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

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

Memory cells have always been an important element of information technology. With
emerging technologies like big data and cloud computing, the scale and complexity of data …

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological
synaptic plasticity and learning are described. The material properties and electrical …

In this review, a comprehensive survey of different oxide-based resistive random-access
memories (RRAMs) for neuromorphic computing is provided. We begin with the history of …

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 …

We propose TiO x-based resistive switching device for neuromorphic synapse applications.
This device is capable of 64-levels conductance states because of their optimized interface …

This paper presents aligned carbon nanotube (CNT) synaptic transistors for large-scale
neuromorphic computing systems. The synaptic behavior of these devices is achieved via …

The applied potential, time, and water content are crucial factors in the electrochemical
anodization process because the growth of one-dimensional nanotubes can be accelerated …

Neural network technologies have taken center stage owing to their powerful computing
capability for supporting deep learning in artificial intelligence. However, conventional …