Perpendicular magnetic anisotropy (PMA) is crucial in magnetic tunnel junctions (MTJs) for next-generation devices such as non-volatile memory devices–magnetic random-access-memory, spin–orbit-torque-based devices, and magnetic sensors. Precise tuning of PMA requires highly controllable modification methods compatible with existing electronic device fabrication technology. In this study, we report on the effects of low-energy (30 keV) Ar and Ne ion irradiation on the PMA of cobalt-iron-boron alloy (CoFeB) based thin films for fluences between 1 × 1013 ions·cm−2 and 1 × 1015 ions·cm−2 and resultant displacement per atom (DPA) of 0.13–13 and 0.32–32 for Ne and Ar ions, respectively. Our results showed that ion irradiation reduced the PMA in CoFeB layer. The effective anisotropy energy, Keff ≈ 64 kJ·m−3 for un-irradiated CoFeB stacks reduced to ≈ 50 kJ·m−3 and ≈ 30 kJ·m−3 for stacks irradiated with 1 × 1013 Ne·cm−2 and with 1 × 1013 Ar·cm−2, respectively. Monte Carlo-based simulations for ion irradiation showed significant intermixing of interface layers upon irradiation leading to decrease in Fe–O and Fe/Co–W orbital hybridization, which reduced the PMA. Furthermore, intermixing of Fe/Co with W resulted in the formation of paramagnetic phases, which decreased the saturation magnetization. Our study shows that ion irradiation is an effective method for tuning PMA in MTJs, and the extent of change in PMA can be directly correlated with the DPA.

Magnetic sensors are key elements in many industrial, security, military, and biomedical applications. Heusler alloys are promising materials for magnetic sensor applications due to their high spin polarization and tunable magnetic properties. The dynamic field range of magnetic sensors is strongly related to the perpendicular magnetic anisotropy (PMA). By tuning the PMA, it is possible to modify the sensing direction, sensitivity and even the accuracy of the magnetic sensors. Here, we report the tuning of PMA in a Co2MnGa Heusler alloy film via argon (Ar) ion irradiation. MgO/Co2MnGa/Pd films with an initial PMA were irradiated with 30 keV 40Ar+ ions with fluences (ions·cm−2) between 1 × 1013 and 1 × 1015 Ar·cm−2, which corresponds to displacement per atom values between 0.17 and 17, estimated from Monte-Carlo-based simulations. The magneto optical and magnetization results showed that the effective anisotropy energy (Keff) decreased from ~153 kJ·m−3 for the un-irradiated film to ~14 kJ·m−3 for the 1 × 1014 Ar·cm−2 irradiated film. The reduced Keff and PMA are attributed to ion-irradiation-induced interface intermixing that decreased the interfacial anisotropy. These results demonstrate that ion irradiation is a promising technique for shaping the PMA of Co2MnGa Heusler alloy for magnetic sensor applications.

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

Self-organised nanoscale networks are currently under investigation because of their potential to be used as novel neuromorphic computing systems. In these systems, electrical input and output signals will necessarily couple to the recurrent electrical signals within the network that provide brain-like functionality. This raises important questions as to whether practical electrode configurations and network geometries might influence the brain-like dynamics. We use the concept of criticality (which is itself a key charactistic of brain-like processing) to quantify the neuromorphic potential of the devices, and find that in most cases criticality, and therefore optimal information processing capability, is maintained. In particular we find that devices with multiple electrodes remain critical despite the concentration of current near the electrodes. We find that broad network activity is maintained because current still flows through the entire network. We also develop a formalism to allow a detailed analysis of the number of dominant paths through the network. For rectangular systems we show that the number of pathways decreases as the system size increases, which consequently causes a reduction in network activity.

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

Manipulation of the heterointerfacial structure and/or chemistry of transition metal oxides is of great interest for the development of novel properties. However, few studies have focused on heterointerfacial effects on the growth characteristics of oxide thin films, although such interfacial engineering is crucial to determine the growth dynamics and physical properties of oxide heterostructures. Herein, we show that heterointerfacial effects play key roles in determining the growth process of oxide thin films by overcoming the simple epitaxial strain energy. Brownmillerite (SrFeO 2.5 ; BM-SFO) thin films are epitaxially grown along the b -axis on both SrTiO 3 (001) and SrRuO 3 /SrTiO 3 (001) substrates, whereas growth along the a -axis is expected from conventional epitaxial strain effects originating from lattice mismatch with the substrates. Scanning transmission electron microscopy measurements and first principles calculations reveal that these peculiar growth characteristics of BM-SFO thin films originate from the heterointerfacial effects governed by their distinct interfacial structures. These include octahedral connectivity between dissimilar oxides containing different chemical species and a peculiar transition layer for BM-SFO/SrRuO 3 /SrTiO 3 (001) and BM-SFO/SrTiO 3 (001) heterostructures, respectively. These effects enable subtle control of the growth process of oxide thin films and could facilitate the fabrication of novel functional devices.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation.

