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 …

Despite technical efforts and upgrades, advances in complementary metal–oxide–
semiconductor circuits have become unsustainable in the face of inherent silicon limits. New …

Memristive devices can offer dynamic behaviour, analogue programmability, and scaling
and integration capabilities. As a result, they are of potential use in the development of …

Due to the constraints imposed by physical effects and performance degradation, silicon-
based chip technology is facing certain limitations in sustaining the advancement of Moore's …

Abstract Two-dimensional (2D) materials could potentially be used to develop advanced
monolithic integrated circuits. However, despite impressive demonstrations of single devices …

Two-dimensional materials (2DMs) have attracted a great deal of interest due to their
immense potential for scientific breakthroughs and technological innovations. While some …

Data-driven algorithms—such as signal processing and artificial neural networks—are
required to process and extract meaningful information from the massive amounts of data …

The conventional computing architecture faces substantial challenges, including high
latency and energy consumption between memory and processing units. In response, in …

The miniaturization of metal–oxide–semiconductor field-effect transistors (MOSFETs) has
been the driving force behind the development of integrated circuits over the past 60 years; …

Memristor crossbar with programmable conductance could overcome the energy
consumption and speed limitations of neural networks when executing core computing tasks …