Reproducing human nervous systems with endogenous mechanisms has attracted
increasing attention, driven by its great potential in streamlining the neuro‐electronic …

Memristor devices for resistive-switching memory and artificial synapses have emerged as
promising solutions for overcoming the technological challenges associated with the von …

Organic memristors based on covalent organic frameworks (COFs) exhibit significant
potential for future neuromorphic computing applications. The preparation of high‐quality …

Natural plant-material-based memory devices have been in the spotlight due to their
versatile applications ranging from nonvolatile memory to neuromorphic computations …

As traditional von Neumann architectures face limitations in handling the demands of big
data and complex computational tasks, neuromorphic computing has emerged as a …

The human brain possesses a highly developed capability for sense-memory-computation,
and the integration of hardware with brain-like functions represents a novel approach to …

Hafnium oxide (HfO x) films are highly valued as functional layers in nonvolatile resistive
switching (RS) memristors due to their scalability, compatibility with CMOS technology, and …

Machine learning (ML) accelerators represent an attractive area of research, offering the
potential to streamline algorithmic complexity and handle massively parallel in-memory …

Conventional computing architectures are not suited to meet the unique workload
requirements of artificial intelligence and deep learning, which has sparked a growing …

We report on the impact of the growth method and substrate-induced crystalline quality on
the performance of planar Al/Gd 0.2 Ca 0.8 MnO 3/Au memristor devices. Structural …