Spiking neurons from tunable Gaussian heterojunction transistors
DC Field | Value | Language |
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dc.contributor.author | Beck, Megan E. | - |
dc.contributor.author | Shylendra, Ahish | - |
dc.contributor.author | Sangwan, Vinod K. | - |
dc.contributor.author | Guo, Silu | - |
dc.contributor.author | Rojas, William A. Gaviria | - |
dc.contributor.author | Yoo, Hocheon | - |
dc.contributor.author | Bergeron, Hadallia | - |
dc.contributor.author | Su, Katherine | - |
dc.contributor.author | Trivedi, Amit R. | - |
dc.contributor.author | Hersam, Mark C. | - |
dc.date.available | 2020-10-20T06:42:44Z | - |
dc.date.created | 2020-06-10 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78502 | - |
dc.description.abstract | Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing. 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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.title | Spiking neurons from tunable Gaussian heterojunction transistors | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000522450900004 | - |
dc.identifier.doi | 10.1038/s41467-020-15378-7 | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.11, no.1 | - |
dc.description.isOpenAccess | N | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Yoo, Hocheon | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | CIRCUIT IMPLEMENTATION | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | SYNAPSE | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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