Low partial pressure chemical vapor deposition of graphene on copper

A systematic study of the Cu-catalyzed chemical vapor deposition of graphene under extremely low partial pressure is carried out. A carbon precursor supply of just P CH4∼ 0.009 mbar during the deposition favors the formation of large-area uniform monolayer graphene verified by Raman spectra. A diluted HNO 3 solution is used to remove Cu before transferring graphene onto SiO 2/Si substrates or carbon grids. The graphene can be made suspended over a ∼12 μm distance, indicating its good mechanical properties. Electron transport measurements show the graphene sheet resistance of ∼0.6 kΩ/□ at zero gate voltage. The mobilities of electrons and holes are ∼1800 cm 2/Vs at 4.2 K and ∼1200 cm 2/Vs at room temperature. © 2011 IEEE.

200-V lateral superjunction LIGBT on partial SOI

This letter presents a novel lateral superjunction lateral insulated-gate bipolar transistor (LIGBT) in partial silicon-on-insulator (SOI) technology in 0.18-μm partial-SOI (PSOI) high-voltage (HV) process. For an n-type superjunction LIGBT, the p-layer in the superjunction drift region not only helps in achieving uniform electric field distribution but also contributes to the on-state current. The superjunction LIGBT successfully achieves a breakdown voltage (BV) of 210 V with an R dson of 765 mΩ ̇ mm 2. It exhibits half the value of specific on-state resistance R dson and three times higher saturation current (I dsat) for the same BV, compared to a comparable lateral superjunction laterally diffused metal-oxide-semiconductor fabricated in the same technology. It also performs well in higher temperature dc operation with 38.8% increase in R dson at 175°C, compared to the room temperature without any degradation in latch-up performance. To realize this device, it only requires one additional mask layer into X-FAB 0.18-μm PSOI HV process. © 2012 IEEE.

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

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.

Riemannian metric and geometric mean for positive semidefinite matrices of fixed rank

This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive cone and the associated natural metric. The resulting Riemannian space has strong geometrical properties: it is geodesically complete, and the metric is invariant with respect to all transformations that preserve angles (orthogonal transformations, scalings, and pseudoinversion). A meaningful approximation of the associated Riemannian distance is proposed, that can be efficiently numerically computed via a simple algorithm based on SVD. The induced mean preserves the rank, possesses the most desirable characteristics of a geometric mean, and is easy to compute. © 2009 Society for Industrial and Applied Mathematics.

Synchronization with partial state feedback on SO(n)

In this paper we consider the problem of constructing a distributed feedback law to achieve synchronization for a group of k agents whose states evolve on SO(n) and which exchange only partial state information along communication links. The partial state information is given by the action of the state on reference vectors in ℝn. We propose a gradient based control law which achieves exponential local convergence to a synchronization configuration under a rank condition on a generalized Laplacian matrix. Furthermore, we discuss the case of time-varying reference vectors and provide a convergence result for this case. The latter helps reach synchronization, requiring less communication links and weaker conditions on the instantaneous reference vectors. Our methods are illustrated on an attitude synchronization problem where agents exchange only their relative positions observed in the respective body frames. ©2009 IEEE.

200V superjunction lateral IGBT fabricated on partial SOI

A 200V lateral insulated gate bipolar transistor (LIGBT) was successfully developed using lateral superjunction (SJ) in 0.18μm partial silicon on insulator (SOI) HV process. The results presented are based on extensive experimental measurements and numerical simulations. For an n-type lateral SJ LIGBT, the p layer in the SJ drift region helps in achieving uniform electric field distribution. Furthermore, the p-pillar contributes to the on-state current. Furthermore, the p-pillar contributes to sweep out holes during the turn-off process, thus leading to faster removal of plasma. To realize this device, one additional mask layer is required in the X-FAB 0.18μm partial SOI HV process. © 2013 IEEE.

A fixed-grid b-spline finite element technique for fluid-structure interaction

We present a fixed-grid finite element technique for fluid-structure interaction problems involving incompressible viscous flows and thin structures. The flow equations are discretised with isoparametric b-spline basis functions defined on a logically Cartesian grid. In addition, the previously proposed subdivision-stabilisation technique is used to ensure inf-sup stability. The beam equations are discretised with b-splines and the shell equations with subdivision basis functions, both leading to a rotation-free formulation. The interface conditions between the fluid and the structure are enforced with the Nitsche technique. The resulting coupled system of equations is solved with a Dirichlet-Robin partitioning scheme, and the fluid equations are solved with a pressure-correction method. Auxiliary techniques employed for improving numerical robustness include the level-set based implicit representation of the structure interface on the fluid grid, a cut-cell integration algorithm based on marching tetrahedra and the conservative data transfer between the fluid and structure discretisations. A number of verification and validation examples, primarily motivated by animal locomotion in air or water, demonstrate the robustness and efficiency of our approach. © 2013 John Wiley & Sons, Ltd.