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Title: A caloritronics-based Mott neuristor

Abstract

Abstract Machine learning imitates the basic features of biological neural networks at a software level. A strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO 2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks.

Authors:
; ; ;
Publication Date:
Research Org.:
Univ. of California, San Diego, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1619806
Alternate Identifier(s):
OSTI ID: 1629873
Grant/Contract Number:  
SC0019273
Resource Type:
Published Article
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Name: Scientific Reports Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 97 MATHEMATICS AND COMPUTING

Citation Formats

del Valle, Javier, Salev, Pavel, Kalcheim, Yoav, and Schuller, Ivan K. A caloritronics-based Mott neuristor. United Kingdom: N. p., 2020. Web. https://doi.org/10.1038/s41598-020-61176-y.
del Valle, Javier, Salev, Pavel, Kalcheim, Yoav, & Schuller, Ivan K. A caloritronics-based Mott neuristor. United Kingdom. https://doi.org/10.1038/s41598-020-61176-y
del Valle, Javier, Salev, Pavel, Kalcheim, Yoav, and Schuller, Ivan K. Mon . "A caloritronics-based Mott neuristor". United Kingdom. https://doi.org/10.1038/s41598-020-61176-y.
@article{osti_1619806,
title = {A caloritronics-based Mott neuristor},
author = {del Valle, Javier and Salev, Pavel and Kalcheim, Yoav and Schuller, Ivan K.},
abstractNote = {Abstract Machine learning imitates the basic features of biological neural networks at a software level. A strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO 2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks.},
doi = {10.1038/s41598-020-61176-y},
journal = {Scientific Reports},
number = 1,
volume = 10,
place = {United Kingdom},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41598-020-61176-y

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