Dataset Information

Tunnel conductivity switching in a single nanoparticle-based nano floating gate memory.

ABSTRACT: Nanoparticles (NPs) embedded in a conductive or insulating matrix play a key role in memristors and in flash memory devices. However, the role of proximity to the interface of isolated NPs has never been directly observed nor fully understood. Here we show that a reversible local switching in tunnel conductivity can be achieved by applying an appropriate voltage pulse using the tip of a scanning tunnelling microscope on NPs embedded in a TiO2 matrix. The resistive switching occurs in the TiO2 matrix in correlation to the NPs that are in proximity of the surface and it is spatially confined to the single NP size. The tunnel conductivity is increased by more than one order of magnitude. The results are rationalized by a model that include the charge of NPs that work as a nano floating gate inducing local band bending that facilitates charge tunnelling and by the formation and redistribution of oxygen vacancies that concentrate in proximity of the charged NPs. Our study demonstrates the switching in tunnel conductivity in single NP and provides useful information for the understanding mechanism or resistive switching.

SUBMITTER: Gambardella A

PROVIDER: S-EPMC3935202 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon.

Dataset Information

Tunnel conductivity switching in a single nanoparticle-based nano floating gate memory.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets