Highlights The fully-integrated memristor chip for edge learning provides a solid foundation
for neural network computation. The fully-integrated memristor chip enables efficient object …

Abstract In the'Beyond Moore's Law'era, with increasing edge intelligence, domain-specific
computing embracing unconventional approaches will become increasingly prevalent. At …

Conductive atomic force microscopy (c-AFM) can provide simultaneous maps of the
topography and electrical current flow through materials with high spatial resolution and it is …

Cation-based resistance switches have been considered as promising candidates for
memory cells and other novel devices. So far, the most accepted switching processes of …

The utilization of memristive analog-digital mixed in-memory computing has significantly
tackled the issues of massive computing resources and time delays in solving combinatorial …

A key challenge in developing new memories for analog in memory computing is being able
to rapidly characterize statistics across a large number of analog devices. In this paper, we …

Neurons and other excitable systems can release energy suddenly given a small stimulus.
Excitability has recently drawn increasing interest in optics, as it is key to realize all-optical …

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single
synaptic memristive device holds the most potential for realizing next-generation …

Fading memory is the capability of a physical system to approach a unique asymptotic
behaviour, irrespective of the initial conditions, when stimulated by an input from a certain …

Neuromorphic computing has emerged as an alternative computing paradigm to address
the increasing computing needs for data-intensive applications. In this context, resistive …