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

Memristors are promising candidates for constructing neural networks. However, their
dissimilar working mechanism to that of the addressing transistors can result in a scaling …

Neuromorphic systems built from memristors that emulate bioelectrical information
processing in the brain may overcome the limitations of traditional computing architectures …

Time‐series analysis and forecasting play a vital role in the fields of economics and
engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has …

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic
computing systems has attracted tremendous interest. However, existing analog memristors …

To fulfill the demands of three-dimensional (3D) cross-point array architectures in embedded
memories, selectors with low voltage and low off current with high selectivity are imperative …

Threshold‐switching devices based on amorphous chalcogenides are considered for use as
selector devices in 3D crossbar memories. However, the fundamental understanding of …

The transistor is broadly used to address memristor networks, but its three-terminal structure
can impose limitations on fully exploiting the potential of efficient integration that a two …

Laser irradiation, as a kind of post-fabrication method for two-dimensional (2D) materials, is
a promising way to tune the properties of materials and the performance of corresponding …

High-density nonvolatile memories can be realized by fabricating resistive random-access
memories (RRAMs) as 4F2 (F: minimum feature size) cells in a crossbar array (CBA) …