Porous crystalline materials for memories and neuromorphic computing systems

G Ding, JY Zhao, K Zhou, Q Zheng, ST Han… - Chemical Society …, 2023 - pubs.rsc.org
Porous crystalline materials usually include metal–organic frameworks (MOFs), covalent
organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites …

Memristors based on 2D materials as an artificial synapse for neuromorphic electronics

W Huh, D Lee, CH Lee - Advanced materials, 2020 - Wiley Online Library
The memristor, a composite word of memory and resistor, has become one of the most
important electronic components for brain‐inspired neuromorphic computing in recent years …

Scaling deep learning for materials discovery

A Merchant, S Batzner, SS Schoenholz, M Aykol… - Nature, 2023 - nature.com
Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …

Resistive switching materials for information processing

Z Wang, H Wu, GW Burr, CS Hwang, KL Wang… - Nature Reviews …, 2020 - nature.com
The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …

A comprehensive review on emerging artificial neuromorphic devices

J Zhu, T Zhang, Y Yang, R Huang - Applied Physics Reviews, 2020 - pubs.aip.org
The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

Y Zhang, GQ Mao, X Zhao, Y Li, M Zhang, Z Wu… - Nature …, 2021 - nature.com
The resistive switching effect in memristors typically stems from the formation and rupture of
localized conductive filament paths, and HfO2 has been accepted as one of the most …

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

C Li, D Belkin, Y Li, P Yan, M Hu, N Ge, H Jiang… - Nature …, 2018 - nature.com
Memristors with tunable resistance states are emerging building blocks of artificial neural
networks. However, in situ learning on a large-scale multiple-layer memristor network has …

Open-loop analog programmable electrochemical memory array

P Chen, F Liu, P Lin, P Li, Y Xiao, B Zhang… - Nature …, 2023 - nature.com
Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …

Memristor modeling: challenges in theories, simulations, and device variability

L Gao, Q Ren, J Sun, ST Han, Y Zhou - Journal of Materials Chemistry …, 2021 - pubs.rsc.org
This article presents a review of the current development and challenges in memristor
modeling. We review the mechanisms of memristive devices based on various …

Power-efficient neural network with artificial dendrites

X Li, J Tang, Q Zhang, B Gao, JJ Yang, S Song… - Nature …, 2020 - nature.com
In the nervous system, dendrites, branches of neurons that transmit signals between
synapses and soma, play a critical role in processing functions, such as nonlinear …