A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing

Han Xu, Dashan Shang (), Qing Luo, Junjie An, Yue Li, Shuyu Wu, Zhihong Yao, Woyu Zhang, Xiaoxin Xu, Chunmeng Dou, Hao Jiang, Liyang Pan, Xumeng Zhang, Ming Wang, Zhongrui Wang, Jianshi Tang (), Qi Liu () and Ming Liu
Additional contact information
Han Xu: Chinese Academy of Sciences
Dashan Shang: Chinese Academy of Sciences
Qing Luo: Chinese Academy of Sciences
Junjie An: Chinese Academy of Sciences
Yue Li: Chinese Academy of Sciences
Shuyu Wu: Chinese Academy of Sciences
Zhihong Yao: Chinese Academy of Sciences
Woyu Zhang: Chinese Academy of Sciences
Xiaoxin Xu: Chinese Academy of Sciences
Chunmeng Dou: Chinese Academy of Sciences
Hao Jiang: Fudan University
Liyang Pan: Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University
Xumeng Zhang: Fudan University
Ming Wang: Fudan University
Zhongrui Wang: The University of Hong Kong
Jianshi Tang: Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University
Qi Liu: Chinese Academy of Sciences
Ming Liu: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.

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

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42172-y 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_s41467-023-42172-y

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

DOI: 10.1038/s41467-023-42172-y

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 ().