arXiv subjects

Feng Miao

Publications and source records attributed to Feng Miao.

At least 19 recordsLinked to original sources

arXiv2026

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing

Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the non-volatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials.

cond-mat.mtrl-sci↗

arXiv2026

BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning

Current Vision-Language Models (VLMs) often struggle to handle complex visual tasks that require consistent and fine-grained reasoning. Recent methods aim to train models to facilitate self-reflective reasoning, i.e., reviewing and improving the generated reasoning. However, they require large volumes of annotated data and lack explicit reflective behavior during test time. By contrast, humans perform explicit and efficient self-reflection through mechanisms such as backward prediction, i.e., predicting which current states are likely to precede a given future state. Inspired by neuroscience, this work proposes a novel solution to address these challenges. We first observe and investigate the phenomenon that mainstream VLMs can perform backward prediction, similar to the human brain. A label-free training framework named Brain-inspired Unsupervised Self-reflection (BUS) is proposed to leverage and exploit backward prediction capability to enhance reflective reasoning in complex visual tasks. BUS enables self-verification of reflective reasoning based on backward prediction, providing explicit learning signals under unsupervised conditions. In this way, BUS eliminates reliance on annotated data while improving reasoning performance. Designed as a model-agnostic plug-in, our framework is compatible with popular fine-tuning methods, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Initialized from Qwen3-VL-8B, it improves HR-Bench-8K (+8.0%), HR-Bench-4K (+7.7%), V* Bench (+6.3%), and MME-RealWorld-Lite (+5.8%), proving backward prediction is key to advancing reflective reasoning.

cs.CV↗

arXiv2026

Tunable Extended Magnetic Non-Fermi Liquid in Graphene Moir\'e Heterostructures

Exploring exotic quantum metallic states beyond Landau's Fermi liquid theory remains a central focus in condensed matter physics. Such non-Fermi liquid behavior is mostly observed near quantum criticality, yet growing attention is directed toward extended NFL phases with intrinsic quantum fluctuations rooted in the extended ground state. While these extended NFL states have been previously reported only in a limited set of d- and f-electron systems, realizing a single, highly tunable platform capable of exhibiting multiple resistance exponent values is essential for uncovering the connection between the resistance exponent and the dominant quantum fluctuations coupled to quasiparticles. However, corresponding experimental progress remains elusive. Here, we report the observation of tunable extended non-Fermi liquid behavior in twisted double bilayer graphene encapsulated by aligned hBN layers. This NFL phase spans a broad range of carrier densities and exhibiting a carrier density dependent resistance exponent. Combined with temperature dependent resistance, magnetotransport and differential resistance measurements, these findings support a scenario where strong quantum fluctuations emerge from the interplay between localized and itinerant carriers. Our work establishes a highly tunable platform beyond conventional frameworks to investigate the organizing principles of non-Fermi liquid physics manifested in diverse behaviors.

cond-mat.mes-hall↗

arXiv2026

Orientation-tunable correlated Chern insulating states in chiral twisted double bilayer graphene proximitized by WSe2

Moire flat bands in graphene systems proximitized by transition-metal dichalcogenides (TMDCs) provide a setting where spin-orbit coupling (SOC) can reshape band topology. The crystallographic alignment angle twist angle between TMDC and graphene layers is predicted to tune the balance of Ising and Rashba SOC, but a combined theoretical and experimental understanding of how twist angle governs the topological character of correlated states has not been systematically established. Here we show that in chiral-stacked twisted double bilayer graphene in proximity to WSe2, twist angle between graphene and WSe2 determines the topological character of correlated Chern insulators. Continuum model calculations reveal that Ising spin-orbit coupling dominates at zero twist angle, giving rise to flat bands with finite valley Chern numbers, whereas Rashba coupling dominates at larger twist angle, resulting in topologically trivial bands. Transport measurements at quarter filling confirm this picture: twist angle = 0 deg devices exhibit C = +1 Chern insulators, consistent with spontaneous isospin polarization, whereas twist angle = 15 degree devices show C = 0 despite exhibiting similar correlated insulating behavior. The sharp contrast establishes crystallographic alignment as a new tuning knob, complementary to twist angle, displacement field, and carrier density, for engineering correlated topological states in van der Waals heterostructures.

