Critical couplings and string tensions via lattice matching of RG decimations

We calculate critical couplings and string tensions in SU(2) and SU(3) pure lattice gauge theory by a simple and inexpensive technique of two-lattice matching of RG block transformations. The transformations are potential moving decimations generating plaquette actions with large number of group characters and exhibit rapid approach to a unique renormalized trajectory. Fixing the critical coupling $\beta_c(N_\tau)$ at one value of temporal lattice length $N_\tau$ by MC simulation, the critical couplings for any other value of $N_\tau$ are then obtained by lattice matching of the block decimations. We obtain $\beta_c(N_\tau)$ values over the range $N_\tau = 3 - 32$ and find agreement with MC simulation results to within a few percent in all cases. A similar procedure allows the calculation of string tensions with similarly good agreement with MC data.Comment: 12 pages, Latex, 1 figur

Statistical variability and reliability in nanoscale FinFETs

A comprehensive full-scale 3D simulation study of statistical variability and reliability in emerging, scaled FinFETs on SOI substrate with gate-lengths of 20nm, 14nm and 10nm and low channel doping is presented. Excellent electrostatic integrity and resulting tolerance to low channel doping are perceived as the main FinFET advantages, resulting in a dramatic reduction of statistical variability due to random discrete dopants (RDD). It is found that line edge roughness (LER), metal gate granularity (MGG) and interface trapped charges (ITC) dominate the parameter fluctuations with different distribution features, while RDD may result in relatively rare but significant changes in the device characteristics

Mapping of AlxGa1–xAs band edges by ballistic electron emission spectroscopy

We have employed ballistic electron emission microscopy (BEEM) to study the energy positions in the conduction band of AlxGa1 – xAs. Epilayers of undoped AlxGa1 – xAs were grown by molecular beam epitaxy on conductive GaAs substrates. The Al composition x took on values of 0, 0.11, 0.19, 0.25, 0.50, 0.80 and 1 so that the material was examined in both the direct and indirect band gap regime. The AlxGa1 – xAs layer thickness was varied from 100 to 500 Å to ensure probing of bulk energy levels. Different capping layers and surface treatments were explored to prevent surface oxidation and examine Fermi level pinning at the cap layer/AlxGa1 – xAs interface. All samples were metallized ex situ with a 100 Å Au layer so that the final BEEM structure is of the form Au/capping layer/AlxGa1 – xAs/bulk GaAs. Notably we have measured the Schottky barrier height for Au on AlxGa1 – xAs. We have also probed the higher lying band edges such as the X point at low Al concentrations and the L point at high Al concentrations. Variations of these critical energy positions with Al composition x were mapped out in detail and compared with findings from other studies. Local variations of these energy positions were also examined and found to be on the order of 30–50 meV. The results of this study suggest that BEEM can provide accurate positions for multiple energy levels in a single semiconductor structure

Effect of cylindrical geometry on the wet thermal oxidation of AlAs

We have investigated the wet thermal oxidation of AlAs in cylindrical geometry, a typical configuration for vertical-cavity surface-emitting lasers. Through both experiment and theoretical calculations, we demonstrate a significantly different time dependence for circular mesas from what has been reported in the literature both in studies of stripes and in a study of circular mesas. We attribute this different time dependence to the effect of geometry on the oxidation

Strain in wet thermally oxidized square and circular mesas

In this paper, we report the observation, through optical microscopy, of drumhead-like patterns in square and circular mesas which have been wet thermally oxidized to completion. Micro-Raman spectroscopy measurements are used to show that these patterns roughly correspond to variations in strain induced in surrounding semiconductor layers by the oxidation process. In addition, the patterns have a specific orientation with respect to the crystallographic axes of the semiconductor. A crystallographic dependence of the oxidation process itself is demonstrated and used to explain the orientation of the drumhead patterns

Tunnel switch diode based on AlSb/GaSb heterojunctions

We report on tunnel switch diodes based on AlSb barriers and GaSb p–n junctions grown by molecular beam epitaxy. These were the devices with thyristor like switching in the GaSb/AlSb system. The characteristic "S" shaped current–voltage curve was found to occur for structures with AlSb barriers less than 300 Å thick. The switching voltage and current density exhibited less sensitivity to barrier and epilayer thickness than was predicted by the punch-through model. The results were correlated with drift diffusion simulations which have been modified to account for the presence of a tunneling contact

Time Quantified Monte Carlo Algorithm for Interacting Spin Array Micromagnetic Dynamics

In this paper, we reexamine the validity of using time quantified Monte Carlo (TQMC) method [Phys. Rev. Lett. 84, 163 (2000); Phys. Rev. Lett. 96, 067208 (2006)] in simulating the stochastic dynamics of interacting magnetic nanoparticles. The Fokker-Planck coefficients corresponding to both TQMC and Langevin dynamical equation (Landau-Lifshitz-Gilbert, LLG) are derived and compared in the presence of interparticle interactions. The time quantification factor is obtained and justified. Numerical verification is shown by using TQMC and Langevin methods in analyzing spin-wave dispersion in a linear array of magnetic nanoparticles.Comment: Accepted for publication in Phys. Rev.

Accurate simulations of the interplay between process and statistical variability for nanoscale FinFET-based SRAM cell stability

In this paper we illustrate how by using advanced atomistic TCAD tools the interplay between long-range process variation and short-range statistical variability in FinFETs can be accurately modelled and simulated for the purposes of Design-Technology Co-Optimization (DTCO). The proposed statistical simulation and compact modelling methodology is demonstrated via a comprehensive evaluation of the impact of FinFET variability on SRAM cell stability

Soft computing for intelligent data analysis

Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. The paper briefly looks at some of the key issues in intelligent data analysis, discusses the opportunities for soft computing in this context, and presents several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies

Learning context-aware outfit recommendation

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching