Dynamical memristors for higher-complexity neuromorphic computing

S Kumar, X Wang, JP Strachan, Y Yang… - Nature Reviews …, 2022 - nature.com
Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …

Recent advances and future prospects for memristive materials, devices, and systems

MK Song, JH Kang, X Zhang, W Ji, A Ascoli… - ACS …, 2023 - ACS Publications
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware

N Rathi, I Chakraborty, A Kosta, A Sengupta… - ACM Computing …, 2023 - dl.acm.org
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Volatile and nonvolatile memristive devices for neuromorphic computing

G Zhou, Z Wang, B Sun, F Zhou, L Sun… - Advanced Electronic …, 2022 - Wiley Online Library
Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …

Nanostructured perovskites for nonvolatile memory devices

Q Liu, S Gao, L Xu, W Yue, C Zhang, H Kan… - Chemical Society …, 2022 - pubs.rsc.org
Perovskite materials have driven tremendous advances in constructing electronic devices
owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties …

2D material based synaptic devices for neuromorphic computing

G Cao, P Meng, J Chen, H Liu, R Bian… - Advanced Functional …, 2021 - Wiley Online Library
The demand for computing power has been increasing exponentially since the emergence
of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel …

Neuromorphic engineering: from biological to spike‐based hardware nervous systems

JQ Yang, R Wang, Y Ren, JY Mao, ZP Wang… - Advanced …, 2020 - Wiley Online Library
The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …

Synaptic devices based neuromorphic computing applications in artificial intelligence

B Sun, T Guo, G Zhou, S Ranjan, Y Jiao, L Wei… - Materials Today …, 2021 - Elsevier
Synaptic devices, including synaptic memristor and synaptic transistor, are emerging
nanoelectronic devices, which are expected to subvert traditional data storage and …

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 …

Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries

Y Li, L Loh, S Li, L Chen, B Li, M Bosman, KW Ang - Nature Electronics, 2021 - nature.com
The implementation of memristive synapses in neuromorphic computing is hindered by the
limited reproducibility and high energy consumption of the switching behaviour of the …