Memristive technologies for data storage, computation, encryption, and radio-frequency communication

M Lanza, A Sebastian, WD Lu, M Le Gallo, MF Chang… - Science, 2022 - science.org
Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

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 …

Hybrid 2D–CMOS microchips for memristive applications

K Zhu, S Pazos, F Aguirre, Y Shen, Y Yuan, W Zheng… - Nature, 2023 - nature.com
Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …

A crossbar array of magnetoresistive memory devices for in-memory computing

S Jung, H Lee, S Myung, H Kim, SK Yoon, SW Kwon… - Nature, 2022 - nature.com
Implementations of artificial neural networks that borrow analogue techniques could
potentially offer low-power alternatives to fully digital approaches,–. One notable example is …

Ai-generated content (aigc): A survey

J Wu, W Gan, Z Chen, S Wan, H Lin - arXiv preprint arXiv:2304.06632, 2023 - arxiv.org
To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …

In‐sensor computing: materials, devices, and integration technologies

T Wan, B Shao, S Ma, Y Zhou, Q Li… - Advanced materials, 2023 - Wiley Online Library
The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large
volume of data generated at sensory terminals. Frequent data transfer between the sensors …

Memory devices and applications for in-memory computing

A Sebastian, M Le Gallo, R Khaddam-Aljameh… - Nature …, 2020 - nature.com
Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Parallel convolutional processing using an integrated photonic tensor core

J Feldmann, N Youngblood, M Karpov, H Gehring, X Li… - Nature, 2021 - nature.com
With the proliferation of ultrahigh-speed mobile networks and internet-connected devices,
along with the rise of artificial intelligence (AI), the world is generating exponentially …

Logic-in-memory based on an atomically thin semiconductor

G Migliato Marega, Y Zhao, A Avsar, Z Wang… - Nature, 2020 - nature.com
The growing importance of applications based on machine learning is driving the need to
develop dedicated, energy-efficient electronic hardware. Compared with von Neumann …

Towards spike-based machine intelligence with neuromorphic computing

K Roy, A Jaiswal, P Panda - Nature, 2019 - nature.com
Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …