arXiv subjects

Junyi Yang

Publications and source records attributed to Junyi Yang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Uniaxial spin texture in a superconducting electron gas revealed by exchange interactions

Two-dimensional superconductors with spin-textured Fermi surfaces can be a platform for realizing unconventional pairing states and are of substantial interest in the context of quantum information science, and superconducting spintronics/orbitronics. We observed an unusual in-plane uniaxial anisotropy in the superconducting 2D electron gas (2DEG) formed at EuOx/KTaO3 (110) interfaces, where the EuOx is magnetic. This anisotropy is not evident in AlOx/KTaO3 (110) where the overlayer is non-magnetic. Our results are consistent with a highly anisotropic 'half-Rashba' spin-textured Fermi surface in 2DEGs formed at the KTaO3 (110) interface that is hidden from external magnetic fields due to a near cancellation between orbital and spin moments but revealed by exchange interactions of the electrons in the 2DEG with Eu moments near the EuOx/KTaO3 (110) interface. The interactions between the uniaxial spin texture and the magnetic overlayer offer new ways to explore the interplay between magnetism and 2D superconductivity.

cond-mat.supr-con↗

arXiv2025

Large asymmetric anomalous Nernst effect in the antiferromagnet SrIr$_{0.8}$Sn$_{0.2}$O$_3$

A large anomalous Nernst effect is essential for thermoelectric energy harvesting in the transverse geometry without external magnetic field. It is often connected with anomalous Hall effect, especially when electronic Berry curvature is believed to be the driving force. This approach implicitly assumes the same symmetry for the Nernst and Hall coefficients, which is however not necessarily true. Here we report a large anomalous Nernst effect in antiferromagnetic SrIr$_{0.8}$Sn$_{0.2}$O$_3$ that defies the antisymmetric constraint on the anomalous Hall effect imposed by the Onsager reciprocal relation. The observed spontaneous Nernst thermopower quickly reaches the sub-$μ$V/K level below the Néel transition around 250 K, which is comparable with many topological antiferromagnetic semimetals and far excels other magnetic oxides. Our analysis indicates that the coexistence of significant symmetric and antisymmetric contributions plays a key role, pointing to the importance of extracting both contributions and a new pathway to enhanced anomalous Nernst effect for transverse thermoelectrics

cond-mat.mtrl-sci↗

arXiv2025

AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies

The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs. Yet, applying generative AI techniques to accelerate the design of analog ICs remains a significant challenge due to critical domain-specific issues, such as the lack of a comprehensive dataset and effective representation methods for analog circuits. This paper proposes, $\textbf{AnalogGenie}$, a $\underline{\textbf{Gen}}$erat$\underline{\textbf{i}}$ve $\underline{\textbf{e}}$ngine for automatic design/discovery of $\underline{\textbf{Analog}}$ circuit topologies--the most challenging and creative task in the conventional manual design flow of analog ICs. AnalogGenie addresses two key gaps in the field: building a foundational comprehensive dataset of analog circuit topology and developing a scalable sequence-based graph representation universal to analog circuits. Experimental results show the remarkable generation performance of AnalogGenie in broadening the variety of analog ICs, increasing the number of devices within a single design, and discovering unseen circuit topologies far beyond any prior arts. Our work paves the way to transform the longstanding time-consuming manual design flow of analog ICs to an automatic and massive manner powered by generative AI. Our source code is available at https://github.com/xz-group/AnalogGenie.

cs.LG↗

arXiv2025

Multi-source Multi-level Multi-token Ethereum Dataset and Benchmark Platform

This paper introduces 3MEthTaskforce (https://3meth.github.io), a multi-source, multi-level, and multi-token Ethereum dataset addressing the limitations of single-source datasets. Integrating over 300 million transaction records, 3,880 token profiles, global market indicators, and Reddit sentiment data from 2014-2024, it enables comprehensive studies on user behavior, market sentiment, and token performance. 3MEthTaskforce defines benchmarks for user behavior prediction and token price prediction tasks, using 6 dynamic graph networks and 19 time-series models to evaluate performance. Its multimodal design supports risk analysis and market fluctuation modeling, providing a valuable resource for advancing blockchain analytics and decentralized finance research.

