A caloritronics-based Mott neuristor

Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. CMOS-based circuits have been used for this purpose, but they are non-scalable, limiting the device density and motivating the search for neuromorphic materials. 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 VO2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. By using local temperature as the internal variable, we avoid the need of external capacitors, which reduces neuristor size by several orders of magnitude. 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. More generally, we show that heat dissipation is not always an undesirable effect: it can perform computing tasks if properly engineered

Voltage-controlled magnetism enabled by resistive switching

The discovery of new mechanisms of controlling magnetic properties by electric fields or currents furthers the fundamental understanding of magnetism and has important implications for practical use. Here, we present a novel approach of utilizing resistive switching to control magnetic anisotropy. We study a ferromagnetic oxide that exhibits an electrically triggered metal-to-insulator phase transition producing a volatile resistive switching. This switching occurs in a characteristic spatial pattern: the formation of a transverse insulating barrier inside a metallic matrix resulting in an unusual ferromagnetic/paramagnetic/ferromagnetic configuration. We found that the formation of this voltage-driven paramagnetic insulating barrier is accompanied by the emergence of a strong uniaxial magnetic anisotropy that overpowers the intrinsic material anisotropy. Our results demonstrate that resistive switching is an effective tool for manipulating magnetic properties. Because resistive switching can be induced in a very broad range of materials, our findings could enable a new class of voltage-controlled magnetism systems

Magnetoresistance anomaly during the electrical triggering of a metal-insulator transition

Phase separation naturally occurs in a variety of magnetic materials and it often has a major impact on both electric and magnetotransport properties. In resistive switching systems, phase separation can be created on demand by inducing local switching, which provides an opportunity to tune the electronic and magnetic state of the device by applying voltage. Here we explore the magnetotransport properties in the ferromagnetic oxide (La,Sr)MnO3 (LSMO) during the electrical triggering of an intrinsic metal-insulator transition (MIT) that produces volatile resistive switching. This switching occurs in a characteristic spatial pattern, i.e., the formation of an insulating barrier perpendicular to the current flow, enabling an electrically actuated ferromagnetic-paramagnetic-ferromagnetic phase separation. At the threshold voltage of the MIT triggering, both anisotropic and colossal magnetoresistances exhibit anomalies including a large increase in magnitude and a sign flip. Computational analysis revealed that these anomalies originate from the coupling between the switching-induced phase separation state and the intrinsic magnetoresistance of LSMO. This work demonstrates that driving the MIT material into an out-of-equilibrium resistive switching state provides the means to electrically control of the magnetotransport phenomena

Non-thermal resistive switching in Mott insulator nanowires

Resistive switching can be achieved in a Mott insulator by applying current/voltage, which triggers an insulator-metal transition (IMT). This phenomenon is key for understanding IMT physics and developing novel memory elements and brain-inspired technology. Despite this, the roles of electric field and Joule heating in the switching process remain controversial. Using nanowires of two archetypal Mott insulators—VO2 and V2O3 we unequivocally show that a purely non-thermal electrical IMT can occur in both materials. The mechanism behind this effect is identified as field-assisted carrier generation leading to a doping driven IMT. This effect can be controlled by similar means in both VO2 and V2O3, suggesting that the proposed mechanism is generally applicable to Mott insulators. The energy consumption associated with the non-thermal IMT is extremely low, rivaling that of state-of-the-art electronics and biological neurons. These findings pave the way towards highly energy-efficient applications of Mott insulators.Fil: Kalcheim, Yoav. University of California at San Diego; Estados UnidosFil: Camjayi, Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: del Valle, Javier. University of California at San Diego; Estados UnidosFil: Salev, Pavel. University of California at San Diego; Estados UnidosFil: Rozenberg, Marcelo. Université Paris Sud; FranciaFil: Schuller, Ivan K.. University of California at San Diego; Estados Unido

A caloritronics-based Mott neuristor.

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 VO2 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