NUMERICAL SIMULATION OF PERCOLATING TUNNELLING NETWORK Numerical simulations of two dimensional percolating tunnelling networks can give valuable insight into 13 the structure in networks of nanoparticles, which are well described by continuum percolating tunnelling 14 models (Fostner, Brown, Carr, & Brown, 2014). In continuum percolation, the conducting objects are 15 uniform discs (representing the deposited particles in our experiments). The discs, shown schematically 16 in Figures 1 and 2, land randomly and are allowed to overlap with each other when they land 17 (representing coalescence between neighbouring particles and formation of groups) (Fostner et al., 2014; 18 Fostner & Brown, 2015). In this regime, with surface coverage p < pc, i.e. below the percolation 19 threshold, no single group spans the entire network and the conduction of the system is due to tunnel 20 currents flowing across small gaps which separate the groups of particles. Each gap is assigned a 21 conductance, Gi = A exp(−δLi), where A and δ are constants and Li is the size of the gap (A = 1 and 22 δ = 100 for convenience) (Fostner et al., 2014). Here it is assumed that the groups are large enough that 23 both the charging energy of a connected group and the quantization of energy levels are negligible, and 24 that the resistance between overlapping particles within a group is negligible, so that the only resistance 25 in the system is due to tunnelling between groups. We focus primarily on simulations with a system size 26 of 200×200 particle diameters (chosen to provide the best trade-off between computational time and 27

Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

We had discovered novel resistance switching phenomena in SrCoOx epitaxial thin films. We have interpreted the results in terms of the topotactic phase transformation between their insulating brownmillerite phase and the conducting perovskite phase and the existence of a rather vertical conducting filament due to its inherent layered structure. However, the rough interface observed between the SrCoOx and the Au top electrode (area ~10000 μm2) was assumed to result in the observed fluctuation in key switching parameters. In order to verify the effect of rough interface on the switching performance in the SrCoOx device, in this work, we studied the resistive switching properties of a SrCoOx device by placing a Au-coated tip (end area ~0.5 μm2) directly on the film surface as the top electrode. The resulting device displayed much improved endurance and showed high uniformity in key switching parameters as compared to the device having a large top electrode area. A simulation result confirmed that the Au-coated tip provides a local confinement of the electrical field, resulting in confinement of oxygen ion distribution and therefore localization of the conducting filament. By minimizing other free and uncontrollable parameters, the designed experiment here provides the most direct and isolated evidence that the rough interface between electrode and ReRAM matrix is detrimental for the reproducibility of resistivity switching phenomena.

The inherent power of the biological brain, with regard to pattern recognition, is unparalleled and cannot even be matched by multi-million dollar supercomputers. Inspired from this, neuromorphic computation, where ideas originating from the complex structure and functionality of the biological brain are utilized for advanced computation has shown great potential. In this regard, we are developing on-chip pattern classification capabilities via inexpensive self-assembly of nanoparticles (NPs). The formation of percolating microstructure of Sn NPs and tunnel junctions leads to a complex atomic-switch network (ASN) poised near criticality. Voltage stimulation is utilized for modulating the synaptic structure of the network, which shows potential for utilization as a 'reservoir' in reservoir computing (RC).

An epitaxial brownmillerite thin-film platform with tuned oxygen vacancy channels is used to realize high ReRAM performance.

Oxygen vacancies have a profound effect on the magnetic, electronic, and transport properties of transition metal oxide materials. Here, we studied the influence of oxygen vacancies on the magnetoresistance (MR) properties of SrRu1 − x Fe x O3 - δ epitaxial thin films (x = 0.10, 0.20, and 0.30). For this purpose, we synthesized highly strained epitaxial SrRu1 − x Fe x O3 − δ thin films with atomically flat surfaces containing different amounts of oxygen vacancies using pulsed laser deposition. Without an applied magnetic field, the films with x = 0.10 and 0.20 showed a metal–insulator transition, while the x = 0.30 thin film showed insulating behavior over the entire temperature range of 2–300 K. Both Fe doping and the concentration of oxygen vacancies had large effects on the negative MR contributions. For the low Fe doping case of x = 0.10, in which both films exhibited metallic behavior, MR was more prominent in the film with fewer oxygen vacancies or equivalently a more metallic film. For semiconducting films, higher MR was observed for more semiconducting films having more oxygen vacancies. A relatively large negative MR (~36.4%) was observed for the x = 0.30 thin film with a high concentration of oxygen vacancies (δ = 0.12). The obtained results were compared with MR studies for a polycrystal of (Sr1 − x La x )(Ru1 − x Fe x )O3. These results highlight the crucial role of oxygen stoichiometry in determining the magneto-transport properties in SrRu1 − x Fe x O3 − δ thin films.

