arXiv subjects

Yubo Yuan

Publications and source records attributed to Yubo Yuan.

7 recordsLinked to original sources

arXiv2026

Ab initio study of carrier mobility in Bi$_2$O$_2$Se

Bi$_2$O$_2$Se is an emerging high-performance layered semiconductor with excellent stability. While experimental studies have explored carrier transport across various doping levels for both $n$-type and $p$-type conduction, a comprehensive theoretical understanding remains incomplete. In this work, we present parameter-free first-principles calculations of the electron and hole mobilities in Bi$_2$O$_2$Se, based on iterative solution of the Boltzmann transport equation that includes electron-phonon scattering and ionized impurity scattering on an equal footing. Intriguingly, we find that Bi$_2$O$_2$Se exhibits high electron mobilities in both the in-plane and out-of-plane directions, whereas the hole mobilities are only significant in the in-plane direction, displaying a unique three-dimensional (3D) electron transport and two-dimensional (2D) hole transport behavior. At 300~K, the calculated intrinsic electron and hole mobilities along the in-plane direction are 447~$\mathrm{cm^2\,V^{-1}\,s^{-1}}$ and 29~$\mathrm{cm^2\,V^{-1}\,s^{-1}}$, respectively, which are primarily affected by Fr\"ohlich electron-phonon interactions. Due to its large static dielectric permittivity, Bi$_2$O$_2$Se exhibits an exceptionally high low-temperature electron mobilities above $1.0\times10^5~\mathrm{cm^2\,V^{-1}\,s^{-1}}$, and its electron mobilities above 50~K is robust against ionized impurity scattering over a wide range of impurity concentrations. By incorporating the Hall effect into our analysis, we predict an in-plane electron Hall mobility of 517~$\mathrm{cm^2\,V^{-1}\,s^{-1}}$ at 300~K, in excellent agreement with experimental data. These results provide valuable insights into the carrier transport mechanisms in Bi$_2$O$_2$Se, and offer predictive benchmarks for future theoretical and experimental investigations.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelectricity in 6 Angstrom-Thick Two-dimensional Ga$_2$O$_3$

Atomic-scale ferroelectric thin films hold great promise for high-density, low-power applications but face stability and voltage scaling challenges at extreme thinness. Here, we demonstrate ferroelectricity in single-crystalline two-dimensional (2D) Ga$_2$O$_3$, an ultra-wide-bandgap semiconductor, at just 6 angstrom thickness, exhibiting exceptional retention and thermal stability. We show that epitaxial beta-Ga$_2$O$_3$ can be exfoliated down to a half-unit cell thickness via a self-limiting mechanism, enabling a biaxial strain-induced phase transition into a novel ferroelectric layered structure. Strain modulation enables the reduction of polarization switching voltage to 0.8 V, meeting CMOS voltage scaling requirements. Theoretical calculations reveal that switching is driven by covalent bond reconstruction, effectively countering depolarization and enhancing stability. Additionally, we integrate ferroelectric 2D Ga$_2$O$_3$ onto silicon using a low-temperature, back-end-of-line-compatible process. This work advances the exploration of sub-nanometer ferroelectrics, paving the way for high-density, low-power, non-volatile applications seamlessly integrated with advanced silicon technology.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Geometric Origin of Strain-Induced Ferroelectric Phase Transitions

Strain-regulated ferroelectric (FE) materials have long attracted significant attention due to their diverse applications. While soft-phonon theory and the (pseudo) Jahn-Teller effect have achieved considerable success in providing phenomenological descriptions and general understanding, the detailed connection between these perspectives and their microscopic dependence on strain regulation remains unclear. Here, under the framework of density-functional perturbation theory (DFPT), we demonstrate that the Berry curvature of electron-phonon coupling (EPC) plays a pivotal role in the interatomic force matrix (IFM). A subsequent model analysis shows that external strain can reverse the polarity of the EPC Berry curvature in (quasi)-degenerate electronic subsystems through band inversion, thereby directly leading to phonon softening. The general theory is then applied to the BiOCl monolayer as a benchmark, which offers an accurate description of the density functional theory (DFT) calculations. This mechanism is further observed across a broad range of materials through ab initio calculations, providing an insightful perspective on EPC quantum geometry in lattice dynamics and FE phase transitions.

cond-mat.mtrl-sci↗

arXiv2024

Highly Polarizable Semiconductors and Universal Origin of Ferroelectricity in Materials with a Litharge-Type Structural Unit

We discover that a large family of [Pb$_2$F$_2$]- and [Bi$_2$O$_2$]-based mixed-anion materials with a litharge-type structural unit are highly polarizable layered semiconductors on the edge of ferroelectricity. First-principles calculations demonstrate that in this family of materials, compounds as diverse as PbFBr, BiOCl, BiCuOSe, Bi$_2$OS$_2$, and Bi$_5$O$_4$S$_3$Cl exhibit static dielectric constants an order of magnitude higher than typical semiconductors. Additionally, they undergo a ferroelectric transition when subjected to a few percent of tensile strain. The ferroelectric transitions of these materials are found to have a universal origin in the strong cross-bandgap hybridization of the cation $p$ orbitals, enabled by the cation 6s$^2$ lone-pair electrons and the litharge-type structure of the [Pb$_2$F$_2$] and [Bi$_2$O$_2$] layers, as demonstrated by the strain-induced ferroelectric transition in the archetypal litharge $α$-PbO. These results establish materials with a litharge-type structural unit as a large and versatile family of highly polarizable layered semiconductors in proximity to ferroelectricity, offering vast opportunities for multifunctional materials design.

cond-mat.mtrl-sci↗

arXiv2022

Boost Distribution System Restoration with Emergency Communication Vehicles Considering Cyber-Physical Interdependence

Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication networks after disasters, in this paper, we propose an integrated distribution system restoration (DSR) framework and optimization models, which can coordinate the repair crews, the distribution system (physical sectors), and the emergency communication (cyber sectors) to pick up unserved power loads as quickly as possible. Case studies validated the effectiveness of the proposed models and proved the benefit of considering ECVs and cyber-physical interdependencies in DSR.

eess.SY↗

arXiv2019

Dynamic Region Division for Adaptive Learning Pedestrian Counting

Accurate pedestrian counting algorithm is critical to eliminate insecurity in the congested public scenes. However, counting pedestrians in crowded scenes often suffer from severe perspective distortion. In this paper, basing on the straight-line double region pedestrian counting method, we propose a dynamic region division algorithm to keep the completeness of counting objects. Utilizing the object bounding boxes obtained by YoloV3 and expectation division line of the scene, the boundary for nearby region and distant one is generated under the premise of retaining whole head. Ulteriorly, appropriate learning models are applied to count pedestrians in each obtained region. In the distant region, a novel inception dilated convolutional neural network is proposed to solve the problem of choosing dilation rate. In the nearby region, YoloV3 is used for detecting the pedestrian in multi-scale. Accordingly, the total number of pedestrians in each frame is obtained by fusing the result in nearby and distant regions. A typical subway pedestrian video dataset is chosen to conduct experiment in this paper. The result demonstrate that proposed algorithm is superior to existing machine learning based methods in general performance.

cs.CV↗

arXiv2014

A study on effectiveness of extreme learning machine

Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.

cs.NE↗