Temperature effect on a weighted vortex spin-torque nano-oscillator for neuromorphic computing.

In this work, we present fabricated magnetic tunnel junctions (MTJs) that can serve as magnetic memories (MMs) or vortex spin-torque nano-oscillators (STNOs) depending on the device geometry. We explore the heating effect on the devices to study how the performance of a neuromorphic computing system (NCS) consisting of MMs and STNOs can be enhanced by temperature. We further applied a neural network for waveform classification applications. The resistance of MMs represents the synaptic weights of the NCS, while temperature acts as an extra degree of freedom in changing the weights and TMR, as their anti-parallel resistance is temperature sensitive, and parallel resistance is temperature independent. Given the advantage of using heat for such a network, we envision using a vertical-cavity surface-emitting laser (VCSEL) to selectively heat MMs and/or STNO when needed. We found that when heating MMs only, STNO only, or both MMs and STNO, from 25 to 75 °C, the output power of the STNO increases by 24.7%, 72%, and 92.3%, respectively. Our study shows that temperature can be used to improve the output power of neural networks, and we intend to pave the way for future implementation of a low-area and high-speed VCSEL-assisted spintronic NCS.

Bipolar Resistive Switching in 2D MoSe2 Grown by Atmospheric Pressure Chemical Vapor Deposition.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are highly promising nanomaterials for various electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. 2D MoSe2 stands out for having high electrical conductivity, charge carrier mobility, and melting point. While these features make it particularly appropriate as a switching layer in memristive devices, reliable and scalable production of large-area 2D MoSe2 still represents a challenge. In this study, we manufacture 2D MoSe2 films by atmospheric-pressure chemical vapor deposition and investigate them on the atomic scale. We selected and transferred MoSe2 bilayer to serve as a switching layer between asymmetric Au-Cu electrodes in miniaturized crossbar vertical memristors. The electrochemical metallization devices showed forming-free, bipolar resistive switching at low voltages, with clearly identifiable nonvolatile states. Other than low-power neuromorphic computing, low switching voltages approaching the range of biological action potentials could unlock hybrid biological interfaces.

Multilayer spintronic neural networks with radiofrequency connections.

Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided 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 radiofrequency 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 radiofrequency 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 radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

Electron Doping Effect in the Resistive Switching Properties of Al/Gd1-xCaxMnO3/Au Memristor Devices.

We report on the resistive switching (RS) properties of Al/Gd1-xCaxMnO3 (GCMO)/Au thin-film memristors. The devices were studied over the whole calcium substitution range x as a function of electrical field and temperature. The RS properties were found to be highly dependent on the Ca substitution. The optimal concentration was determined to be near x = 0.9, which is higher than the values reported for other similar manganite-based devices. We utilize an equivalent circuit model which accounts for the obtained results and allows us to determine that the electrical conduction properties of the devices are dominated by the Poole-Frenkel conduction mechanism for all compositions. The model also shows that lower trap energy values are associated with better RS properties. Our results indicate that the main RS properties of Al/GCMO/Au devices are comparable to those of other similar manganite-based materials, but there are marked differences in the switching behavior, which encourage further exploration of mixed-valence perovskite manganites for RS applications.