cs.CE↗

arXiv2024

Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models. In this work, we experimentally demonstrate the implementation of a non-linear activation function integrated with a ramp analog-to-digital conversion (ADC) at the periphery of the memory to improve in-memory implementation of RNNs. Our approach uses an extra column of memristors to produce an appropriately pre-distorted ramp voltage such that the comparator output directly approximates the desired nonlinear function. We experimentally demonstrate programming different nonlinear functions using a memristive array and simulate its incorporation in RNNs to solve keyword spotting and language modelling tasks. Compared to other approaches, we demonstrate manifold increase in area-efficiency, energy-efficiency and throughput due to the in-memory, programmable ramp generator that removes digital processing overhead.

cs.AR↗

arXiv2024

Topkima-Former: Low-energy, Low-Latency Inference for Transformers using top-k In-memory ADC

Transformer model has gained prominence as a popular deep neural network architecture for neural language processing (NLP) and computer vision (CV) applications. However, the extensive use of nonlinear operations, like softmax, poses a performance bottleneck during transformer inference and comprises up to 40% of the total latency. Hence, we propose innovations at the circuit, architecture, and algorithm levels to accelerate the transformer. At the circuit level, we propose topkima-combining top-k activation selection with in-memory ADC (IMA) to implement a low-energy and low-latency softmax without any sorting latency. Only the k largest activations are sent to the softmax calculation block, reducing the huge computational cost of softmax. Using a modified training scheme with top-k only in the forward pass, experimental results demonstrate only a 0.4% to 1.2% reduction in accuracy across ViT, distilBERT, and BERT-base models when evaluated on CIFAR-10, CIFAR-100, and SQuAD datasets with k=5. At the architecture level, an improved scale-free technique is introduced to reduce the computational cost of attention. The combined system, dubbed Topkima-Former, enhances 1.8x-84x speedup and 1.3x-35x energy efficiency (EE) over prior In-memory computing (IMC) accelerators. Compared to a conventional softmax macro and a digital top-k (Dtopk) softmax macro, our proposed tokima softmax macro achieves about 15x and 8x faster speed respectively.

cs.AR↗

arXiv2024

Redefining Information Retrieval of Structured Database via Large Language Models

Retrieval augmentation is critical when Language Models (LMs) exploit non-parametric knowledge related to the query through external knowledge bases before reasoning. The retrieved information is incorporated into LMs as context alongside the query, enhancing the reliability of responses towards factual questions. Prior researches in retrieval augmentation typically follow a retriever-generator paradigm. In this context, traditional retrievers encounter challenges in precisely and seamlessly extracting query-relevant information from knowledge bases. To address this issue, this paper introduces a novel retrieval augmentation framework called ChatLR that primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval. Additionally, we construct an LLM-based search and question answering system tailored for the financial domain by fine-tuning LLM on two tasks including Text2API and API-ID recognition. Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8\%.

cs.IR↗

arXiv2024

Anomalous Proximitized Transport in Metal/Quantum Magnet Heterostructure $\rm{Bi_{2}Ir_{2}O_{7}/Yb_{2}Ti_{2}O_{7}}$

Fluctuations of quantum spins play a crucial role in the emergence of exotic magnetic phases and excitations. The lack of the charge degree of freedom in insulating quantum magnets, however, precludes such fluctuations from mediating electronic transport. Here we show that the quantum fluctuations of a localized frustrated magnet induce strong proximitized charge transport of the conduction electrons in a synthetic heterostructure comprising an epitaxial $\rm{Bi_{2}Ir_{2}O_{7}}$ ultrathin film on the single crystal of $\rm{Yb_{2}Ti_{2}O_{7}}$. The proximity effects are evidenced by the scaling behavior of the $\rm{Bi_{2}Ir_{2}O_{7}}$ resistance in correspondance with the dynamic scaling of the dynamic spin correlation function of $\rm{Yb_{2}Ti_{2}O_{7}}$, which is a result of quantum fluctuations near a multi-phase quantum critical point. The proximitized transport in $\rm{Bi_{2}Ir_{2}O_{7}}$ can be effectively tuned by magnetic field through suppressing the quantum spin fluctuations as well as inducing transitions via magnetic anisotropy in $\rm{Yb_{2}Ti_{2}O_{7}}$. Our work establishes a new pathway for harnessing quantum spin fluctuations in magnetic insulators with electric transport, offering exciting prospects for potential applications in the realm of quantum spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Revealing subterahertz atomic vibrations in quantum paraelectrics by surface-sensitive spintronic terahertz spectroscopy

