Results 131 to 140 of about 8,787 (345)

SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range

open access: yesAdvanced Electronic Materials, EarlyView.
A physics‐guided compact model for YBCO memristors is introduced, incorporating carrier trapping, field‐induced detrapping, and a differential balance equation to describe their switching dynamics. The model is compared with experiments and implemented in LTspice, allowing realistic circuit‐level simulations.
Thomas Günkel   +6 more
wiley   +1 more source

Silicon Nitride Resistive Memories

open access: yesAdvanced Electronic Materials, EarlyView.
Amorphous SiNx is an attractive resistance switching material for ReRAM applications due to its physicochemical properties, such as humidity resistance, low oxygen diffusivity, and is used as a metal diffusion blocker. By modifying the ratio between N and Si atoms, the microstructure of the SiNx is affected, rendering it possible to change the ...
Alexandros‐Eleftherios Mavropoulis   +7 more
wiley   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez   +10 more
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

open access: yesAdvanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj   +2 more
wiley   +1 more source

Nonlocal problems with Neumann boundary conditions

open access: yes, 2014
We introduce a new Neumann problem for the fractional Laplacian arising from a simple probabilistic consideration, and we discuss the basic properties of this model. We can consider both elliptic and parabolic equations in any domain. In addition,we formulate problems with nonhomogeneous Neumann conditions, and also with mixed Dirichlet and Neumann ...
Dipierro, Serena   +2 more
openaire   +2 more sources

Roughness effect on the Neumann boundary condition

open access: yes, 2011
36 pagesInternational audienceWe study the effect of a periodic roughness on a Neumann boundary condition. We show that, as in the case of a Dirichlet boundary condition, it is possible to approach this condition by a more complex law on a domain without
Laurent Chupin, Chupin, Laurent
core  

Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks

open access: yesAdvanced Intelligent Discovery, EarlyView.
Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç   +2 more
wiley   +1 more source

Lp estimates for Dirichlet-to-Neumann operator and applications

open access: yesElectronic Journal of Differential Equations, 2015
In this article, we consider the time dependent linear elliptic problem with dynamic boundary condition. We recall the corresponding Dirichlet-to-Neumann operator on $\Gamma$ denoted by $-\Lambda_\gamma$.
Toufic El Arwadi, Toni Sayah
doaj  

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

open access: yesAdvanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy