A caloritronics-based Mott neuristor

del Valle, Javier; Salev, Pavel; Kalcheim, Yoav; Schuller, Ivan K.

doi:10.1038/s41598-020-61176-y

Title: A caloritronics-based Mott neuristor

Journal Article · 08 March 2020 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-020-61176-y · OSTI ID:1619806

del Valle, Javier; Salev, Pavel; Kalcheim, Yoav; Schuller, Ivan K.

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; SC0019273

OSTI ID:: 1619806

Alternate ID(s):: OSTI ID: 1629873

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 10; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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