Understanding oxygen anionic-electronic defects under high electric fields: Resistive switches devices

Nanoscale resistive switches (ReRAMs) were recently proposed as new class of non-volatile memories by switching non-linearly between low- and high-resistance values through application of voltage pulses in the ns-range. Through this paper we firstly introduce the topic of resistive switching oxides under high electric fields, their charge transport mechanism and often named memristive characteristics; and critically address open questions. In the second part we turn, to innovative new approaches in making of doped oxides and interface designs to novel device structures for oxide-based switches based on own results: Here, we will firstly discuss a mixed anionic electronic conductor model experiment, being a Gd-doped ceria series with tuned doping concentration to affect the defect association and mobility of the oxide switching bits in a systematic manner. We find a clear correlation between concentration and mobility of oxygen ionic carriers and resistive switching response, and discuss those down to the changes in the near order structures connected therein. Secondly, we exemplify the switching characteristics based on either compressively or tensely strained Gd0.1Ce0.9O2-x heterostructures modulated by Er2O3 or Sm2O3 layers, respectively, and discuss directly the device implication. Thereby, we present a new type of a model material device concept entitled a strained ReRAM . Here, new material engineering of oxides beyond doping is discussed to control resistive switching device properties like retention, Roff/Ron ratios and power consumption by interfacial strain engineering of mixed conducting oxide . Thirdly, we grow nanoscopically-flat LaFeO3 switching bits and demonstrate in a model experiment for amorphous and epitaxially grown films the implication of grain-boundary free but varying defect levels of the structures on resistive switching. Fourthly, we turn to the role of electric field and frequency dependencies of SrTiO3-based ReRAMs. Here, electrochemical impedance spectroscopy, cyclic voltammetry and chronoamperometry are used to investigate optimum operation concerning fast switching and stable retention with high resistance modulation. We show that two different switching mechanisms can be individually addressed depending on electric field strength and switching times. The Memristor-based Cottrell analysis is used to successfully determine diffusion constant characteristics of the materials and separating capacitive and memristive contributions. Finally, we conclude on the role of oxygen anionic-electronic carriers and transfer for oxide-based switches, and discuss the applicability for bits and circuits of potential memory and logic applications. References S. Schweiger, M. Kubicek, F. Messerschmitt, C. Murer, J.L.M. Rupp, ACS Nano, 8, 5, 5032, 2014. F. Messerschmitt, M. Kubicek, S. Schweiger, J.L.M. Rupp, Adv. Funct. Mater. 24, 47, 7448, 2014. F. Messerschmitt, M. Kubicek, J.L.M. Rupp, Adv. Funct. Mater. 25, 32, 5117, 2015. M. Kubicek, R. Schmitt, F. Messerschmitt, J.L.M. Rupp ACS Nano, 9, 11, 10737, 201

Jonctions tunnel ferroélectriques : memristors pour le calcul neuromorphique

Classical computer architectures are optimized to process pre-formatted information in a deterministic way and therefore struggle to treat unorganized natural data (images, sounds, etc.). As these become more and more important, the brain inspires new, neuromorphic computer circuits such as neural networks. Their energy-efficient hardware implementations will greatly benefit from nanodevices, called memristors, whose small size could enable the high synaptic connectivity degree observed in the brain.In this work, we concentrate on memristors based on ferroelectric tunnel junctions that are composed of an ultrathin ferroelectric film between two metallic electrodes. We show that the polarization reversal in BiFeO3 films can induce resistance contrasts as high as 10^4 and how mixed domain states are connected to intermediate resistance levels.Changing the electrode materials provides insights into their influence on the electrostatic barrier and dynamic properties of these memristors. Single-shot switching experiments reveal very fast polarization switching which we further investigate in cumulative measurements. Their analysis in combination with piezoresponse force microscopy finally allows us to establish a model describing the memristor dynamics under arbitrary voltage signals. After the demonstration of an important learning rule for neural networks, called spike-timing-dependent plasticity, we successfully predict new, previously unexplored learning curves. This constitutes an important step towards the realization of unsupervised self-learning hardware neural networks.Les architectures d’ordinateur classiques sont optimisées pour le traitement déterministe d’informations pré-formatées et ont donc des difficultés avec des données naturelles bruitées (images, sons, etc.). Comme celles-ci deviennent nombreuses, de nouveaux circuits neuromorphiques (inspirés par le cerveau) tels que les réseaux de neurones émergent. Des nano-dispositifs, appelés memristors, pourraient permettre leur implémentation sur puce avec une haute efficacité énergétique et en s’approchant de la haute connectivité synaptique du cerveau.Dans ce travail, nous étudions des memristors basés sur des jonctions tunnel ferroélectriques qui sont composées d’une couche ferroélectrique ultramince entre deux électrodes métalliques. Nous montrons que le renversement de la polarisation de BiFeO3 induit des changements de résistance de quatre ordres de grandeurs et établissons un lien direct entre les états de domaines mixtes et les niveaux de résistance intermédiaires.En alternant les matériaux des électrodes, nous révélons leur influence sur la barrière électrostatique et les propriétés dynamiques des memristors. Des expériences d’impulsion unique de tension montrent un retournement de polarisation ultra-rapide. Nous approfondissons l’étude de cette dynamique par des mesures d’impulsions cumulées. La combinaison de leur analyse avec de l’imagerie par microscopie à force piézoélectrique nous permet d’établir un modèle dynamique du memristor. Suite à la démonstration de la spike-timing-dependent plasticity, une règle d’apprentissage importante, nous pouvons prédire le comportement de notre synapse artificielle. Ceci représente une avance majeure vers la réalisation de réseaux de neurones sur puce dotés d’un auto-apprentissage non-supervisé

Silicon neuron dedicated to memristive spiking neural networks

International audienceSince memristor came out in 2008, neuromorphic designers investigated the possibility of using memristors as plastic synapses due to their intrinsic properties of plasticity and weight storage. In this paper we will present a silicon neuron compatible with memristive synapses in order to build analog neural network. This neuron mainly includes current conveyor (CCII) for driving memristor as excitatory or inhibitory synapses and spike generator whose waveform is dedicated to synaptic plasticity algorithm based on Spike Timing Dependent Plasticity (STDP). This silicon neuron has been fabricated, characterized and finally connected with a ferroelectric memristor to validate the synaptic weight updating principle

Learning through ferroelectric domain dynamics in solid-state synapses

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.ISSN:2041-172

Plasticity in memristive devices for spiking neural networks

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use