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

Collaborative socially responsible practices for improving the position of Chinese workers in global supply chains

In this paper we evaluate three projects with the participation of 40 supplier firms in several Chinese coastal provinces representing multi-stakeholder efforts to provide alternative channels through which workers can voice their concerns. The supplier firms took on these projects to reduce worker dissatisfaction and employee turnover. The projects fill an institutional void in employer–employee relations within Chinese supplier firms as they provide alternative channels for workers to voice their concerns. The role of civil society organisations focusing on labour interests was a crucial feature of the projects, through capacity-building for workers and by providing independence. The supplier firms and their workers have benefitted as firms take measures to enhance worker satisfaction, while the reduced employee turnover positively impacted firm performance. We propose that these collaborative socially responsible practices are a potential way to strengthen the positions of workers and supplier firms in global supply chain

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, pro ling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how e ciently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations

STUDY OF EXISTING MODES OF REDUCTIONS DURING PRODUCTION OF RAILWAY AXLES

Study of existing modes of reductions during production of railway axles