Understanding surface collective dynamics in quantum materials is crucial for advancing quantum technologies. For example, surface phonon modes in quantum paraelectrics are thought to play an essential role in facilitating interfacial superconductivity. However, detecting these modes, especially below 1 terahertz (THz), is challenging due to limited sampling volumes and the need for high spectroscopic resolution. Here, we report surface soft transverse optical (TO1) phonon dynamics in KTaO3 and SrTiO3 by developing surface-sensitive spintronic THz spectroscopy that can sense the collective modes only a few nanometers deep from the surface. In KTaO3, the TO1 mode softens and sharpens with decreasing temperature, leveling off at 0.7 THz. In contrast, this mode in SrTiO3 broadens significantly below the quantum paraelectric crossover and coincides with the hardening of a sub-meV phonon mode related to the antiferrodistortive transition. These observations that deviate from their bulk properties may have implications for interfacial superconductivity and ferroelectricity. The developed technique opens opportunities for sensing low-energy surface excitations.

cond-mat.mtrl-sci↗

arXiv2024

Sketched Nanoscale KTaO3-Based Superconducting Quantum Interference Device

The discovery of two-dimensional superconductivity in LaAlO3/KTaO3 (111) and (110) interfaces has raised significant interest in this system. In this manuscript we report the first successful fabrication of a superconducting quantum interference device (DC-SQUID) in the KTO system. The key device elements, superconducting weak links, are created by conductive atomic force microscope (c-AFM) lithography which can reversibly control the conductivity at the LAO/KTO(110) interface with nanoscale resolution. The periodic modulation of the SQUID critical current, Ic(B), with magnetic field corresponds well with our theoretical modeling, which reveals a large kinetic inductance of the superconducting two-dimensional electron gas in KTO. The kinetic inductance of the SQUID is tunable by electrical gating from the back, due to the large dielectric constant of KTO. The demonstration of weak links and SQUIDs in KTO broadens the scope for exploring the underlying physics of KTO superconductivity, including the role of spin-orbit-coupling, pairing symmetry, and inhomogeneity. It also promotes KTO as a versatile platform for a growing family of quantum devices, which could be applicable in the realm of quantum computing and information.

cond-mat.supr-con↗

arXiv2024

Dual-grating single-shot pump-probe technique

A simple and effective single-shot pump-probe technique is reported for studying the ultrafast dynamic processes in various materials. Using only two commercial gratings, a large time window of ~ 95.58 ps is spatially encoded in a single probe pulse, and single-shot time-resolved measurements are implemented. This time window exceeds the maximum reported values for single-shot pump-probe techniques using the echelon or angle beam encoding strategy. The phase difference problem in the echelon encoding strategies is also eliminated and a customized echelon is not needed in this technique. The ultrafast dynamic processes of ZnSe and indolium squaraine at a wavelength of 650 nm were investigated using this technique.

physics.optics↗

arXiv2024

Intelligent EC Rearview Mirror: Enhancing Driver Safety with Dynamic Glare Mitigation via Cloud Edge Collaboration

Sudden glare from trailing vehicles significantly increases driving safety risks. Existing anti-glare technologies such as electronic, manually-adjusted, and electrochromic rearview mirrors, are expensive and lack effective adaptability in different lighting conditions. To address these issues, our research introduces an intelligent rearview mirror system utilizing novel all-liquid electrochromic technology. This system integrates IoT with ensemble and federated learning within a cloud edge collaboration framework, dynamically controlling voltage to effectively eliminate glare and maintain clear visibility. Utilizing an ensemble learning model, it automatically adjusts mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on the test set. Furthermore, the system leverages federated learning for distributed data training across devices, which enhances privacy and updates the cloud model continuously. Distinct from conventional methods, our experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be convenient and costeffective, this system demonstrates how IoT and AI can significantly enhance rearview mirror anti-glare performance.

cs.HC↗

arXiv2023

Single-shot pump-probe technique by the combination of an echelon and a grating with a time window of 109 ps

