Growth-Induced In-Plane Uniaxial Anisotropy in V2_{2}O3_{3}/Ni Films

We report on a strain-induced and temperature dependent uniaxial anisotropy in V$_{2}$O$_{3}$/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 V$_{2}$O$_{3}$; 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 V$_{2}$O$_{3}$/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 (2000–2016)

[Objective] To study the prevalence and distribution of HBV genotypes in Spain for the period 2000–2016.[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: 41–62), 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 Microbiología Clínica, GEHEP/SEIMC)