Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning

Yijun Li, Jianshi Tang (), Bin Gao, Jian Yao, Anjunyi Fan, Bonan Yan, Yuchao Yang, Yue Xi, Yuankun Li, Jiaming Li, Wen Sun, Yiwei Du, Zhengwu Liu, Qingtian Zhang, Song Qiu, Qingwen Li, He Qian and Huaqiang Wu ()
Additional contact information
Yijun Li: Tsinghua University
Jianshi Tang: Tsinghua University
Bin Gao: Tsinghua University
Jian Yao: Chinese Academy of Science
Anjunyi Fan: Peking University
Bonan Yan: Peking University
Yuchao Yang: Peking University
Yue Xi: Tsinghua University
Yuankun Li: Tsinghua University
Jiaming Li: Tsinghua University
Wen Sun: Tsinghua University
Yiwei Du: Tsinghua University
Zhengwu Liu: Tsinghua University
Qingtian Zhang: Tsinghua University
Song Qiu: Chinese Academy of Science
Qingwen Li: Chinese Academy of Science
He Qian: Tsinghua University
Huaqiang Wu: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42981-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42981-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42981-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().