Semiconductor memories are essential ingredients of modern electronic devices. Resistive
Random-Access Memories (RRAMs) have emerged as better alternatives for conventional …

The aqueous electrolyte can be a deformable and stretchable liquid material for iontronic
resistive memory devices. An aqueous medium makes a device closer to the brain‐like …

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

Controllable multilevel resistive switching (RS) and neuromorphic characteristics emerges
as a promising paradigm to build power-efficient computing hardware for high density data …

Distinct from the conductive filament-type counterparts, the interface-type resistive switching
(RS) devices are electroforming-free and exhibit bidirectionally continuous conductance …

Resistive random-access memory (RRAM) is a promising technology to develop nonvolatile
memory and artificial synaptic devices for brain-inspired neuromorphic computing. Here, we …

Given the limitations of von Neumann computing systems, we propose a high-performance
reservoir computing system as an alternative. These systems operate as neural networks …

Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable
attention over filament‐type (FT) devices for neuromorphic computing because of their …

Ferroelectric memristors, as one of the most potential non-volatile memory to meet the rapid
development of the artificial intelligence era, have the comprehensive function of simulating …

Serving as neuromorphic hardware accelerators, memristors play a crucial role in large-
scale neuromorphic computing. Herein, two-terminal memristors utilizing amorphous indium …