31 research outputs found
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
Tööedukust mõjustavaid tegureid metsatöötamisel = Die Arbeitsleistung beeinflussende Faktoren bei der Waldaufarbeitung
Digiteeritud Euroopa Regionaalarengu Fondi rahastusel, projekti "Eesti teadus- ja õppekirjandus" (2014-2020.12.03.21-0848) raames.https://www.ester.ee/record=b2317526*es
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
Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
The capabilities of image probe experiments are rapidly expanding, providing
new information about quantum materials on unprecedented length and time
scales. Many such materials feature inhomogeneous electronic properties with
intricate pattern formation on the observable surface. This rich spatial
structure contains information about interactions, dimensionality, and disorder
-- a spatial encoding of the Hamiltonian driving the pattern formation. Image
recognition techniques from machine learning are an excellent tool for
interpreting information encoded in the spatial relationships in such images.
Here, we develop a deep learning framework for using the rich information
available in these spatial correlations in order to discover the underlying
Hamiltonian driving the patterns. We first vet the method on a known case,
scanning near-field optical microscopy on a thin film of VO2. We then apply our
trained convolutional neural network architecture to new optical microscope
images of a different VO2 film as it goes through the metal-insulator
transition. We find that a two-dimensional Hamiltonian with both interactions
and random field disorder is required to explain the intricate, fractal
intertwining of metal and insulator domains during the transition. This
detailed knowledge about the underlying Hamiltonian paves the way to using the
model to control the pattern formation via, e.g., tailored hysteresis
protocols. We also introduce a distribution-based confidence measure on the
results of a multi-label classifier, which does not rely on adversarial
training. In addition, we propose a new machine learning based criterion for
diagnosing a physical system's proximity to criticality