A fast 600-V Tandem PiN Schottky (TPS) rectifier with ultra-low on-state voltage

The Tandem PiN Schottky (TPS) rectifier features lowly-doped p-layers in both active and termination regions, and is applied in 600-V rating for the first time. In the active region, the Schottky contact is in series connection with a transparent p-layer, leading to a superior forward performance than the conventional diodes. In addition, due to the benefit of moderate hole injection from the p-layer, the TPS offers a better trade-off between the on-state voltage and the switching speed. The active p-layer also helps to stabilise the Schottky contact, and hence the electrical data distributions are more concentrated. Regarding the floating p-layer in the termination region, its purpose is to reduce the peak electric fields, and the TPS demonstrates a high breakdown voltage with a compact termination width, less than 70% of the state-of-the-art devices on the market. Experimental results have shown that the 600-V TPS rectifier has an ultra-low on-state voltage of 0.98 V at 250 A/cm 2, a fast turn-off time of 75 ns by the standard RG1 test (I F=0.5A, I R=1A, and I RR=0.25A) and a breakdown voltage over 720 V. It is noteworthy that the p-layers in the active and termination regions can be formed at no extra cost for the use of self-alignment process. © 2012 IEEE.

Impact of high-k dielectrics on breakdown performances of SiC and diamond Schottky diodes

This paper presents a comparison between SiC and diamond Schottky barrier diodes using the oxide ramp termination. The influences of the dielectric thickness and relative permittivity on the diode's electrical performance are investigated. Typical commercial drift layer parameters are used for this study. The extension of the space charge area throughout the drift region and the current distribution at breakdown are shown. The efficiency of the termination is also evaluated for both SiC and diamond diodes. © (2009) Trans Tech Publications, Switzerland.

Breakdown voltage for superjunction power devices with charge imbalance: An analytical model valid for both punch through and non punch through devices

An analytical model for the electric field and the breakdown voltage (BV) of an unbalanced superjunction (SJ) device is presented in this paper. The analytical technique uses a superposition approach treating the asymmetric charge in the pillars as an excess charge component superimposed on a balanced charge component. The proposed double-exponentialmodel is able to accurately predict the electric field and the BV for unbalanced SJ devices in both punch through and non punch through conditions. The model is also reasonably accurate at extremely high levels of charge imbalance when the devices behave similarly to a PiN diode or to a high-conductance layer. The analytical model is compared against numerical simulations of charge unbalanced SJ devices and against experimental results. © 2009 IEEE.

Structural sensitivity of spiral vortex breakdown

Previous numerical simulations have shown that vortex breakdown starts with the formation of a steady axisymmetric bubble and that an unsteady spiralling mode then develops on top of this. We investigate this spiral mode with a linear global stability analysis around the steady bubble and its wake. We obtain the linear direct and adjoint global modes of the linearized Navier-Stokes equations and overlap these to obtain the structural sensitivity of the spiral mode, which identifies the wavemaker region. We also identify regions of absolute instability with a local stability analysis. At moderate swirls, we find that the m=-1 azimuthal mode is the most unstable and that the wavemaker regions of the m=-1 mode lie around the bubble, which is absolutely unstable. The mode is most sensitive to feedback involving the radial and azimuthal components of momentum in the region just upstream of the bubble. To a lesser extent, the mode is also sensitive to feedback involving the axial component of momentum in regions of high shear around the bubble. At an intermediate swirl, in which the bubble and wake have similar absolute growth rates, other researchers have found that the wavemaker of the nonlinear global mode lies in the wake. We agree with their analysis but find that the regions around the bubble are more influential than the wake in determining the growth rate and frequency of the linear global mode. The results from this paper provide the first steps towards passive control strategies for spiral vortex breakdown. © 2013 Cambridge University Press.

High-density remote plasma sputtering of high-dielectric-constant amorphous hafnium oxide films

Hafnium oxide (HfOx) is a high dielectric constant (k) oxide which has been identified as being suitable for use as the gate dielectric in thin film transistors (TFTs). Amorphous materials are preferred for a gate dielectric, but it has been an ongoing challenge to produce amorphous HfOx while maintaining a high dielectric constant. A technique called high target utilization sputtering (HiTUS) is demonstrated to be capable of depositing high-k amorphous HfOx thin films at room temperature. The plasma is generated in a remote chamber, allowing higher rate deposition of films with minimal ion damage. Compared to a conventional sputtering system, the HiTUS technique allows finer control of the thin film microstructure. Using a conventional reactive rf magnetron sputtering technique, monoclinic nanocrystalline HfOx thin films have been deposited at a rate of ∼1.6nmmin-1 at room temperature, with a resistivity of 1013Ωcm, a breakdown strength of 3.5MVcm-1 and a dielectric constant of ∼18.2. By comparison, using the HiTUS process, amorphous HfOx (x=2.1) thin films which appear to have a cubic-like short-range order have been deposited at a high deposition rate of ∼25nmmin-1 with a high resistivity of 1014Ωcm, a breakdown strength of 3MVcm-1 and a high dielectric constant of ∼30. Two key conditions must be satisfied in the HiTUS system for high-k HfOx to be produced. Firstly, the correct oxygen flow rate is required for a given sputtering rate from the metallic target. Secondly, there must be an absence of energetic oxygen ion bombardment to maintain an amorphous microstructure and a high flux of medium energy species emitted from the metallic sputtering target to induce a cubic-like short range order. This HfOx is very attractive as a dielectric material for large-area electronic applications on flexible substrates. A remote plasma sputtering process (high target utilization sputtering, HiTUS) has been used to deposit amorphous hafnium oxide with a very high dielectric constant (∼30). X-ray diffraction shows that this material has a microstructure in which the atoms have a cubic-like short-range order, whereas radio frequency (rf) magnetron sputtering produced a monoclinic polycrystalline microstructure. This is correlated to the difference in the energetics of remote plasma and rf magnetron sputtering processes. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.

The breakdown of the viscous regime in riblets

We investigate the mechanisms involved in the breakdown of the viscous regime in riblets, with a view to determining the point of optimum performance, where drag reduction ceases to be proportional to the riblet size. This occurs empirically for a groove cross-section $A_g^+ \approx 120^+$. To study the interaction of the riblets with the overlaying turbulent flow, we systematically conduct DNSes in a ribbed turbulent channel with increasing riblet size. The conditionally averaged crossflow above and within the grooves reveals a mean recirculation bubble that exists up to the point of viscous breakdown, isolating the groove floor from the overlying crossflow, and preventing the high momentum fluid from entering the grooves. We do not find evidence of outside vortices lodging within the grooves until $A_g^+ \approx 400$, which is well past the drag minimum, and already into the drag increasing regime. Interestingly, as the bubble breaks down, we observe that quasi-two-dimensional spanwise structures form just above the riblets, similar to those observed above porous surfaces and plant canopies, which appear to be involved in the performance degradation.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.