Ultimate conductivity performance in metallic nanowire networks

Rocha, C. G.; Manning, Hugh G.; O’Callaghan, Colin; Ritter, Carlos; Bellew, Allen T.; Boland, John J.; Ferreira, M. S.

doi:10.1039/c5nr03905c

Cited by 59 publications

(65 citation statements)

References 30 publications

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“…[275][276][277][278][279][280][281][282][283] In the following, resistive switching in stacked and planar devices based on NW networks is described. Resistive switching networks based on random ordered nanowires, dendritic, and fractal structures have attracted great attention because of the peculiar conduction properties that make these structures promising for resistive switching and neuromorphic applications.…”

Section: Resistive Switching In Nanowire Random Networkmentioning

confidence: 99%

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Milano

Porro

Valov

et al. 2019

Adv Elect Materials

107

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Memristive devices are considered one of the most promising candidates to overcome technological limitations for realizing next‐generation memories, logic applications, and neuromorphic systems in the modern nanoelectronics and information technology. Despite the continuous efforts, understanding the resistive switching mechanism underlying memristive/neuromorphic behavior still represents a challenge. Metal oxide nanowire‐based memristors appear suitable model systems for a deeper understanding of the involved physical/chemical phenomena due the possibility for localizing and investigating the switching mechanism. In practical aspects, nanowire‐based devices can be grown using a bottom‐up approach, thus being considered reliable candidates for going beyond the current scaling limitations of the top‐down approach by the standard lithography. In addition, taking into advantage of the high surface‐to‐volume ratio of these nanostructures, new device functionalities can be achieved by exploiting surface effects. In the literature, a variety of nanowire‐based devices such as single nanowires, nanowire arrays, and networks are reported to exhibit memristive behavior, explained by different switching mechanisms. This work provides a comparative review and a comprehensive analysis of device performances and physical phenomena responsible for memristive and neuromorphic behavior in such nanostructures. The analysis of the state‐of‐art of memristor devices based on nanowires and nanorods represent a milestone toward the development of nanowire‐based artificial neural networks.

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Section: Resistive Switching In Nanowire Random Networkmentioning

confidence: 99%

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Milano

Porro

Valov

et al. 2019

Adv Elect Materials

107

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“…We also use the MNR method to describe the percolating network of nanowires, including both linear and contact resistances (ρ and R c ) as in Ref. 1,5,19 , and adding the electrode/nanowire contact resistance in the model, for the first time. A closed form expression for the effective resistance, as a function of these physical parameters, is estimated numerically, by means of Monte-Carlo simulations and fitting of the obtained data points.…”

Section: Stick Conductancementioning

confidence: 99%

“…Geometrical coefficients A N i , Li l * , B N i , Li l * , C N i , Li l * are estimated numerically either for the virtual 'twin' networks of the real networks -in the case that the actual precise distribution of wires in the real network can be captured from an SEM analysis, as in Ref. 1 -, or from the average coefficients at the corresponding densities and aspect ratios. From these experimentally measured resistances, and numerically estimated geometrical coefficients, it is possible to recover the values of the elementary contact resistance between two wires, the wire resistance per unit length and the electrode/nanowire contact resistance (three unknowns, and at least three independent equations).…”

Section: Applications To the Understanding And Design Optimizatiomentioning

confidence: 99%

Effective resistance of random percolating networks of stick nanowires: Functional dependence on elementary physical parameters

Benda

Lebental

Cancès

2019

Journal of Applied Physics

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We study by means of Monte-Carlo numerical simulations the resistance of two-dimensional random percolating networks of stick, widthless nanowires. We use the multi-nodal representation (MNR) 1 to model a nanowire network as a graph. We derive numerically from this model the expression of the total resistance as a function of all meaningful parameters, geometrical and physical, over a wide range of variation for each. We justify our choice of non-dimensional variables applying Buckingham π−theorem. The effective resistance of 2D random percolating networks of nanowires is found to write as R eq (ρ, Rc, Rm,w) = A N, L l * ρl * + B N, L l * Rc + C N, L l * Rm,w where N , L l * are the geometrical parameters (number of wires, aspect ratio of electrode separation over wire length) and ρ, Rc, Rm,w are the physical parameters (nanowire linear resistance per unit length, nanowire/nanowire contact resistance, metallic electrode/nanowire contact resistance). The dependence of the resistance on the geometry of the network, one the one hand, and on the physical parameters (values of the resistances), on the other hand, is thus clearly separated thanks to this expression, much simpler than the previously reported analytical expressions. I. INTRODUCTIONThe study of random percolating networks of conducting nanowires has become in the last years a hot topic of investigation. Numerous applications are possible thanks to the outstanding electrical and optical performances of such thin films, combined to their low cost and ease of fabrication. Applications are as diverse as thin film transistors based on carbon nanotubes networks (CNNs) 2,3 , CNNs-based field-effect transistors 4 , transparent conductive (high transmittance, low resistance) thin-film electrodes based on silver nanowires 1,5 or carbon nanotubes (CNTs) 6 -useful for instance in the context of solar cells, the conducting nanowire network acting as a charge carrier collector 7 -and sensors 4 . The conductivity, as well as the sheet transmittance of such thin film nanowire networks has been extensively studied experimentally and theoretically 6,8,9 . These systems are well approximated by random 2D networks, and we study in this paper the dependence of their effective resistance on all structural and physical parameters. We focus on the functional dependence of the resistance on physical parameters, motivated by sensor applications of nanowire networks. In this context, physical parameters, such as nanowire linear resistance, or nanowire/nanowire contact resistance, can vary, due to environment changes. The geometry and structure of the network is thus not sufficient to understand properly the resistance variations.Many parameters impact the total effective resistance of nanowire random networks. Structural parameters such as the density (or coverage), the aspect ratio of electrode separation to wire length, and the alignment of the wires (i.e. the statistical distribution of their angles with respect to a fixed direction) are, among others, known to play on the trans...

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“…MNW networks have been the subject of a lot of research lately, with a focus on the industrial integration of the networks as transparent electrodes into opto‐electronic devices, as recently reviewed by several authors. [ 20,38,39,157,158 ] Among the possible MNW materials, silver has been the most investigated, [ 33,34,39,159–162 ] while CuNW networks constitute an interesting alternative. [ 48,66,76,163,164 ] Figure 7b exhibits a high‐resolution TEM image of a CuNW showing the fivefold symmetry.…”

Section: The Investigated Materials Technologies For Transparent Heatersmentioning

confidence: 99%

Transparent Heaters: A Review

Papanastasiou

Schultheiss

Muñoz‐Rojas

et al. 2020

Adv Funct Materials

187

191

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Transparent heaters (TH) have attracted intense attention from both scientific and industrial sectors due to the key role they play in many technologies, including smart windows, deicers, defoggers, displays, actuators, and sensors. While transparent conductive oxides have dominated the field for the past five decades, a new generation of THs based on nanomaterials has led to new paradigms in terms of applications and prospects in the past years. Here, the most recent developments and strategies to improve the properties, stability, and integration of these new THs are reviewed.

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Ultimate conductivity performance in metallic nanowire networks

Cited by 59 publications

References 30 publications

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Effective resistance of random percolating networks of stick nanowires: Functional dependence on elementary physical parameters

Transparent Heaters: A Review

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