No abstract

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.

Wearable fabric-based energy harvesters have continued to gain importance for use in portable consumer electronics as an ecofriendly energy source that is independently self-powered by various activities. Herein, we address the output features of highly flexible Ni-Cu fabric-based triboelectric nanogenerators (F-TENG) employing surface-embossed polydimethylsiloxane (SE-PDMS) layers, as a crucial approach for enhancing power generation. Such SE-PDMS configurations were achieved via control of the ZnO nanowire (NW) and nanoflake (NF) frames initially prepared on bare Ni-Cu fabrics by a hydrothermal approach. The wearable SE-PDMS and Al-evaporated fabrics, respectively, served as triboelectric bottom and top materials in F-TENGs. Along with the structural analyses of the F-TENGs, the enhanced power generation of the F-TENGs was illustrated via the application of periodic mechanical stress using an adjustable bending machine. The present approach may provide a useful and simple route for developing self-powered, wearable, and smart electronics based on fabric substrates.

Magnetic skyrmions, which are topological swirling spin textures, have drawn much attention in spintronics because of their use as an information carrier with distinct robustness rooted in their topological nature. Real-time generation of skyrmions is therefore imperative for realizing skyrmion-based spintronic devices. However, to date, experimental demonstration has been limited to exquisite works with well-tuned samples. Here, we report a method to generate skyrmions by driving the stripe instability via an in-plane magnetic field. We have demonstrated that the key parameter determining the stripe domain instability is the stripe width, regardless of other material parameters. This skyrmion generation method can be applicable to generic magnetic films with perpendicular magnetic anisotropy. Our work will facilitate the development of skyrmion-based devices by offering a general method for controlling a large skyrmion population.

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.