In this study, using only a single pulse, pump-probe measurement with a large time window of more than 100 ps is implemented. A commercial grating is used to encode a time window of ~ 56 ps in a single pulse; therefore, there is no need for machining customization. In addition, in this technique, the grating surface is accurately imaged, eliminating the image blur problem caused by phase differences in previous echelon-based techniques. Moreover, to make full use of the grating surface and obtain a larger time window, a simple reflection echelon is combined that matches the grating in the time window. This combination encoding strategy results in a total time window of ~ 109 ps and maintains accurate imaging of the grating surface. This time window is an order of magnitude greater than the maximum reported values of the echelon encoding strategy and the angle beam encoding strategy. To demonstrate this single-shot pump-probe technique, the two-photon absorption process of ZnSe and the excited-state absorption process of a symmetrical phenoxazinium bromine salt were studied. The possibility of further improving the experimental setup is also discussed.

physics.optics↗

arXiv2023

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission. We propose a novel deep learning based framework where the procedures of pilot design, channel feedback, and hybrid beamforming are realized by carefully crafted deep neural networks. All the considered modules are jointly learned in an end-to-end manner, and a graph neural network is adopted to effectively capture interactions between beamformers based on the built graphical representation. Numerical results validate the effectiveness of our method.

eess.SP↗

arXiv2023

Extraordinary magnetic response of an anisotropic 2D antiferromagnet via site-dilution

A prominent character of two-dimensional magnetic systems is the enhanced spin fluctuations, which however reduce the ordering temperature. Here we report that a magnetic field of only one-thousandth of the Heisenberg superexchange interaction can induce a crossover, which for practical purposes is the effective ordering transition, at temperatures about 6 times of the Neel transition in a site-diluted two-dimensional anisotropic quantum antiferromagnet. Such a strong magnetic response is enabled because the system directly enters the antiferromagnetically ordered state from the isotropic disordered state skipping the intermediate anisotropic stage. The underlying mechanism is achieved on a pseudospin-half square lattice realized in the [(SrIrO3)1/(SrTiO3)2] superlattice thin film that is designed to linearly couple the staggered magnetization to external magnetic fields by virtue of the rotational symmetry-preserving Dzyaloshinskii Moriya interaction. Our model analysis shows that the skipping of the anisotropic regime despite the finite anisotropy is due to the enhanced isotropic fluctuations under moderate dilution.

cond-mat.str-el↗

arXiv2023

Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach

Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss. To tackle the challenging problem, we propose a novel deep learning-based hierarchical beam alignment method for both multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems, which learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine search manner. Specifically, a hierarchical beam alignment network (HBAN) is developed for MISO systems, which first performs coarse channel measurement using a tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The propounded HBAN is trained in two steps: the tier-1 PC and the tier-2 PC selector are first trained jointly, followed by the joint training of all the tier-2 PCs and beam predictors. Furthermore, an HBAN for MIMO systems is proposed to directly predict the optimal beam pair without performing beam alignment individually at the transmitter and receiver. Numerical results demonstrate that the proposed HBANs are superior to the state-of-art methods in both alignment accuracy and signaling overhead reduction.

eess.SP↗

arXiv2022

Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.

eess.SP↗

arXiv2022

LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches

With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network. Aside from terrestrial base stations, satellite nodes, especially the low-earth-orbit (LEO) satellites, can also serve as base stations for Internet access, and constitute the LEO-satellite-based access network (LEO-SAN). LEO-SAN is expected to provide seamless massive access and extended coverage with high signal quality. However, its practical implementation still faces significant technical challenges, e.g., high mobility and limited budget for communication payloads of LEO satellite nodes. This paper aims at revealing the main technical issues that have not been fully addressed by the existing LEO-SAN designs, from three aspects namely random access, beam management and Doppler-resistant transmission technologies. More specifically, the critical issues of random access in LEO-SAN are discussed regarding low flexibility, long transmission delay, and inefficient handshakes. Then the beam management for LEO-SAN is investigated in complex propagation environments under the constraints of high mobility and limited payload budget. Furthermore, the influence of Doppler shifts on LEO-SAN is explored. Correspondingly, promising technologies to address these challenges are also discussed, respectively. Finally, the future research directions are envisioned.

eess.SP↗