Area-Dependent Resistive Switching and Interfacial Dynamics in GCMO-Based Memristors.

This study explores the area-dependent resistive switching (RS) characteristics of GdCaMnO (GCMO)-based memristors with aluminum (Al) and gold (Au) electrodes, emphasizing their potential for neuromorphic computing applications. Using a combination of electrical measurements and X-ray photoelectron spectroscopy (XPS), we demonstrate that the high-resistance (HRS) and low-resistance (LRS) states exhibit predictable scaling with device area, with HRS resistances ranging from 10 to 10 Ω and LRS from 10 to 10 Ω, supporting the hypothesis of interface-type RS. XPS depth profiling revealed notable differences in AlO interfacial layer composition between HRS and LRS, with a higher oxide content and a widened interfacial region in HRS.

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.

Bipolar Resistive Switching in 2D MoSe 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 MoSe 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 MoSe still represents a challenge.

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.

Electron Doping Effect in the Resistive Switching Properties of Al/GdCaMnO/Au Memristor Devices.

We report on the resistive switching (RS) properties of Al/GdCaMnO (GCMO)/Au thin-film memristors. The devices were studied over the whole calcium substitution range as a function of electrical field and temperature. The RS properties were found to be highly dependent on the Ca substitution.