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

Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …

This tutorial describes challenges and possible avenues for the implementation of the
components of a solid-state system, which emulates a biological brain. The tutorial is …

In this Review, memristors are examined from the frameworks of both von Neumann and
neuromorphic computing architectures. For the former, a new logic computational process …

In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …

The implementation of biocompatible and biodegradable information storage would be a
significant step toward next-generation green electronics. On the other hand, benefiting from …

Semiconductor memories are essential ingredients of modern electronic devices. Resistive
Random-Access Memories (RRAMs) have emerged as better alternatives for conventional …

Memristors have been intensively studied in recent years as potential building blocks for the
construction of versatile neuromorphic architectures. The prevalent developments focus on …

Rapid advances in the semiconductor industry, driven largely by device scaling, are now
approaching fundamental physical limits and face severe power, performance, and cost …

Metaplasticity, a higher order of synaptic plasticity, as well as a key issue in neuroscience, is
realized with artificial synapses based on a WO3 thin film, and the activity-dependent …