In this paper, emerging memory devices are investigated for a promising synaptic device of
neuromorphic computing. Because the neuromorphic computing hardware requires high …

Phase-change memory (PCM) is a promising candidate for data storage in flexible
electronics, but its high switching current and power are often drawbacks. In this study, we …

Historically, memory technologies have been evaluated based on their storage density, cost,
and latencies. Beyond these metrics, the need to enable smarter and intelligent computing …

In this paper, the use of HfO 2-based oxide-based resistive memory (OxRAM) devices
operated in binary mode to implement synapses in a convolutional neural network (CNN) is …

With the advent of low-power neuromorphic computing systems, new possibilities have
emerged for deployment in various sectors, like healthcare and transport, that require …

Superlattice (SL) phase change materials have shown promise to reduce the switching
current and resistance drift of phase change memory (PCM). However, the effects of internal …

Cognitive tasks are essential for the modern applications of electronics, and rely on the
capability to perform inference. The Von Neumann bottleneck is an important issue for such …

Neural networks trained by backpropagation have achieved tremendous successes on
numerous intelligent tasks. However, naïve gradient-based training and updating methods …

The inherent structural flexibility and reversibility of non-covalent organic frameworks have
enabled them to exhibit switchable multistate structures under external stimuli, providing …

Large switching current density and resistance drift remain challenges for phase change
memory (PCM) in data storage and neuromorphic computing applications. Here, we address …