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Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional inter...

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Autores principales: Wu, Chaoxing, Kim, Tae Whan, Choi, Hwan Young, Strukov, Dmitri B., Yang, J. Joshua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622032/
https://www.ncbi.nlm.nih.gov/pubmed/28963546
http://dx.doi.org/10.1038/s41467-017-00803-1
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author Wu, Chaoxing
Kim, Tae Whan
Choi, Hwan Young
Strukov, Dmitri B.
Yang, J. Joshua
author_facet Wu, Chaoxing
Kim, Tae Whan
Choi, Hwan Young
Strukov, Dmitri B.
Yang, J. Joshua
author_sort Wu, Chaoxing
collection PubMed
description If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks.
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spelling pubmed-56220322017-10-02 Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability Wu, Chaoxing Kim, Tae Whan Choi, Hwan Young Strukov, Dmitri B. Yang, J. Joshua Nat Commun Article If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks. Nature Publishing Group UK 2017-09-29 /pmc/articles/PMC5622032/ /pubmed/28963546 http://dx.doi.org/10.1038/s41467-017-00803-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Wu, Chaoxing
Kim, Tae Whan
Choi, Hwan Young
Strukov, Dmitri B.
Yang, J. Joshua
Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title_full Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title_fullStr Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title_full_unstemmed Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title_short Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
title_sort flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622032/
https://www.ncbi.nlm.nih.gov/pubmed/28963546
http://dx.doi.org/10.1038/s41467-017-00803-1
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