Conventional artificial intelligence (AI) systems are facing bottlenecks due to the
fundamental mismatches between AI models, which rely on parallel, in-memory, and …

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic
memristive and memristive‐like devices exhibit an attractive future in constructing novel …

In‐memory computing based on memristor arrays holds promise to address the speed and
energy issues of the classical von Neumann computing system. However, the stochasticity of …

Memristor-enabled in-memory computing provides an unconventional computing paradigm
to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the …

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural
network (NN) computations, but studies on these devices are limited to software-based …

Memristors based on two-dimensional (2D) materials can exhibit great scalability and
ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin …

To apply resistive random‐access memory (RRAM) to the neuromorphic system and
improve performance, each cell in the array should be able to operate independently by …

This study presents a method to enhance data processing by integrating a unidirectional
analogue artificial neuromorphic memristor device with a piezoelectric nanogenerator …

Memristors integrated into a crossbar‐array architecture (CAA) are promising candidates for
nonvolatile memory elements in artificial neural networks. However, the relatively low …

Smart memristors with innovative properties are crucial for the advancement of next‐
generation information storage and bioinspired neuromorphic computing. However, the …