cond-mat.mes-hall↗

arXiv2026

UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing

Ultra-high-resolution (UHR) remote sensing imagery couples kilometer-scale context with query-critical evidence that may occupy only a few pixels. Such vast spatial scale leads to a quadratic explosion of visual tokens and hinders the extraction of information from small objects. Previous works utilize direct downsampling, dense tiling, or global top-k pruning, which either compromise query-critical image details or incur unpredictable compute. In this paper, we propose UHR-BAT, a query-guided and region-faithful token compression framework to efficiently select visual tokens under a strict context budget. Specifically, we leverage text-guided, multi-scale importance estimation for visual tokens, effectively tackling the challenge of achieving precise yet low-cost feature extraction. Furthermore, by introducing region-wise preserve and merge strategies, we mitigate visual token redundancy, further driving down the computational budget. Experimental results show that UHR-BAT achieves state-of-the-art performance across various benchmarks. Code will be available at https://github.com/Yunkaidang/UHR.

cs.CV↗

arXiv2026

From stacking to function: emergent states and quantum devices in 2D superconductor heterostructures

Two-dimensional (2D) superconductors provide a powerful building block for engineering emergent quantum states shaped by reduced dimensionality, enhanced quantum fluctuations, and interfacial symmetry breaking. In van der Waals heterostructures, atomically sharp and lattice-mismatch-free interfaces enable superconductivity to be deliberately coupled with magnetism, spin orbit interaction, and band topology, allowing collective electronic orders to be combined and reconfigured in ways unattainable in bulk materials. This Review summarizes recent advances in vdW heterostructures of 2D superconductors, focusing on superconductor/magnet, superconductor/topological material, and superconductor/superconductor junctions. We discuss the microscopic mechanisms underlying proximity effects and highlight how interfacial exchange fields, spin orbit coupling, and twist-controlled tunneling give rise to unconventional pairing, long-range spin-triplet supercurrents, nonreciprocal Josephson transport, and topological superconductivity potentially hosting Majorana bound states. Beyond their fundamental significance, the ability to controllably generate topological and nonreciprocal superconducting states positions 2D superconductor heterostructures as promising building blocks for emerging quantum technologies, including ultra-sensitive quantum sensing, programmable superconducting logic, and energy-efficient quantum and neuromorphic computing architectures. Looking forward, advances in materials synthesis, interface engineering, and device integration are expected to further expand the scope and functionality of 2D superconductor heterostructures, reinforcing their role as a central platform for exploring and controlling emergent quantum phases.

cond-mat.mes-hall↗

arXiv2026

HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework

Current Large Multimodal Models (LMMs) struggle with high-resolution visual inputs during the reasoning process, as the number of image tokens increases quadratically with resolution, introducing substantial redundancy and irrelevant information. A common practice is to identify key image regions and refer to their high-resolution counterparts during reasoning, typically trained with external visual supervision. However, such visual supervision cues require costly grounding labels from human annotators. Meanwhile, it remains an open question how to enhance a model's grounding abilities to support reasoning without relying on additional annotations. In this paper, we propose High-resolution Annotation-free Reasoning Technique (HART), a closed-loop framework that enables LMMs to focus on and self-verify key regions of high-resolution visual inputs. HART incorporates a post-training paradigm in which we design Advantage Preference Group Relative Policy Optimization (AP-GRPO) to encourage accurate localization of key regions without external visual annotations. Notably, HART provides explainable reasoning pathways and enables efficient optimization of localization. Extensive experiments on MME-RealWorld-Lite, TreeBench, V* Bench, HR-Bench-4K/8K, and MMStar demonstrate that HART improves performance across a wide range of high-resolution visual tasks, consistently outperforming strong baselines.

