Entropy-stabilized oxide: A promising material for artificial neural networks?

Entropy-stabilized oxide (ESO) materials show great promise for a variety of applications, including energy and electronic applications, due to their unique properties of lattice distortion, compositional modulation freedom, and cocktail effects. Recently, written in Nature Electronics, the ESO was applied for tunable and stable memristor devices. Furthermore, tuning the Mg composition in the oxide film provides internal switching dynamics for reservoir computing neural networks, which exhibited better recognition accuracy and energy efficiency compared with previously reported memristor-based reservoir computing systems. Further material and device designs are proposed to improve ESO-based memristor devices for future memory and neuromorphic computing applications. Entropy-stabilized oxide (ESO) materials show great promise for a variety of applications, including energy and electronic applications, due to their unique properties of lattice distortion, compositional modulation freedom, and cocktail effects. Recently, written in Nature Electronics, the ESO was applied for tunable and stable memristor devices. Furthermore, tuning the Mg composition in the oxide film provides internal switching dynamics for reservoir computing neural networks, which exhibited better recognition accuracy and energy efficiency compared with previously reported memristor-based reservoir computing systems. Further material and device designs are proposed to improve ESO-based memristor devices for future memory and neuromorphic computing applications.