This article is protected by copyright. All rights reserved. track the frequency of the oxygen stretching mode around Fe 4+ , as it decreases during reduction as the material expands and increases during re-oxidation as the material shrinks. This methodology of oxygen pumping and in situ Raman of oxide films enables future in operando measurements even for small material volumes, as typical for applications of films as electrodes or electrolytes utilized in electrochemical energy conversion or memory devices.
Memristive devices based on mixed ionic–electronic resistive switches have an enormous potential to replace today's transistor‐based memories and Von Neumann computing architectures thanks to their ability for nonvolatile information storage and neuromorphic computing. It still remains unclear however how ionic carriers are propagated in amorphous oxide films at high local electric fields. By using memristive model devices based on LaFeO3 with either amorphous or epitaxial nanostructures, we engineer the structural local bonding units and increase the oxygen‐ionic diffusion coefficient by one order of magnitude for the amorphous oxide, affecting the resistive switching operation. We show that only devices based on amorphous LaFeO3 films reveal memristive behavior due to their increased oxygen vacancy concentration. We achieved stable resistive switching with switching times down to microseconds and confirm that it is predominantly the oxygen‐ionic diffusion character and not electronic defect state changes that modulate the resistive switching device response. Ultimately, these results show that the local arrangement of structural bonding units in amorphous perovskite films at room temperature can be used to largely tune the oxygen vacancy (defect) kinetics for resistive switches (memristors) that are both theoretically challenging to predict and promising for future memory and neuromorphic computing applications.
Memristive devices are among the most prominent candidates for future computer memory storage and neuromorphic computing. Though promising, the major hurdle for their industrial fabrication is their device-to-device and cycle-to-cycle variability. These occur due to the random nature of nanoionic conductive filaments, whose rupture and formation govern device operation. Changes in filament location, shape, and chemical composition cause cycle-to-cycle variability. This challenge is tackled by spatially confining conductive filaments with Ni nanoparticles. Ni nanoparticles are integrated on the bottom La 0.2 Sr 0.7 Ti 0.9 Ni 0.1 O 3−δ electrode by an exsolution method, in which, at high temperatures under reducing conditions, Ni cations migrate to the perovskite surface, generating metallic nanoparticles. This fabrication method offers fine control over particle size and density and ensures strong particle anchorage in the bottom electrode, preventing movement and agglomeration. In devices based on amorphous SrTiO 3 , it is demonstrated that as the exsolved Ni nanoparticle diameter increases up to ≈50 nm, the ratio between the ON and OFF resistance states increases from single units to 180 and the variability of the low resistance state reaches values below 5%. Exsolution is applied for the first time to engineer solid-solid interfaces extending its realm of application to electronic devices.
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