Spiking neurons from tunable Gaussian heterojunction transistors

Designing high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2 to realize simplified spiking neuron circuits.

Bibliographic Details
Main Authors:	Megan E. Beck, Ahish Shylendra, Vinod K. Sangwan, Silu Guo, William A. Gaviria Rojas, Hocheon Yoo, Hadallia Bergeron, Katherine Su, Amit R. Trivedi, Mark C. Hersam
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-03-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-15378-7

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spelling	doaj-4bbd6d5c2b1f4d8f83394ac1c32b13192021-05-11T08:52:56ZengNature Publishing GroupNature Communications2041-17232020-03-011111810.1038/s41467-020-15378-7Spiking neurons from tunable Gaussian heterojunction transistorsMegan E. Beck0Ahish Shylendra1Vinod K. Sangwan2Silu Guo3William A. Gaviria Rojas4Hocheon Yoo5Hadallia Bergeron6Katherine Su7Amit R. Trivedi8Mark C. Hersam9Department of Materials Science and Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, University of IllinoisDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, University of IllinoisDepartment of Materials Science and Engineering, Northwestern UniversityDesigning high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2 to realize simplified spiking neuron circuits.https://doi.org/10.1038/s41467-020-15378-7
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Megan E. Beck Ahish Shylendra Vinod K. Sangwan Silu Guo William A. Gaviria Rojas Hocheon Yoo Hadallia Bergeron Katherine Su Amit R. Trivedi Mark C. Hersam
spellingShingle	Megan E. Beck Ahish Shylendra Vinod K. Sangwan Silu Guo William A. Gaviria Rojas Hocheon Yoo Hadallia Bergeron Katherine Su Amit R. Trivedi Mark C. Hersam Spiking neurons from tunable Gaussian heterojunction transistors Nature Communications
author_facet	Megan E. Beck Ahish Shylendra Vinod K. Sangwan Silu Guo William A. Gaviria Rojas Hocheon Yoo Hadallia Bergeron Katherine Su Amit R. Trivedi Mark C. Hersam
author_sort	Megan E. Beck
title	Spiking neurons from tunable Gaussian heterojunction transistors
title_short	Spiking neurons from tunable Gaussian heterojunction transistors
title_full	Spiking neurons from tunable Gaussian heterojunction transistors
title_fullStr	Spiking neurons from tunable Gaussian heterojunction transistors
title_full_unstemmed	Spiking neurons from tunable Gaussian heterojunction transistors
title_sort	spiking neurons from tunable gaussian heterojunction transistors
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-03-01
description	Designing high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2 to realize simplified spiking neuron circuits.
url	https://doi.org/10.1038/s41467-020-15378-7
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Spiking neurons from tunable Gaussian heterojunction transistors

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