Ultrafast and accurate prediction of polycrystalline hafnium oxide phase-field ferroelectric hysteresis using graph neural networks.

Polycrystalline hafnium oxide emerges as a promising material for the future of nanoelectronic devices. While phase-field modeling stands as a primary choice tool for forecasting domain structure evolution and electromechanical properties of ferroelectric materials, it suffers from a high computational cost, which impedes its applicability to real-size systems. Here, we propose a Graph Neural Network (GNN) machine-learning framework to predict the ferroelectric hysteresis of polycrystalline hafnium oxide, with the goal of significantly accelerating computations in contrast to high-fidelity phase-field methods.

Conductive-bridge memory cells based on a nanoporous electrodeposited GeSbTe alloy.

We report on the fabrication of memory devices based on a nanoporous GeSbTe layer electrodeposited inbetween TiN and Ag electrodes. It is shown that devices can operate along two distinct electrical modes consisting of a volatile or a non-volatile resistance switching mode upon appropriate preconditioning procedures. Based on electrical measurements conducted in both switching modes and physical analysis performed on a device after electrical stress, resistance switching is attributed to the formation/dissolution of a conductive filament from the Ag electrode into the GST layer whereas the volatile/non-volatile resistance switching is attributed to the presence of an interface layer between the GST and the Ag top electrode.