Results 181 to 190 of about 1,405,029 (363)

Ferroelectric Hafnium Oxide: A Potential Game‐Changer for Nanoelectronic Devices and Systems

open access: yesAdvanced Electronic Materials, EarlyView.
Devices based on ferroelectric hafnium oxide have spurred interest in a wide range of nanoelectronic applications. This review focuses on the advantages and challenges of these devices, including non‐volatile memories, neuromorphic devices, sensors, actuators, and RF devices.
David Lehninger   +8 more
wiley   +1 more source

Ravel-XL: a hardware accelerator for assigned-delay compiled-code logic gate simulation [PDF]

open access: green, 2002
Michael A. Riepe   +3 more
openalex   +1 more source

High Mobility, High Carrier Density SnSe2 Field‐Effect Transistors with Ultralow Subthreshold Swing and Gate‐Controlled Photoconductance Switching

open access: yesAdvanced Electronic Materials, EarlyView.
Field‐effect transistors with layered SnSe2 channel gated by a solution top gate combine very high room‐temperature electron mobility, large on‐off current ratios, and a subthreshold swing below the thermodynamic limit at exceptionally high sheet carrier concentrations.
Yuan Huang   +3 more
wiley   +1 more source

Development of Partial Discharge Automated Measuring System Using Multiple Logic Gates

open access: bronze, 2000
Min Chen   +4 more
openalex   +2 more sources

Soft Electronic Switches and Adaptive Logic Gates Based on Nanostructured Gold Networks

open access: yesAdvanced Electronic Materials, EarlyView.
Reconfigurable threshold logic gates and reversible switches are here demonstrated on a flexible and stretchable substrate, by exploiting the adaptability of the complex network composed of gold cluster‐assembled film embedded in the polymer matrix.
Giacomo Nadalini   +5 more
wiley   +1 more source

Convolutional Differentiable Logic Gate Networks [PDF]

open access: yesarXiv
With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed. Logic gate networks are faster than conventional neural network approaches because their inference only ...
arxiv  

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