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

TitleA low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing
Authors
Issue Date11-Oct-2023
PublisherNature Research
Citation
Nature Communications, 2023, v. 14, n. 1 How to Cite?
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 similar to 3.16 fW and a biology-comparable read energy of similar to 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.


Persistent Identifierhttp://hdl.handle.net/10722/339379
ISSN
2023 Impact Factor: 14.7
2023 SCImago Journal Rankings: 4.887
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, Han-
dc.contributor.authorShang, Dashan-
dc.contributor.authorLuo, Qing-
dc.contributor.authorAn, Junjie-
dc.contributor.authorLi, Yue-
dc.contributor.authorWu, Shuyu-
dc.contributor.authorYao, Zhihong-
dc.contributor.authorZhang, Woyu-
dc.contributor.authorXu, Xiaoxin-
dc.contributor.authorDou, Chunmeng-
dc.contributor.authorJiang, Hao-
dc.contributor.authorPan, Liyang-
dc.contributor.authorZhang, Xumeng-
dc.contributor.authorWang, Ming-
dc.contributor.authorWang, Zhongrui-
dc.contributor.authorTang, Jianshi-
dc.contributor.authorLiu, Qi-
dc.contributor.authorLiu, Ming -
dc.date.accessioned2024-03-11T10:36:07Z-
dc.date.available2024-03-11T10:36:07Z-
dc.date.issued2023-10-11-
dc.identifier.citationNature Communications, 2023, v. 14, n. 1-
dc.identifier.issn2041-1723-
dc.identifier.urihttp://hdl.handle.net/10722/339379-
dc.description.abstract<p>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 similar to 3.16 fW and a biology-comparable read energy of similar to 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.<br></p>-
dc.languageeng-
dc.publisherNature Research-
dc.relation.ispartofNature Communications-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleA low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41467-023-42172-y-
dc.identifier.scopuseid_2-s2.0-85173839493-
dc.identifier.volume14-
dc.identifier.issue1-
dc.identifier.eissn2041-1723-
dc.identifier.isiWOS:001099083700026-
dc.identifier.issnl2041-1723-

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