Reference : Accelerated Ionic Motion in Amorphous Memristor Oxides for Nonvolatile Memories and N...
Scientific journals : Other
Physical, chemical, mathematical & earth Sciences : Physics
Physics and Materials Science
http://hdl.handle.net/10993/45049
Accelerated Ionic Motion in Amorphous Memristor Oxides for Nonvolatile Memories and Neuromorphic Computing
English
Schmitt, Rafael [> >]
Kubicek, Markus [> >]
Sediva, Eva [> >]
Trassin, Morgan [> >]
Weber, Mads C. [> >]
Rossi, Antonella [> >]
Hutter, Herbert [> >]
Kreisel, Jens mailto [University of Luxembourg > CRC > Vice-rectorate for Research (VR Research)]
Fiebig, Manfred [> >]
Rupp, Jennifer L. M. [> >]
2019
ADVANCED FUNCTIONAL MATERIALS
29
5
Yes
International
1616-301X
[en] 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.
http://hdl.handle.net/10993/45049
10.1002/adfm.201804782

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