Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Porous crystalline materials usually include metal–organic frameworks (MOFs), covalent
organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites …

Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …

Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …

Artificial neurons and synapses are considered essential for the progress of the future brain‐
inspired computing, based on beyond von Neumann architectures. Here, a discussion on …

With the booming growth of artificial intelligence (AI), the traditional von Neumann
computing architecture based on complementary metal oxide semiconductor devices are …

Quantum materials exhibit dissipationless topological edge state transport with quantized
Hall conductance, offering notable potential for fault-tolerant computing technologies …

Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …

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 …

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior
that is assembled with a physically integrated core processing unit (CPU) and memory unit …