Due to the voltage driven switching at low voltages combined with nonvolatility of the
achieved polarization state, ferroelectric materials have a unique potential for low power …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

The emergence of memristors with potential applications in data storage and artificial
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …

The superior density of passive analog-grade memristive crossbar circuits enables storing
large neural network models directly on specialized neuromorphic chips to avoid costly off …

In this study, resistive random-access memory (RRAM)-based crossbar arrays with a
memristor W/TiO 2/HfO 2/TaN structure were fabricated through atomic layer deposition …

Building bidirectional biointerfaces is one of the key challenges of modern engineering and
medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of …

The shift towards a distributed computing paradigm, where multiple systems acquire and
elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming …

A memristor can comprehensively emulate the neural components rather than imitating a
single characteristic superficially due to its analog and hysteretic resistive switching. Bio …

The design of modern convolutional artificial neural networks (ANNs) composed of formal
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …

Neuromorphic computing can process large amounts of information in parallel and provides
a powerful tool to solve the von Neumann bottleneck. Constructing an artificial neural …