Graphene-like quaternary compound SiBCN: a new wide direct band gap semiconductor predicted by a first-principles study

Due to the lack of two-dimensional silicon-based semiconductors and the fact that most of the components and devices are generated on single-crystal silicon or silicon-based substrates in modern industry, designing two-dimensional silicon-based semiconductors is highly desired. With the combination of a swarm structure search method and density functional theory in this work, a quaternary compound SiBCN with graphene-like structure is found and displays a wide direct band gap as expected. The band gap is of ~2.63 eV which is just between ~2.20 and ~3.39 eV of the highlighted semiconductors SiC and GaN. Notably, the further calculation reveals that SiBCN possesses high carrier mobility with ~5.14x10^3 and ~13.07x10^3 cm^2V^-1s^-1 for electron and hole, respectively. Furthermore, the ab initio molecular dynamics simulations also show that the graphene-like structure of SiBCN can be well kept even at an extremely high temperature of 2000 K. The present work tells that designing ulticomponent silicides may be a practicable way to search for new silicon-based low-dimensional semiconductors which can match well with the previous Si-based substrates

Substrate-induced half-metallic property in epitaxial silicene

For most practical applications in electronic devices, two-dimensional materials should be transferred onto semiconducting or insulating substrates, since they are usually generated on metallic substrates. However, the transfer often leads to wrinkles, damages, contaminations and so on which would destroy the intrinsic properties of samples. Thus, generating two-dimensional materials directly on nonmetallic substrates has been a desirable goal for a long time. Here, via a swarm structure search method and density functional theory, we employed an insulating N-terminated cubic boron nitride(111) surface as a substrate for the generation of silicene. The result shows that the silicene behaves as a ferromagnetic half-metal because of the strong interaction between silicon and surface nitrogen atoms. The magnetic moments are mainly located on surface nitrogen sites without bonding silicon atoms and the value is about 0.12 uB. In spin-up channel, it behaves as a direct band gap semiconductor with a gap of around 1.35 eV, while it exhibits metallic characteristic in spin-down channel, and the half-metallic band gap is about 0.11 eV. Besides, both the magnetic and electronic properties are not sensitive to the external compressive strain. This work maybe open a way for the utility of silicene in spintronic field

Atomically thin mononitrides SiN and GeN: new two-dimensional semiconducting materials

Low-dimensional Si-based semiconductors are unique materials that can both match well with the Si-based electronics and satisfy the demand of miniaturization in modern industry. Owing to the lack of such materials, many researchers put their efforts into this field. In this work, employing a swarm structure search method and density functional theory, we theoretically predict two-dimensional atomically thin mononitrides SiN and GeN, both of which present semiconducting nature. Furthermore study shows that SiN and GeN behave as indirect band gap semiconductors with the gap of 1.75 and 1.20 eV, respectively. The ab initio molecular dynamics calculation tells that both two mononitrides can exist stably even at extremely high temperature of 2000 K. Notably, electron mobilities are evaluated as 0.888x$10^3$ $cm^2V^{-1}s^{-1}$ and 0.413x$10^3$ $cm^2V^{-1}s^{-1}$ for SiN and GeN, respectively. The present work expands the family of low-dimensional Si-based semiconductors.Comment: arXiv admin note: text overlap with arXiv:1703.0389

One-Shot Fine-Grained Instance Retrieval

Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification.Comment: Accepted by MM2017, 9 pages, 7 figure

SimSwap: An Efficient Framework For High Fidelity Face Swapping

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using this module, we extend the architecture of an identity-specific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art methods. The code is already available on github: https://github.com/neuralchen/SimSwap.Comment: Accepted by ACMMM 202

Spin Logic Devices via Electric Field Controlled Magnetization Reversal by Spin-Orbit Torque

We describe a spin logic device with controllable magnetization switching of perpendicularly magnetized ferromagnet/heavy metal structures on a ferroelectric (1-x)[Pb(Mg 1/3 Nb 2/3 )O 3 ]-x[PbTiO 3 ] (PMN-PT) substrate using current-induced spin-orbit torque. The devices were operated without an external magnetic field and controlled by voltages as low as 10 V applied across the PMN-PT substrate, which is much lower compared with the previous reports (500 V). The deterministic switching with smaller voltage was realized from the virgin state of the PMN-PT. The ferroelectric simulation shows the unsaturated minor loop exhibits obvious asymmetries in the polarizations. Larger polarization can be induced from the initial ferroelectric state, while it is difficult for opposite polarization. The XNOR, AND, NAND and NOT logic functions were demonstrated by the deterministic magnetization switching from the interaction between the spin-orbit torque and electric field at the PMN-PT/Pt interface. The nonvolatile spin logic scheme in this letter is simple, scalable and programmable, which are favorable in the logic-in-memory design with low energy consumption