Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

Efforts to design devices emulating complex cognitive abilities and response processes of
biological systems have long been a coveted goal. Recent advancements in flexible …

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

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge
devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory …

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

Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

The ability to scale two-dimensional (2D) material thickness down to a single monolayer
presents a promising opportunity to realize high-speed energy-efficient memristors. Here …

Owing to its close resemblance to biological systems and materials, soft matter has been
successfully implemented in numerous bioelectronic and biosensing applications, as well as …

Implementations of artificial neural networks that borrow analogue techniques could
potentially offer low-power alternatives to fully digital approaches,–. One notable example is …