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En électronique, le memristor (ou memristance) est un composant électronique passif. Il a été décrit comme le quatrième composant passif élémentaire, aux côtés du condensateur (ou capacité), du résistor (ou résistance) et de la bobine(ou inductance). Le nom est un mot-valise formé à partir des deux mots anglais memory et resistor. Un memristor stocke efficacement l’information car la valeur de sa résistance électrique change de façon permanente lorsqu’un courant est appliqué. Là où une résistance classique apporte une valeur stable de résistance, un memristor peut avoir une valeur élevée de résistance interprétable dans un ordinateur comme un « 1 » en termes logiques, et une faible valeur qui peut être interprétée comme un « 0 ». Ainsi, une donnée peut être enregistrée et réécrite par un courant de contrôle. Dans un certain sens, un memristor est une résistance variable qui, par la valeur de sa résistance, reflète sa propre histoire. Le memristor a é

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Analysis of Quantized Electrical Characteristics of Microscale TiO2 Ink-Jet Printed Memristor

Heinrich Hofmann, Marijana Mionic Ebersold, Goran Stojanovic

We demonstrate the ink-jet printed fabrication technique for TiO2-based memristor, followed by detailed analysis of electrical characteristics and development of a new model that considers observed phenomena of quantized conductance steps. The existence of pinched hysteretic current-voltage characteristics is evidence of memristive behavior, provided by the reversible atomic rearrangement taking place in the functional layer. For the first time, performed electrical measurement on the micrometer thickness devices based on TiO2 active layer has captured the plateaux steps of the conductance at integer multiples of elementary quantum conductance. This behavior is consistent with the assumption that transport from electrode to electrode emerges through confined paths of conductive filaments with radius in the nanometer size range. Moreover, we introduce a novel model, based on the diffusion equation for ballistic transport in memristive devices, which considers the conductance plateaux steps.

Institute of Electrical and Electronics Engineers2015

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Memristive Biosensors Under Varying Humidity Conditions

Camilla Baj-Rossi, Sandro Carrara, Akshat Dave, Giovanni De Micheli, Yusuf Leblebici, Francesca Puppo, Davide Sacchetto

We attempt to examine the potential of silicon nanowire memristors in the field of nanobiosensing. The mem- ristive devices are crystalline Silicon (Si) Nanowires (NWs) with Nickel Silicide (NiSi) terminals. The nanowires are fabricated on a Silicon-on-Insulator (SOI) wafer by an Ebeam Lithography Technique (EBL) process that allows high resolution at the nanoscale. A Deep Reactive Ion Etching (DRIE) technique is used to define free-standing nanowires. The close alignment between Silicon (Si) and Nickel-Silicide (NiSi) terminals forms a Schottky- barrier at their junction. The memristive effect of the fabricated devices matches well with the memristor theory. An equivalent circuit reproducing the memristive effect in current-voltage (I- V) characteristics of our silicon nanowires is presented too. The memristive silicon nanowire devices are then functionalized with anti-human VEGF (Vascular Endothelial Growth Factor) antibody and I-V characteristics are examined for the nanowires prior to and after protein functionalization. The uptake of bio- molecules linked to the surface of the memristive NWs is con- firmed by the increased voltage gap in the hysteresis curve. The effects of varying humidity conditions on the conductivity of bio- modified memristive silicon nanowires are deeply investigated

Institute of Electrical and Electronics Engineers2014

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All-memristive neuromorphic computing with level-tuned neurons

Evangelos Eleftheriou, Angeliki Pantazi, Stanislaw Andrzej Wozniak

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

Iop Publishing Ltd2016

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