40 research outputs found
Growth-Induced In-Plane Uniaxial Anisotropy in VO/Ni Films
We report on a strain-induced and temperature dependent uniaxial anisotropy
in VO/Ni hybrid thin films, manifested through the interfacial
strain and sample microstructure, and its consequences on the angular dependent
magnetization reversal. X-ray diffraction and reciprocal space maps identify
the in-plane crystalline axes of the VO; atomic force and scanning
electron microscopy reveal oriented rips in the film microstructure.
Quasi-static magnetometry and dynamic ferromagnetic resonance measurements
identify a uniaxial magnetic easy axis along the rips. Comparison with films
grown on sapphire without rips shows a combined contribution from strain and
microstructure in the VO/Ni films. Magnetization reversal
characteristics captured by angular-dependent first order reversal curve
measurements indicate a strong domain wall pinning along the direction
orthogonal to the rips, inducing an angular-dependent change in the reversal
mechanism. The resultant anisotropy is tunable with temperature and is most
pronounced at room temperature, which is beneficial for potential device
applications
Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators
Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent
works show that the non-linearity of their oscillation amplitude can be
leveraged to achieve waveform classification for an input signal encoded in the
amplitude of the input voltage. Here we show that the frequency and the phase
of the oscillator can also be used to recognize waveforms. For this purpose, we
phase-lock the oscillator to the input waveform, which carries information in
its modulated frequency. In this way we considerably decrease amplitude, phase
and frequency noise. We show that this method allows classifying sine and
square waveforms with an accuracy above 99% when decoding the output from the
oscillator amplitude, phase or frequency. We find that recognition rates are
directly related to the noise and non-linearity of each variable. These results
prove that spin-torque nano-oscillators offer an interesting platform to
implement different computing schemes leveraging their rich dynamical features
RF signal classification in hardware with an RF spintronic neural network
Extracting information from radiofrequency (RF) signals using artificial
neural networks at low energy cost is a critical need for a wide range of
applications. Here we show how to leverage the intrinsic dynamics of spintronic
nanodevices called magnetic tunnel junctions to process multiple analogue RF
inputs in parallel and perform synaptic operations. Furthermore, we achieve
classification of RF signals with experimental data from magnetic tunnel
junctions as neurons and synapses, with the same accuracy as an equivalent
software neural network. These results are a key step for embedded
radiofrequency artificial intelligence.Comment: 8 pages, 5 figure
Multilayer spintronic neural networks with radio-frequency connections
Spintronic nano-synapses and nano-neurons perform complex cognitive
computations with high accuracy thanks to their rich, reproducible and
controllable magnetization dynamics. These dynamical nanodevices could
transform artificial intelligence hardware, provided that they implement
state-of-the art deep neural networks. However, there is today no scalable way
to connect them in multilayers. Here we show that the flagship nano-components
of spintronics, magnetic tunnel junctions, can be connected into multilayer
neural networks where they implement both synapses and neurons thanks to their
magnetization dynamics, and communicate by processing, transmitting and
receiving radio frequency (RF) signals. We build a hardware spintronic neural
network composed of nine magnetic tunnel junctions connected in two layers, and
show that it natively classifies nonlinearly-separable RF inputs with an
accuracy of 97.7%. Using physical simulations, we demonstrate that a large
network of nanoscale junctions can achieve state-of the-art identification of
drones from their RF transmissions, without digitization, and consuming only a
few milliwatts, which is a gain of more than four orders of magnitude in power
consumption compared to currently used techniques. This study lays the
foundation for deep, dynamical, spintronic neural networks
GEHEP 010 study: Prevalence and distribution of hepatitis B virus genotypes in Spain (2000â2016)
[Objective] To study the prevalence and distribution of HBV genotypes in Spain for the period 2000â2016.[Methods] Retrospective study recruiting 2559 patients from 17 hospitals. Distribution of HBV genotypes, as well as sex, age, geographical origin, mode of transmission, HDV-, HIV- and/or HCV-coinfection, and treatment were recorded.[Results] 1924 chronically HBV native Spanish patients have been recruited. Median age was 54 years (IQR: 41â62), 69.6% male, 6.3% HIV-coinfected, 3.1% were HCV-coinfected, 1.7% HDV-co/superinfected. Genotype distribution was: 55.9% D, 33.5% A, 5.6% F, 0.8% G, and 1.9% other genotypes (E, B, H and C). HBV genotype A was closely associated with male sex, sexual transmission, and HIV-coinfection. In contrast, HBV genotype D was associated with female sex and vertical transmission. Different patterns of genotype distribution and diversity were found between different geographical regions. In addition, HBV epidemiological patterns are evolving in Spain, mainly because of immigration. Finally, similar overall rates of treatment success across all HBV genotypes were found.[Conclusions] We present here the most recent data on molecular epidemiology of HBV in Spain (GEHEP010 Study). This study confirms that the HBV genotype distribution in Spain varies based on age, sex, origin, HIV-coinfection, geographical regions and epidemiological groups.This study has been funded in part by the funds of the research project GEHEP-2018-010, granted by the Hepatitis Group of the Spanish Society of Infectious Diseases and Clinical Microbiology (Grupo de Hepatitis de la Sociedad Española de Enfermedades Infecciosas y MicrobiologĂa ClĂnica, GEHEP/SEIMC)