unveil the interactions in Dirac fluids by tracking polaritonic interference in time at temporal scales commensurate with the electronic scattering. Spacetime SPP interference patterns recorded in tera-hertz (THz) frequency range provided unobstructed readouts of the group velocity and lifetime of polariton that can be directly mapped onto the electronic spectral weight and the relaxation rate. Our data uncovered prominent departures of the electron dynamics from the predictions of the conventional Fermi-liquid theory. The deviations are particularly strong when the densities of electrons and holes are approximately equal. The proposed spacetime imaging methodology can be broadly applied to probe the electrodynamics of quantum materials.

cond-mat.str-el↗

arXiv2020

Anisotropic band flattening in graphene with 1D superlattices

Patterning graphene with a spatially-periodic potential provides a powerful means to modify its electronic properties. Dramatic effects have been demonstrated in twisted bilayers where coupling to the resulting moiré-superlattice yields an isolated flat band that hosts correlated many-body phases. However, both the symmetry and strength of the effective moiré potential are constrained by the constituent crystals, limiting its tunability. Here we exploit the technique of dielectric patterning to subject graphene to a one-dimensional electrostatic superlattice (SL). We observe the emergence of multiple Dirac cones and find evidence that with increasing SL potential the main and satellite Dirac cones are sequentially flattened in the direction parallel to the SL basis vector. Our results demonstrate the ability to induce tunable transport anisotropy in high mobility two-dimensional materials, a long-desired property for novel electronic and optical applications, as well as a new approach to engineering flat energy bands where electron-electron interactions can lead to emergent properties.

cond-mat.mes-hall↗

arXiv2017

Effects of Grain Boundaries and Defects on Anisotropic Magnon Transport in Textured Sr14Cu24O41

The strong spin-spin exchange interaction in some low-dimensional magnetic materials can give rise to a high group velocity and thermal conductivity contribution from magnons. One example is the incommensurate layered compounds (Sr,Ca,La)14Cu24O41. The effects of grain boundaries and defects on quasi-one-dimensional magnon transport in these compounds are not well understood. Here we report the microstructures and anisotropic thermal transport properties of textured Sr14Cu24O41, which are prepared by solid-state reaction followed by spark plasma sintering. Transmission electron microscopy clearly reveals nano-layered grains and the presence of dislocations and planar defects. The thermal conductivity contribution and mean free paths of magnons in the textured samples are evaluated with the use of a kinetic model for one-dimensional magnon transport, and found to be suppressed significantly as compared to single crystals at low temperatures. The experimental results can be explained by a one-dimensional magnon-defect scattering model, provided that the magnon-grain boundary scattering mean free path in the anisotropic magnetic structure is smaller than the average length of these nano-layers along the c axis. The finding suggests low transmission coefficients for magnons across grain boundaries.

cond-mat.mtrl-sci↗