cs.CV↗

arXiv2025

A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs

Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on low-resolution imagery, and some high-resolution benchmarks suffer from flawed reasoning-task designs. We show that text-only LLMs can perform competitively with multimodal vision-language models on RS reasoning tasks without access to images, revealing a critical mismatch between current benchmarks and the intended evaluation of visual understanding. To enable faithful assessment, we introduce RSHR-Bench, a super-high-resolution benchmark for RS visual understanding and reasoning. RSHR-Bench contains 5,329 full-scene images with a long side of at least 4,000 pixels, with up to about 3 x 10^8 pixels per image, sourced from widely used RS corpora and UAV collections. We design four task families: multiple-choice VQA, open-ended VQA, image captioning, and single-image evaluation. These tasks cover nine perception categories and four reasoning types, supporting multi-turn and multi-image dialog. To reduce reliance on language priors, we apply adversarial filtering with strong LLMs followed by rigorous human verification. Overall, we construct 3,864 VQA tasks, 3,913 image captioning tasks, and 500 fully human-written or verified single-image evaluation VQA pairs. Evaluations across open-source, closed-source, and RS-specific VLMs reveal persistent performance gaps in super-high-resolution scenarios. Code: https://github.com/Yunkaidang/RSHR

cs.CV↗

arXiv2025

All-Electrical Self-Switching of van der Waals Chiral Antiferromagnet

Antiferromagnets have garnered significant attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and ultrafast spintronic applications. Their dual functionality as both spin sources and information carriers could enable all-electrical self-induced switching of antiferromagnetic order, offering great potential for ultra-compact spintronic devices. However, related progress is still elusive. Here, we report the deterministic switching of chiral antiferromagnetic orders induced by charge current at zero external magnetic field in the van der Waals (vdW) magnetically intercalated transition metal dichalcogenide CoTa3S6. This system exhibits strong interactions between cobalt atom magnetic moment lattice and itinerant electrons within the metallic layers, as demonstrated by temperature-dependent angle-resolved photoemission, scanning tunneling spectroscopy, and topological Nernst effect measurements. Notably, the itinerant-localization interactions lead to current-induced chiral spin orbit torques as well as Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange torques that interact with the localized magnetic moments, facilitating all-electrical switching of the chiral magnetic order in the CoTa3S6 flake. Our work opens a promising avenue for manipulating antiferromagnetic orders by delicately engineering the synergistic interactions between magnetic moments and itinerant electrons.

cond-mat.mes-hall↗

arXiv2025

Dimensionality-Driven Anomalous Metallic State with Zero-field Nonreciprocal Transport in Layered Ising Superconductors

The anomalous metal state (AMS), observed in failed superconductors, provides insights into superconductivity and quantum criticality, with studies revealing unconventional quantum phases like the Bose metal. Recently, layered transition metal dichalcogenide (TMD) superconductors approaching the two-dimensional limit have garnered significant attention for the enhanced phase fluctuations and electronic correlations. Investigating AMS in these systems, particularly in the absence of an external magnetic field, could offer valuable insights into the dimensionality-driven emergence of exotic quantum phenomena, including triplet Cooper pairing, phase fluctuation dynamics, and especially the recently discovered field-free superconducting diode effects. However, the field-free AMS has yet to be observed in TMD superconductors. Here, we report the dimensionality-tunable AMS near the superconducting quantum phase transitions in a layered TMD superconductor 2H-Ta2S3Se. In samples with thicknesses below 10 nm, we demonstrate magnetic field-driven AMS under external magnetic field, characterized by the vanishing of the Hall resistance and the presence of finite longitudinal resistance. Remarkably, an unexpected zero-field AMS emerges as the sample thickness is reduced to 3 nm. This AMS aligns well with the quantum vortex creep model and exhibits non-reciprocal transport behaviors, suggesting the onset of spontaneous time-reversal symmetry breaking accompanied by vortex motion as the system approaches the two-dimensional limit. Our findings open new avenues for exploring dimensionality-driven exotic superconducting quantum critical phases, and pave the way for a deeper understanding of zero-field superconducting diode effects.

