97 research outputs found
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
and 0.413x 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
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