Continuum percolation with tunneling

Fostner, Shawn; Brown, Richard G.; Carr, James C.; Brown, Simon

doi:10.1103/physrevb.89.075402

Cited by 28 publications

(57 citation statements)

References 17 publications

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“…The disks are allowed to have random positions and we have previously shown that this continuum percolation model provides a good representation of the random assemblies of nanoparticles that are of interest here [33][34][35]. Reference [33] provides a more detailed comparison with (and discussion of) the experimental system-note in particular that when the particles touch they are strongly connected electrically, as are overlapping disks.…”

Section: Simulation Detailsmentioning

confidence: 99%

“…[33]. Tunneling processes [37,38] and related soft percolation problems [39] have been studied for some time, but it has only recently been demonstrated [33,40,41] that when tunneling is allowed the system conductance depends exponentially on surface coverage p below the percolation threshold, p c = 0.676336 [42] and that for p > p c the system conductance obeys the power law:…”

Section: Simulation Detailsmentioning

confidence: 99%

“…Note that we have chosen to focus on voltage ramps for consistency with the experimental protocol used in Ref. [33], but discuss dc applied voltages and voltage pulses in Sec. IV D.…”

Section: Simulation Detailsmentioning

confidence: 99%

“…We focus on simulations of overlapping disks for a system size of 200 × 200 particle diameters (chosen to provide the best [33] means that there are fewer tunnel or switching junctions between groups and a much higher conductance prior to the switching process (by up to 8 orders of magnitude); it also means that there are fewer locations at which switching can occur.…”

Section: Simulation Detailsmentioning

confidence: 99%

“…1), where the conductance of the system is due to tunneling across gaps between the groups of connected particles. The connections between overlapping particles are assumed to have negligible resistance [33]. Each gap is assigned a conductance,…”

Section: Simulation Detailsmentioning

confidence: 99%

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Neuromorphic behavior in percolating nanoparticle films

Fostner

Brown

2015

Phys. Rev. E

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We show that the complex connectivity of percolating networks of nanoparticles provides a natural solid-state system in which bottom-up assembly provides a route to realization of neuromorphic behavior. Below the percolation threshold the networks comprise groups of particles separated by tunnel gaps; an applied voltage causes atomic scale wires to form in the gaps, and we show that the avalanche of switching events that occurs is similar to potentiation in biological neural systems. We characterize the level of potentiation in the percolating system as a function of the surface coverage of nanoparticles and other experimentally relevant variables, and compare our results with those from biological systems. The complex percolating structure and the electric field driven switching mechanism provide several potential advantages in comparison to previously reported solid-state neuromorphic systems.

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Section: Simulation Detailsmentioning

confidence: 99%

Section: Simulation Detailsmentioning

confidence: 99%

“…Note that we have chosen to focus on voltage ramps for consistency with the experimental protocol used in Ref. [33], but discuss dc applied voltages and voltage pulses in Sec. IV D.…”

Section: Simulation Detailsmentioning

confidence: 99%

Section: Simulation Detailsmentioning

confidence: 99%

Section: Simulation Detailsmentioning

confidence: 99%

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Neuromorphic behavior in percolating nanoparticle films

Fostner

Brown

2015

Phys. Rev. E

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Experimental Demonstration of Reservoir Computing with Self‐Assembled Percolating Networks of Nanoparticles

Mallinson,

Steel,

Heywood

et al. 2024

Advanced Materials

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The complex self‐assembled network of neurons and synapses that comprizes the biological brain enables natural information processing with remarkable efficiency. Percolating Networks of Nanoparticles (PNNs) are complex self‐assembled nanoscale systems that have been shown to possess many promising brain‐like attributes and which are therefore appealing systems for neuromorphic computation. Here we perform experiments that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, non‐linear transformation and memory capacity). For each task we compile relevant literature results and show that the performance of the PNNs compares favourably to that previously reported from nanoelectronic reservoirs. We then demonstrate experimentally that PNNs can be used for spoken digit recognition with state‐of‐the‐art accuracy. Finally, we emulate a parallel reservoir architecture, which increases the dimensionality and richness of the reservoir outputs and results in further improvements in performance across all tasks.This article is protected by copyright. All rights reserved

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Computational Modeling of Nanoparticle Coalescence

Grammatikopoulos

Sowwan

Kioseoglou

2019

Advcd Theory and Sims

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The coalescence of nanoclusters fabricated in the gas phase is a fundamental growth mechanism determining cluster shapes, sizes, compositions, and structures, with resultant effects on practically all of their physical and chemical properties. Furthermore, coalescence can affect properties of larger structures that consist of nanoparticles as their elementary building blocks, such as the fractal dimension of cluster aggregates and the porosity and conductance of thin films. Therefore, it comes as no surprise that a great body of research, both experimental and theoretical, has focused on nanoparticle coalescence over the course of the past few decades. This review attempts to summarize the most important recent results from computational studies on nanoparticle coalescence and draw parallels between theoretical and experimental findings. The approach used here aspires to explain nanoparticle coalescence within the framework of a single intuitive narrative by integrating previous results obtained using various methods by the authors and others. Simultaneously, it is discussed where understanding and controlling (i.e., enhancing or inhibiting) nanoparticle coalescence can have great technological interest.

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Continuum percolation with tunneling

Cited by 28 publications

References 17 publications

Neuromorphic behavior in percolating nanoparticle films

Neuromorphic behavior in percolating nanoparticle films

Experimental Demonstration of Reservoir Computing with Self‐Assembled Percolating Networks of Nanoparticles

Computational Modeling of Nanoparticle Coalescence

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