cond-mat.supr-con↗

arXiv2025

Skin-inspired in-sensor encoding of strain vector using tunable quantum geometry

Human skin provides crucial tactile feedback, allowing us to skillfully perceive various objects by sensing and encoding complex deformations through multiple parameters in each tactile receptor. However, replicating this high-dimensional tactile perception with conventional materials' electronic properties remains a daunting challenge. Here, we present a skin-inspired method to encode strain vectors directly within a sensor. This is achieved by leveraging the strain-tunable quantum properties of electronic bands in the van der Waals topological semimetal Td -WTe2. We observe robust and independent responses from the second-order and third-order nonlinear Hall signals in Td -WTe2 when subjected to variations in both the magnitude and direction of strain. Through rigorous temperature-dependent measurements and scaling law analysis, we establish that these strain responses primarily stem from quantum geometry-related phenomena, including the Berry curvature and Berry-connection polarizability tensor. Furthermore, our study demonstrates that the strain-dependent nonlinear Hall signals can efficiently encode high-dimensional strain information using a single device. This capability enables accurate and comprehensive sensing of complex strain patterns in the embossed character "NJU". Our findings highlight the promising application of topological quantum materials in advancing next-generation, bio-inspired flexible electronics.

cond-mat.mes-hall↗

arXiv2024

Selective and Quasi-continuous Switching of Ferroelectric Chern Insulator Device for Neuromorphic Computing

Topologically protected edge state transport in quantum materials is dissipationless and features quantized Hall conductance, and shows great potential in highly fault-tolerant computing technologies. However, it remains elusive about how to develop topological edge state-based computing devices. Recently, exploration and understanding of interfacial ferroelectricity in various van der Waals heterostructure material systems have received widespread attention among the community of materials science and condensed matter physics3-11. Such ferroelectric polarization emergent at the vdW interface can coexist with other quantum states and thus provides an unprecedented opportunity to electrically switch the topological edge states of interest, which is of crucial significance to the fault-tolerant electronic device applications based on the topological edge states. Here, we report the selective and quasi-continuous ferroelectric switching of topological Chern insulator devices and demonstrate its promising application in noise-immune neuromorphic computing. We fabricate this ferroelectric Chern insulator device by encapsulating magic-angle twisted bilayer graphene with doubly-aligned h-BN layers, and observe the coexistence of the interfacial ferroelectricity and the topological Chern insulating states. This ferroelectricity exhibits an anisotropic dependence on the in-plane magnetic field. By using a VBG pulse with delicately controlled amplitude, we realize the nonvolatile switching between any pair of Chern insulating states and achieve 1280 distinguishable nonvolatile resistance levels on a single device. Furthermore, we demonstrate deterministic switching between two arbitrary levels among the record-high number of nonvolatile resistance levels.

cond-mat.mes-hall↗

arXiv2024

Electrical switching of Ising-superconducting nonreciprocity for quantum neuronal transistor

Nonreciprocal quantum transport effect is mainly governed by the symmetry breaking of the material systems and is gaining extensive attention in condensed matter physics. Realizing electrical switching of the polarity of the nonreciprocal transport without external magnetic field is essential to the development of nonreciprocal quantum devices. However, electrical switching of superconducting nonreciprocity remains yet to be achieved. Here, we report the observation of field-free electrical switching of nonreciprocal Ising superconductivity in Fe3GeTe2/NbSe2 van der Waals (vdW) heterostructure. By taking advantage of this electrically switchable superconducting nonreciprocity, we demonstrate a proof-of-concept nonreciprocal quantum neuronal transistor, which allows for implementing the XOR logic gate and faithfully emulating biological functionality of a cortical neuron in the brain. Our work provides a promising pathway to realize field-free and electrically switchable nonreciprocity of quantum transport and demonstrate its potential in exploring neuromorphic quantum devices with both functionality and performance beyond the traditional devices.

cond-mat.mes-hall↗

arXiv2024

Tunable moir\'e bandgap in hBN-aligned bilayer graphene device with in-situ electrostatic gating

