Electrochemical Control of the Conductivity in an Organic Memristor: A Time-Resolved X-ray Fluorescence Study of Ionic Drift as a Function of the Applied Voltage

Abstract

Grazing-incidence X-ray fluorescence measurements were applied for a time-resolved study of an organic memristor conductivity variation mechanism. A comparison of these results with electrical measurements has allowed us to conclude that the variation of the fluorescence intensity of Rb ions is directly connected to the ionic charge transferred between the conducting polymer and the solid electrolyte, which made up the device. In addition, the conductivity of the memristor was shown to be a function of the transferred ionic charge.

Memristive devices fabricated with silicon nanowire schottky barrier transistors

“…These phenomena are mainly based on the application of an external bias across the device which results in a drift of charged dopant in the device channel [5]. However, memristors based on either organic materials [4], [7] or TiO2 [1] cannot be easily integrated with silicon technology for VLSI applications. Therefore, new fabrication processes are required to investigate memristor as a novel element for realization of integrated circuits.…”

First steps towards the realization of a double layer perceptron based on organic memristive devices

“…The electronic current presents a slight increment before 0.5 V applied, while, at about 0.7 V, the current increases markedly, because of the oxidation process. 24 During the backward voltage sweep, the reduction process results in the conductivity decrease. According to this, the voltage of 0.4 V was used for reading the output values and memristive device's conductance to prevent their noticeable variations.…”

“…Learning was performed following back propagation learning algorithm described in Ref. 24, simplified by replacing the derivative of the activation function by a constant 0.5 to speed up the convergence. Optimal learning rate constant η was found to be equal to 2 for used initial weights uniformly distributed on the interval.…”

Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning

“…The other essential feature of the presented characteristics is connected to the fact that the values of the ionic current is about one order of magnitude lower with respect to the total current value. It means that the composition of the solid electrolyte in the network was optimal -the concentration of ions, whose motion is necessary for the conductivity switching, 24 is enough for the effective carrying out of the redox reactions, but the presence of these ions does not contribute significantly to the value of the total device current (stochastic nature of the network does not allow to have more pronounced difference between the values of these current; it can reach about three orders of magnitude in devices with a deterministic architecture). 4 The cyclic characteristics for the differential current, representing the electronic compound of the total current of the single branch of the stochastic network, is shown in the Fig.…”