A caloritronics-based Mott neuristor

• Published in: Scientific reports Vol. 10; no. 1; p. 4292
• Main Authors: del Valle, Javier, Salev, Pavel, Kalcheim, Yoav, Schuller, Ivan K.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.03.2020; Nature Publishing Group
• Subjects: 142/126; 639/301/119/2795; 639/301/119/995; 639/301/357/995; 639/766/1130/2798; 639/766/119/995; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Heat transfer; Humanities and Social Sciences; Learning algorithms; Machine learning; MATHEMATICS AND COMPUTING; multidisciplinary; Neural coding; Neural networks; Science; Science (multidisciplinary); Synapses
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-61176-y

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.