Over the years, great efforts have been devoted in introducing a sizable and tunable band gap in graphene for its potential application in next-generation electronic devices. The primary challenge in modulating this gap has been the absence of a direct method for observing changes of the band gap in momentum space. In this study, we employ advanced spatial- and angle-resolved photoemission spectroscopy technique to directly visualize the gap formation in bilayer graphene, modulated by both displacement fields and moir\'e potentials. The application of displacement field via in-situ electrostatic gating introduces a sizable and tunable electronic bandgap, proportional to the field strength up to 100 meV. Meanwhile, the moir\'e potential, induced by aligning the underlying hexagonal boron nitride substrate, extends the bandgap by ~ 20 meV. Theoretical calculations, effectively capture the experimental observations. Our investigation provides a quantitative understanding of how these two mechanisms collaboratively modulate the band gap in bilayer graphene, offering valuable guidance for the design of graphene-based electronic devices.

cond-mat.mes-hall↗

arXiv2024

Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure

The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant T-odd SHE in Fe3GeTe2/MoTe2 van der Waals heterostructure, representing a previously unidentified interfacial magnetic spin Hall effect (interfacial-MSHE). Through rigorous symmetry analysis and theoretical calculations, we attribute the interfacial-MSHE to a symmetry-breaking induced spin current dipole at the vdW interface. Furthermore, we show that this linear effect can be used for implementing multiply-accumulate operations and binary convolutional neural networks with cascaded multi-terminal devices. Our findings uncover an interfacial T-odd charge-spin conversion mechanism with promising potential for energy-efficient in-memory computing.

cond-mat.mes-hall↗

arXiv2023

Moire synaptic transistor for homogeneous-architecture reservoir computing

Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information. Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the reservoir and the readout layer of reservoir computing system. Two-dimensional moire material, with an artificial lattice constant many times larger than the atomic length scale, is one type of most studied artificial quantum materials in community of material science and condensed-matter physics over the past years. These materials are featured with gate-tunable periodic potential and electronic correlation, thus varying the electric field allows the electrons in the moire potential per unit cell to exhibit distinct and reproducible dynamics, showing great promise in robust reservoir computing. Here, we report that a moire synaptic transistor can be used to implement the reservoir computing system with a homogeneous reservoir-readout architecture. The synaptic transistor is fabricated based on a h-BN/bilayer graphene/h-BN moire heterostructure, exhibiting ferroelectricity-like hysteretic gate voltage dependence of resistance. Varying the magnitude of the gate voltage enables the moire transistor to be switched between long-term memory and short-term memory with nonlinear dynamics. By employing the short- and long-term memory as the reservoir nodes and weights of the readout layer, respectively, we construct a full-moire physical neural network and demonstrate that the classification accuracy of 90.8% can be achieved for the MNIST handwritten digit database. Our work would pave the way towards the development of neuromorphic computing based on the moire materials.

cond-mat.mes-hall↗

arXiv2023

Parallel in-memory wireless computing

Parallel wireless digital communication with ultralow power consumption is critical for emerging edge technologies such as 5G and Internet of Things. However, the physical separation between digital computing units and analogue transmission units in traditional wireless technology leads to high power consumption. Here we report a parallel in-memory wireless computing scheme. The approach combines in-memory computing with wireless communication using memristive crossbar arrays. We show that the system can be used for the radio transmission of a binary stream of 480 bits with a bit error rate of 0. The in-memory wireless computing uses two orders of magnitude less power than conventional technology (based on digital-to-analogue and analogue-to-digital converters). We also show that the approach can be applied to acoustic and optical wireless communications

cs.AR↗

arXiv2022

Approaching intrinsic threshold breakdown voltage and ultra-high gain in graphite/InSe Schottky photodetector

Realizing both ultra-low breakdown voltage and ultra-high gain has been one of the major challenges in the development of high-performance avalanche photodetector. Here, we report that an ultra-high avalanche gain of 3*10^5 can be realized in the graphite/InSe Schottky photodetector at a breakdown voltage down to 5.5 V. Remarkably, the threshold breakdown voltage can be further reduced down to 1.8 V by raising the operating temperature, approaching the theoretical limit of 1.5E_g/e with E_g the band gap of semiconductor. We develop a two-dimensional impact ionization model and uncover that observation of high gain at low breakdown voltage arises from reduced dimensionality of electron-phonon (e-ph) scattering in the layered InSe flake. Our findings open up a promising avenue for developing novel weak-light detectors with low energy consumption and high sensitivity.

physics.app-ph↗