Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

Geometrically constrained magnetic domain walls (DWs) in magnetic nanowires can be manipulated at the nanometer scale. The inhomogeneous magnetic stray field generated by a DW can capture a magnetic nanoparticle in solution. On-chip nanomanipulation of individual magnetic beads coated with proteins is demonstrated through the motion of geometrically constrained DWs in specially designed magnetic nanoconduits fully integrated in a lab-on-a-chip platform

Magnetoelectric effects in Fe/BaTiO3(001) heterostructures have been investigated via magneto-optical Kerr effect as a function of temperature. We find major modifications of magnetic anisotropies and magnetic coercivity induced by the application of electric fields perpendicular to the interface. Changes in the coercive magnetic field, on the order of 100% at 250 K (in the orthorhombic phase of BaTiO3) and 40% at 300 K (in the tetragonal phase), which are linked to the ferroelectric behavior of BaTiO3, are reported.

The multiple resistive switching of Pt/HfO2/TiN devices is demonstrated as a result of a competition between the switching at opposite metal/oxide interfaces. Three switching operation modes are demonstrated: clockwise (CW) switching (set for negative voltage and reset for positive voltage at Pt electrode), as already reported in literature for similar material stacks; counterclockwise (CCW) switching and complementary switching (CS) that consist in a set and a reset for increasing voltage of the same polarity. The multiple switching operation modes are enabled by a deep-reset operation that brings the cell resistance close to the initial one. As a consequence, the set transition after a deep-reset occurs at the same voltage and currents as those of the forming and leads to a low resistance state whose resistance can be further decreased in a CCW switching or increased back with a CW switching. With a suitable choice of the stop voltage, a CS in obtained, as well. The coexistence of all three CW, CCW, and CS operations demonstrates that both metal-oxide interfaces are active in the formation and the dissolution of conductive filaments responsible for the switching. All these observations are discussed in terms of a competition between ion migration processes occurring at the opposite metal-oxide interfaces.