Neuromorphic behavior in percolating nanoparticle films

Fostner, Shawn; Brown, Simon

doi:10.1103/physreve.92.052134

Cited by 30 publications

(47 citation statements)

References 60 publications

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“…[32]- [34] Training of a single 'output layer' then allows implementation of various time series prediction, pattern recognition and classification tasks. [36]- [38] Randomly assembled atomic switch networks (ASNs) based on sulphidised Ag nanowires [10], [11] and percolating films of nanoparticles [39], [40] are immediately ammenable to RC. ASNs are also an appealing alternative to regular arrays of devices because they allow realisation of complexity similar to that of the brain and fabrication via self-assembly immediately circumvent the limitations of lithographic processing.…”

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confidence: 99%

Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications

Bose

Mallinson

Gazoni

et al. 2017

IEEE Trans. Electron Devices

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Nature inspired neuromorphic architectures are being explored as an alternative to imminent limitations of conventional complementary metal-oxide semiconductor (CMOS) architectures. Utilization of such architectures for practical applications like advanced pattern recognition tasks will require synaptic connections that are both reconfigurable and stable. Here, we report realization of stable atomic-switch networks (ASN), with inherent complex connectivity, self-assembled from percolating metal nanoparticles (NPs). The device conductance reflects the configuration of synapses which can be modulated via voltage stimulus. By controlling Relative Humidity (RH) and oxygen partial-pressure during NP deposition we obtain stochastic conductance switching that is stable over several months. Detailed characterization reveals signatures of electric-field induced atomic-wire formation within the tunnel-gaps of the oxidized percolating network. Finally we show that the synaptic structure can be reconfigured by stimulating at different repetition rates, which can be utilized as short-term to long-term memory conversion. This demonstration of stable stochastic switching in ASNs provides a promising route to hardware implementation of biological neuronal models and, as an example, we highlight possible applications in Reservoir Computing (RC). Index TermsAtomic switch networks, Clusters, Neuromorphic architecture I. INTRODUCTIONT HE astounding success of the von Neumann architecture for computers [1], as encapsulated in Moore's Law, is now meeting with fundamental limitations (physical transistor dimensions are approaching classical limits) and practical limitations (the exponential increase in research and development costs for every new process line) [2], [3]. Natural information processing systems, like the biological brain, on the other hand, can perform highly complex computational tasks like navigation, recognition and decision-making with remarkable ease and with very low energy consumption [4]. This natural computation, processing the useful data (patterns) from a multitude of sensory information, is immediate and cannot be matched by even the most-advanced supercomputers [5], [6]. Nature inspired architectures [7]-[13] are therefore currently being pursued as a disruptive alternative to the von Neumann architecture. A recent review on Neuromorphic architecture [14] and implementations can be found in Ref. [15].The alternative brain-inspired hardware approach must address three key issues simultaneously: mimic the complex biological network of neurons, replicate synaptic structures and allow implementation of standard computational algorithms [16]. Achieving all of these goals is obviously enormously challenging and will require long-term research. Nevertheless significant progress has been made towards solving several different problems, using a variety of architectures. Proposals for non-CMOS approaches include those based on networks of memristors [17]-[19], atomic switches [10], [11] and Metal Oxide Resistive Rando...

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confidence: 99%

Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications

Bose

Mallinson

Gazoni

et al. 2017

IEEE Trans. Electron Devices

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“…On the other hand, once charge was transferred between clusters, a double-charge surface layer was formed in each particle, leading to their repulsion and suppressing their coalescence. [146] The inherent complexity of these nanoparticle networks, exhibiting power-law behavior near criticality, makes them promising candidates for on-chip neuromorphic computation and memristor applications. For example, Fostner et al investigated devices based on Sn clusters specifically grown so that they would be just below the percolation threshold, where quantum mechanical tunneling is dominant, [144] as also indicated by Ayesh.…”

Section: Calculated Properties Of Nanoparticulated Filmsmentioning

confidence: 99%

“…[145] By application of well-controlled, pulsed voltage sequences, the devices exhibited neuromorphic properties, where groups of connected clusters can be likened to neurons, and tunnel gaps provide switching elements similar to those of brain synapses. [146] The inherent complexity of these nanoparticle networks, exhibiting power-law behavior near criticality, makes them promising candidates for on-chip neuromorphic computation and memristor applications. [147] Another trending field in nanotechnology is the clustersupport interaction of clusters fabricated by cluster beam deposition.…”

Section: Calculated Properties Of Nanoparticulated Filmsmentioning

confidence: 99%

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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“…Near the percolation threshold, networks of self-assembled nanoparticles contain a multitude of interconnected atomic-switches. 21,24,28 Fig. 1 (a) shows a typical scanning electron micrograph (SEM) of such a network with percolating pathways.…”

Section: Synaptic Network Of Atomic-switchesmentioning

confidence: 99%

“…The green markers denote the atomic-switches that reconfigured (active synapses) between the gray shaded ohmically connected groups of nanoparticles. The details about the simulations are discussed elsewhere 21 . The increased density as well as spatial distribution of the active synapses at higher voltages (b) indicates activation of previously inactive synapses when lowvoltage was applied (a).…”

Section: Network Activity and Event-ratesmentioning

confidence: 99%

Synaptic dynamics in complex self-assembled nanoparticle networks

et al. 2019

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We report a detailed study of neuromorphic switching behaviour in inherently complex percolating networks of self-assembled metal nanoparticles. We show that variation of the strength and duration of the electric field applied to this network of synapse-like atomic switches allows us to control the switching dynamics. Switching is observed for voltages above a well-defined threshold, with higher voltages leading to increased switching rates. We demonstrate two behavioral archetypes and show how the switching dynamics change as a function of duration and amplitude of the voltage stimulus. We show that the state of each synapse can influence the activity of the other synapses, leading to complex switching dynamics. We further demonstrate the influence of the morphology of the network on the measured device properties, and the constraints imposed by the overall network conductance. The correlated switching dynamics, device stability over long periods, and the simplicity of the device fabrication provide an attractive pathway to practical implementation of on-chip neuromorphic computing. arXiv:1812.09865v1 [cond-mat.dis-nn]

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Neuromorphic behavior in percolating nanoparticle films

Cited by 30 publications

References 60 publications

Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications

Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications

Computational Modeling of Nanoparticle Coalescence

Synaptic dynamics in complex self-assembled nanoparticle networks

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