Recommended implementation of electrical resistance tomography for conductivity mapping of metallic nanowire networks using voltage excitation

Abstract

The knowledge of the spatial distribution of the electrical conductivity of metallic nanowire networks (NWN) is important for tailoring the performance in applications. This work focuses on Electrical Resistance Tomography (ERT), a technique that maps the electrical conductivity of a sample from several resistance measurements performed on its border. We show that ERT can be successfully employed for NWN characterisation if a dedicated measurement protocol is employed. When applied to other materials, ERT measurements are typically performed with a constant current excitation; we show that, because of the peculiar microscopic structure and behaviour of metallic NWN, a constant voltage excitation protocols is preferable. This protocol maximises the signal to noise ratio in the resistance measurements—and thus the accuracy of ERT maps—while preventing the onset of sample alterations.

Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks

“…The approximation turns out to be valid even over few NW lengths scale for normalized nanowire density D > 2D c , where D c = 5.63 is the percolation critical density [25]. Also, previous works [26,27] on NW network conductivity mapping by electrical resistance tomography have outlined a nearly homogenous conductivity distribution across NW network by tailoring drop-casting. Experimental data of Ag NW networks [5] considered here have been measured on a sample (figure 1(a)) with an estimated normalized density D = 39.06, well above the percolation limit.…”

Tomography of memory engrams in self-organizing nanowire connectomes

“…This non-scanning technique is based on the reconstruction of the spatial distribution of conductivity across the network from boundary electrical measurements. The set of four-terminal resistance measurements required for ERT reconstruction was experimentally acquired through an adjacent pattern measurement scheme (details of the measurement protocol in Supplementary note 2), 41,42 by injecting current in between a pair of adjacent terminals through a constant applied voltage bias ( while measuring voltage across remaining pairs of adjacent terminals ( ), as schematized in Fig. 2a (details in Methods, Supplementary Figure S4).…”

“…Multiterminal electrical characterization were performed by contacting the samples with spring-mounted needle probes through a custom xture, as described in previous works (details in Supplementary Figure S2). 41,42,47 Needle probes have a contact section of about 40 µm in diameter, ensuring reliable contacts to NW networks with AMD falling within the range 60 − 181 mg/m 2 . 42 The impedance matrix, electrically describing the NW network internal state, was acquired according to the so-called adjacent measurement protocol with constant voltage excitation that maximizes the signalto-noise ratio in the measurements, while preventing electrical alterations of the NW network sample, as detailed in our previous work (details in Supplementary Note 2).…”

“…42 The impedance matrix, electrically describing the NW network internal state, was acquired according to the so-called adjacent measurement protocol with constant voltage excitation that maximizes the signalto-noise ratio in the measurements, while preventing electrical alterations of the NW network sample, as detailed in our previous work (details in Supplementary Note 2). 41 For each measurement con guration represented by a pair of adjacent source terminals and a pair of adjacent sense terminals , the transresistance is calculated as: 1 where is the measured current owing in between adjacent source terminals when a voltage bias is applied, while is the voltage drop measured across adjacent sense terminals.…”

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

“…Modeling of the emergent spatiotemporal dynamics of the reservoir was performed by mapping the nanonetwork in a grid-graph model [48], as schematized in figure 2(a). This model, that approximates the nanonetwork as a continuous and uniform medium (as expected for high density networks [49][50][51]), is based on the parcellation of the network domain that is then abstracted as a regular grid graph where interaction in between network areas (nodes) is provided by memristive connections (edges). A detail of a memristive edge interaction is schematized in figure 2(b), where the edge conductance depends on the history of electrical stimulation.…”