Anomalous electrical conduction and negative temperature coefficient of resistance in nanostructured gold resistive switching films

Inkjet printed IGZO memristors with volatile and non-volatile switching

Solution-based memristors deposited by inkjet printing technique have a strong technological potential based on their scalability, low cost, environmentally friendlier processing by being an efficient technique with minimal material waste. Indium-gallium-zinc oxide (IGZO), an oxide semiconductor material, shows promising resistive switching properties. In this work, a printed Ag/IGZO/ITO memristor has been fabricated. The IGZO thickness influences both memory window and switching voltage of the devices. The devices show both volatile counter8wise (c8w) and non-volatile 8wise (8w) switching at low operating voltage. The 8w switching has a SET and RESET voltage lower than 2 V and - 5 V, respectively, a retention up to 105 s and a memory window up to 100, whereas the c8w switching shows volatile characteristics with a low threshold voltage (Vth < - 0.65 V) and a characteristic time (τ) of 0.75 ± 0.12 ms when a single pulse of - 0.65 V with width of 0.1 ms is applied. The characteristic time alters depending on the number of pulses. These volatile characteristics allowed them to be tested on different 4-bit pulse sequences, as an initial proof of concept for temporal signal processing applications.

Engineering the structural and electrical interplay of nanostructured Au resistive switching networks by controlling the forming process

Networks of random-assembled gold clusters produced in the gas phase show resistive switching (RS) activity at room temperature and they are suitable for the fabrication of devices for neuromorphic data processing and classification. Fully connected cluster-assembled nanostructured Au films are characterized by a granular structure rich of interfaces, grain boundaries and crystalline defects. Here we report a systematic characterization of the electroforming process of the cluster-assembled films demonstrating how this process affects the interplay between the nano- and mesoscale film structure and the neuromorphic characteristics of the resistive switching activity. The understanding and the control of the influence of the resistive switching forming process on the organization of specific structures at different scales of the cluster-assembled films, provide the possibility to engineer random-assembled neuromorphic architectures for data processing task.

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of non-linear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, and non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.

Gas-phase synthesis of nanoparticles: current application challenges and instrumentation development responses

Nanoparticles constitute fundamental building blocks required in several fields of application with current global importance. To fully exploit nanoparticle properties specifically determined by the size, shape, chemical composition and interfacial configuration, rigorous nanoparticle growth and deposition control is needed. Gas-phase synthesis, in particular magnetron-sputtering inert-gas condensation, provides unique opportunities to realise engineered nanoparticles optimised for the desired use case. Here, we provide an overview of recent nanoparticle growth experiments via this technique, how the latter can meet application-specific requirements, and what challenges might impede the wide-spread adoption for scalable industrial synthesis. More specifically, we discuss the timely topics of energy, catalysis, and sensing applications enabled by gas-phase synthesised nanoparticles, as well as recently emerging advances in neuromorphic devices for unconventional computing. Having identified the most relevant challenges and limiting factors, we outline how advances in nanoparticle source instrumentation and/or in situ diagnostics can address current shortcomings. Eventually we identify common trends and directions, giving our perspective on the most promising and impactful applications of gas-phase synthesised nanoparticles in the future.

Dynamical stochastic simulation of complex electrical behavior in neuromorphic networks of metallic nanojunctions

Nanostructured Au films fabricated by the assembling of nanoparticles produced in the gas phase have shown properties suitable for neuromorphic computing applications: they are characterized by a non-linear and non-local electrical behavior, featuring switches of the electric resistance whose activation is typically triggered by an applied voltage over a certain threshold. These systems can be considered as complex networks of metallic nanojunctions where thermal effects at the nanoscale cause the continuous rearrangement of regions with low and high electrical resistance. In order to gain a deeper understanding of the electrical properties of this nano granular system, we developed a model based on a large three dimensional regular resistor network with non-linear conduction mechanisms and stochastic updates of conductances. Remarkably, by increasing enough the number of nodes in the network, the features experimentally observed in the electrical conduction properties of nanostructured gold films are qualitatively reproduced in the dynamical behavior of the system. In the activated non-linear conduction regime, our model reproduces also the growing trend, as a function of the subsystem size, of quantities like Mutual and Integrated Information, which have been extracted from the experimental resistance series data via an information theoretic analysis. This indicates that nanostructured Au films (and our model) possess a certain degree of activated interconnection among different areas which, in principle, could be exploited for neuromorphic computing applications.

Neuromorphic behaviour in discontinuous metal films

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

Phase Transition to Heptagonal-Cluster-Packed Structure of Gold Nanoribbons

Transforming periodic crystals into packing of atomic clusters is attracting enormous interest for both fundamental research and potential application, but it still remains a big challenge for noble metals. Here, we have observed gold nanoribbons packed with heptagonal clusters, where every two or three constituent clusters connect edge-to-edge with their neighbors. This is the first reported metallic structure packed from building blocks with heptagonal symmetry. The cluster-packed nanoribbons transited from two-dimensional hexagonal structure under tensile condition and a reverse transition occurred by compression, resolved by in situ observation. The cluster-packed structure was stabilized by the s-d orbital hybridization. Theoretical calculations demonstrate that the conductance of the ribbons undergoes a quantized change from 6 to 4 G₀ (G₀ = 2e²/h) during the phase transition and backward for the reverse transition.