In spintronics, the magnetic moment of electrons is used to transfer and manipulate information. An ultra-compact 2D spin-logic circuitry could be built from 2D materials that can transport the spin ...

Memristors, or memory resistors, are components that adjust their resistance based on past electrical activity, effectively storing a memory of it. While most existing memristors are solid-state ...

A collaboration between teams from the National Graphene Institute (NGI) at The University of Manchester, and the École Normale Supérieure (ENS), Paris, demonstrated Hebbian learning in artificial ...

Two-dimensional covalent organic frameworks are a unique type of organic crystals with both weak layer-layer interaction and regular one-dimensional nanochannels. Therefore, it is possible to ...

A technical paper titled “Probing Optical Multi-Level Memory Effects in Single Core–Shell Quantum Dots and Application Through 2D-0D Hybrid Inverters” was published by researchers at Korea Institute ...

“In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus exploring homogeneous devices for these components is an important ...

Memory technology has played a pivotal role in the development of the semiconductor industry over the past decades, driven by the explosive growth of massive data storage and desire of ultrafast data ...

Hebbian learning is a well-known learning mechanism, it is the process when we ‘get used’ to doing an action. Similar to what occurs in neural networks, the researchers were able to show the existence ...