In this work, the magnetotransport properties of epitaxial Sr1-xLaxRu1-xFexO3 (x = 0.05, 0.10, 0.20, and 0.30) thin films grown by pulsed laser deposition on SrTiO3 (001) substrates were investigated. Compared to doping Fe into the Ru4+ site of SrRuO3, doping LaFeO3 into SrRuO3 resulted in an increase in the zero-field resistivity. A larger zero-field resistivity value in magnetic perovskite oxide is, in many cases, favorable for obtaining high magnetoresistance. The films (0.0 <= x <= 0.10) showed metallic behavior and ferromagnetic ordering, although the resistivity increased and the ferromagnetic transition temperature T-C decreased with an increase in x. The thin film with x = 0.20 displayed a clear metal-toinsulator phase transition at low temperature and also displayed a well-defined resistivity minimum. This upturn in the resistivity curve is associated with the large electron-electron interaction present in the material. The magnetoresistance values increased as x increased, and we observed a large negative magnetoresistance (MR = -35%) for the thin film with x = 0.30. The observed high MR values are associated with spin fluctuation of the mobile electronic carriers in the material. (C) 2017 Elsevier B.V. All rights reserved.

Resistive switching memory, which is mostly based on polycrystalline thin films, suffers from wide distributions in switching parameters-including set voltage, reset voltage, and resistance-in their low- and high-resistance states. One of the most commonly used methods to overcome this limitation is to introduce inhomogeneity. By contrast, in this paper, we obtained uniform resistive switching parameters and sufficiently low forming voltage by maximizing the uniformity of an epitaxial thin film. To achieve this result, we deposited an SrFeOx/SrRuO3 heteroepitaxial structure onto an SrTiO3 (001) substrate by pulsed laser deposition, and then we deposited an Au top electrode by electron-beam evaporation. This device exhibited excellent bipolar resistance switching characteristics, including a high on/off ratio, narrow distribution of key switching parameters, and long data retention time. We interpret these phenomena in terms of a local, reversible phase transformation in the SrFeOx film between brownmillerite and perovskite structures. Using the brownmillerite structure and atomically uniform thickness of the heteroepitaxial SrFeOx thin film, we overcame two major hurdles in the development of resistive random-access memory devices: high forming voltage and broad distributions of switching parameters.

SrRu1-xFexO3-d (x = 0.00, 0.05, 0.10, and 0.20) thin films were fabricated to study the intrinsic aspects of a "self spin valve". Using epitaxial strain and high oxygen partial pressure during thin film growth, single phase thin films with negligible oxygen vacancies were successfully grown, and problems related to A-site disorder and grain boundaries were minimized. Under application of an external magnetic field of up to 9 T, the resistivity of all films decreased, resulting in large negative magnetoresistance (up to similar to 14.4%), which was stronger at temperatures in the range 10-30 K. An abrupt metal-insulator transition at T similar to 43 K was found in the x = 0.20 film, which was explained using a two-fluid model related to electron-electron interactions. From the model, two fitting parameters were found to be necessary for in-situ and homogenous defects, while three or unphysical fitting parameters were necessary for ex-situ and inhomogeneous defects. (C) 2015 Elsevier B.V. All rights reserved.

We observed bipolar switching behavior from an epitaxial strontium cobaltite film grown on a SrTiO3 (001) substrate. The crystal structure of strontium cobaltite has been known to undergo topotactic phase transformation between two distinct phases: insulating brownmillerite (SrCoO2.5) and conducting perovskite (SrCoO3−δ) depending on the oxygen content. The current–voltage characteristics of the strontium cobaltite film showed that it could have a reversible insulator-to-metal transition triggered by electrical bias voltage. We propose that the resistance switching in the SrCoOx thin film could be related to the topotactic phase transformation and the peculiar structure of SrCoO2.5.

Rb doped 0.94Bi(0.5)Na(0.5)TiO(3)-0.06BaTiO(3) (BNT-BT-Rb-x) thin films with x mol% Rb (x = 0, 2.5, 5, 7.5, 10) were deposited on Pt/Ti/SiO2/Si substrate by metal-organic solution deposition method. Experiments were conducted to investigate the effect of Rb doping on phase formation, microstructure, leakage current, and the resulting ferroelectric and piezoelectric property. It was found that substantial enhancement in structural, morphological and electrical properties can be achieved by Rb doping of BNT-BT thin films. Optimal electrical properties were obtained for 5 mol% Rb doped BNT-BT thin films, with a dielectric constant, remnant polarization, and effective piezoelectric constant of similar to 681, similar to 28.9 mu C/cm(2) and similar to 86 pm/V, respectively. It was suggested that the enhanced electrical properties in the case of 5 mol% Rb BNT-BT thin films can be attributed to domain wall movement induced by A-site substitutions, large grain size, and lattice distortion. (C) 2014 Elsevier B.V. All rights reserved.

The piezoelectric and the ferroelectric properties of x-mol% Li-doped 0.94Bi 0.5 Na 0.5 TiO 3 -0.06BaTiO 3 (BNT-BT-Li x ), x = 0, 5, 10, 15, 20) lead-free piezoelectric thin films deposited on Pt (111)/Ti/SiO 2 /Si substrates via a metal-organic solution deposition method were investigated. Our results show that the Li-substituted film has a remarkable improvement in electrical properties compared with the BNT-BT film. We also found that the substitution was effective in decreasing the coercive field in the thin films. The enhanced electromechanical properties are contributed by both crystal structure evolution and improved microstructure. The optimal ferroelectric properties were obtained in the film with x = 10 providing a remanent polarization ( P r ) and a coercive field ( E c ) of ∼23.9 µC/cm 2 and ∼124 kV/cm, respectively.