Abstract

Embedded memory, which heavily relies on the manufacturing process, has been widely adopted in various industrial applications. As the field of embedded memory continues to evolve, innovative strategies are emerging to enhance performance. Among them, resistive random access memory (RRAM) has gained significant attention due to its numerous advantages over traditional memory devices, including high speed (<1 ns), high density (4 F2·n−1), high scalability (∼nm), and low power consumption (∼pJ). This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potential applications. It provides a brief introduction to the concepts and advantages of RRAM, discusses the key factors that impact its industrial manufacturing, and presents the commercial progress driven by cutting-edge nanotechnology, which has been pursued by many semiconductor giants. Additionally, it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing, with a particular emphasis on its role in neuromorphic computing. Finally, the review discusses the current challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Details

Title
Advances of embedded resistive random access memory in industrial manufacturing and its potential applications
Author
Wang, Zijian 1   VIAFID ORCID Logo  ; Song, Yixian 1 ; Zhang, Guobin 1 ; Luo, Qi 1 ; Xu, Kai 1 ; Gao, Dawei 1 ; Yu, Bin 1 ; Loke, Desmond 2 ; Zhong, Shuai 3 ; Zhang, Yishu 1 

 College of Integrated Circuits, Zhejiang University , Hangzhou, Zhejiang 3112000, People’s Republic of China; ZJU-Hangzhou Global Scientific and Technological Innovation Center , Hangzhou 310027, People’s Republic of China 
 Department of Science, Mathematics and Technology, Singapore University of Technology and Design , Singapore 487372, Singapore 
 Guangdong Institute of Intelligence Science and Technology , Hengqin, Zhuhai 519031, People’s Republic of China 
First page
032006
Publication year
2024
Publication date
Jun 2024
Publisher
IOP Publishing
e-ISSN
26317990
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2973440275
Copyright
© 2024 The Author(s). Published by IOP Publishing Ltd on behalf of the IMMT. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.