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Graphene lateral spin valves (LSVs) are promising devices for future memory and magnetic field sensing applications. In this article, we study the dependence of the nonlocal spin resistance, $\Delta {R}_{{\text {NL}}}$ , and the baseline resistance, ${R}_{{\text {BS}}}$ , as a function of the graphene channel width, ${W}$. The scaling trend is quantitatively assessed by using graphene deposited by chemical vapor deposition, which provides a large number of devices with consistent performance. As ${W}$ is scaled from 10 to $0.5~\mu \text{m}$ , the change in $\Delta {R}_{{\text {NL}}}$ matches the theory of contact-induced spin relaxation with a current spin polarization of 3%–5% and a spin diffusion length of $\lambda _{s} = 1.5$ – $2.5~\mu \text{m}$. We also observe a systematic and dramatic decrease in ${R}_{{\text {BS}}}$ , which we attribute to the reduction in charge current spreading. However, we find in the narrowest devices that a small ${R}_{{\text {BS}}}$ remains that arises due to thermoelectric effects, and this trend is confirmed using gate voltage- and charge current-dependent analyses. Finally, we introduce a nonideality factor, ${m} = \vert {R}_{{\text {BS}}} / \Delta {R}_{{\text {NL}}}\vert $ , as a figure of merit to describe the suppression of the baseline relative to the spin signal. In an LSV with ${L} = {1.5}\,\,\mu \text{m}$ , ${W} = {0.5}\,\,\mu \text{m}$ , and n-type conduction, the nonideality factor is as low as ${m} = {0.0252}\,\,\pm \,\,0.0202$ at room temperature showing that nearly ideal bipolar and symmetric spin signals can be achieved in graphene LSVs. [ABSTRACT FROM AUTHOR]

Stochastic computing (SC) is a promising technology that can be used for low-cost hardware designs. However, SC suffers from its long latency. Although parallel processing can efficiently shorten the latency, duplicated stochastic number generators (SNGs) are necessary, which cause substantial hardware overhead. This paper proposes a scalable SNG based on the spin-Hall-effect (SHE), which is capable of generating multiple independent stochastic streams simultaneously. The design takes advantages of the efficient charge-to-spin conversion from the Spin-Hall material and the intrinsic stochasticity of nanomagnets. Compared to previous spintronic SNGs, the SHE-SNG can reduce the area by $1.6\times -7.8\times $ and the power by $4.9\times -13\times $ while increasing the degree of parallelism from 1 to 16. Compared to CMOS-based SNGs, the proposed SNG obtained $24\times -120\times $ and $53\times $ reduction in terms of area and power, respectively. Finally, three benchmarks were implemented, and the results indicate that SC implementations with the proposed SHE-SNG can achieve $1.2\times -29\times $ reduction of hardware resources compared to implementations with previous CMOS- and spintronic-based designs while scaling the degree of parallelism from 1 to 64. [ABSTRACT FROM AUTHOR]

An ac small signal model for lateral spin valves (LSVs) is developed, and is applied to two different types of devices: spin transmitters and magnetic field sensors. The small signal models explicitly decouple the charge- and spin-dependent components, which describe the intrinsic RC delay and the ac spin accumulation dynamics, separately. The analysis is applied to graphene LSVs as an example to illustrate the optimization strategies for LSV devices. By using the charge-spin circuit model, the scattering parameters and the reflection coefficients at the output terminals are quantitatively calculated as a function of frequency. This model can be used as a guide for future LSV design and to benchmark performance across different material systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach based on spectral geometry to quantify and visualize non-isometric deformations of 3D surfaces by mapping two manifolds. The proposed method can determine multi-scale, non-isometric deformations through the variation of Laplace-Beltrami spectrum of two shapes. Given two triangle meshes, the spectra can be varied from one to another with a scale function defined on each vertex. The variation is expressed as a linear interpolation of eigenvalues of the two shapes. In each iteration step, a quadratic programming problem is constructed, based on our derived spectrum variation theorem and smoothness energy constraint, to compute the spectrum variation. The derivation of the scale function is the solution of such a problem. Therefore, the final scale function can be solved by integral of the derivation from each step, which, in turn, quantitatively describes non-isometric deformations between two shapes. To evaluate the method, we conduct extensive experiments on synthetic and real data. We employ real epilepsy patient imaging data to quantify the shape variation between the left and right hippocampi in epileptic brains. In addition, we use longitudinal Alzheimer data to compare the shape deformation of diseased and healthy hippocampus. In order to show the accuracy and effectiveness of the proposed method, we also compare it with spatial registration-based methods, e.g., non-rigid Iterative Closest Point (ICP) and voxel-based method. These experiments demonstrate the advantages of our method. [ABSTRACT FROM AUTHOR]

A composite free layer spin-transfer torque random access memory (STT-RAM) cell is proposed for ultra-high density memory. The structure consists of three layers—a high anisotropy interior layer and two low anisotropy outer layers that assist the switching of the interior layer via exchange coupling. Efficiencies ( kBT/\mu \text {A}) of 4.5 and 4.1 are achieved for the proposed structure with perpendicular and longitudinal anisotropies, respectively. An efficiency of 4.3 is obtained for the state-of-the-art single-layer dual-referenced structure with perpendicular anisotropy. Simulation of the conventional single-layer structure with perpendicular anisotropy yields an efficiency of 1.6. Therefore, the proposed structure with perpendicular anisotropy achieves an improvement of 5% and 181% relative to the state-of-the-art dual-referenced and conventional STT-RAM cells, respectively. Furthermore, use of low anisotropy assistive layers enables reduction of Gilbert damping and an increase of partial spin polarization within those low anisotropy layers—not feasible with single layer structures that require high anisotropy for thermal stability. This significantly increases perpendicular and longitudinal efficiencies to 8.5 and 6.8, respectively. Therefore, this augmented proposed structure with perpendicular anisotropy achieves an improvement of 99% and 446% relative to the state-of-the-art dual-referenced and conventional STT-RAM cells, respectively. [ABSTRACT FROM PUBLISHER]

In order to visualize and analyze complex collective data, complicated geometric structure of each data is desired to be mapped onto a canonical domain to enable map-based visual exploration. This paper proposes a novel volume-preserving mapping and registration method which facilitates effective collective data visualization. Given two 3-manifolds with the same topology, there exists a mapping between them to preserve each local volume element. Starting from an initial mapping, a volume restoring diffeomorphic flow is constructed as a compressible flow based on the volume forms at the manifold. Such a flow yields equality of each local volume element between the original manifold and the target at its final state. Furthermore, the salient features can be used to register the manifold to a reference template by an incompressible flow guided by a divergence-free vector field within the manifold. The process can retain the equality of local volume elements while registering the manifold to a template at the same time. An efficient and practical algorithm is also presented to generate a volume-preserving mapping and a salient feature registration on discrete 3D volumes which are represented with tetrahedral meshes embedded in 3D space. This method can be applied to comparative analysis and visualization of volumetric medical imaging data across subjects. We demonstrate an example application in multimodal neuroimaging data analysis and collective data visualization. [ABSTRACT FROM PUBLISHER]

Parameterization of complex surfaces constitutes a major means of visualizing highly convoluted geometric structures as well as other properties associated with the surface. It also enables users with the ability to navigate, orient, and focus on regions of interest within a global view and overcome the occlusions to inner concavities. In this paper, we propose a novel area-preserving surface parameterization method which is rigorous in theory, moderate in computation, yet easily extendable to surfaces of non-disc and closed-boundary topologies. Starting from the distortion induced by an initial parameterization, an area restoring diffeomorphic flow is constructed as a Lie advection of differential 2-forms along the manifold, which yields equality of the area elements between the domain and the original surface at its final state. Existence and uniqueness of result are assured through an analytical derivation. Based upon a triangulated surface representation, we also present an efficient algorithm in line with discrete differential modeling. As an exemplar application, the utilization of this method for the effective visualization of brain cortical imaging modalities is presented. Compared with conformal methods, our method can reveal more subtle surface patterns in a quantitative manner. It, therefore, provides a competitive alternative to the existing parameterization techniques for better surface-based analysis in various scenarios. [ABSTRACT FROM AUTHOR]