The resistance switching current-voltage $\left(I\text{-}V\right)$ characteristics in polycrystalline NiO films were investigated in the temperature range of $10\mathrm{K}<T<300\mathrm{K}$ . Very clear reversible resistive switching phenomena were observed in the entire temperature range. An analysis of the temperature dependence of the resistance switching transport revealed additional features, not reported in previous studies, that weak metallic conduction and correlated barrier polaron hopping coexist in the high-resistance off state and that relative dominance depends on the temperature and defect configuration. In addition, the authors propose that metallic Ni defects, existing near polycrystalline (or granular) boundaries, play a key role in the formation of a metallic channel.

Perpendicularly magnetized tunnel junctions (p-MTJs) show promise as reliable candidates for next-generation memory due to their outstanding features. However, several key challenges remain that affect CoFeB/MgO-based p-MTJ performance. One significant issue is the low thermal stability (Δ) due to the rapid perpendicular magnetic anisotropy (PMA) degradation during annealing at temperatures greater than 300 °C. Thus, the ability to provide thermally robust PMA characteristics is a key steps towards extending the use of these materials. Here, we examine the influence of a W spacer on double MgO/CoFeB/W/CoFeB/MgO frames as a generic alternative layer to ensure thermally-robust PMAs at temperatures up to 425 °C. The thickness-dependent magnetic features of the W layer were evaluated at various annealing temperatures to confirm the presence of strong ferromagnetic interlayer coupling at an optimized 0.55 nm W spacer thickness. Using this W layer we achieved a higher Δ of 78 for an approximately circular 20 nm diameter free layer device.

One long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices.

The integration of ZnO nanowire-based energy harvesting devices into flexible polyesters or clothes would have a significant effect on the energy harvesting building block for harvesting the mechanical energy from human motions. Moreover, the demonstration of high output power via a doping process opens an important method for enhancing the output power. Here, we report solution-based synthesis of Ag-doped ZnO nanowires on flexible polyester substrates without using any high temperature annealing processes. Along with the structural and optical characteristics of the Ag-doped ZnO nanowires, we demonstrate the efficient features of Ag-doped nanogenerators through the measurement of a sound-driven piezoelectric energy device with an output power of 0.5 μW, which is nearly 2.9 times that of a nanogenerator with un-doped ZnO NWs. This finding could provide the possibility of high output nanogenerators for practical applications in future portable/wearable personal displays and motion sensors.