Filamentary Switching: Synaptic Plasticity through Device Volatility

Replicating the computational functionalities and performances of the brain remains one of the biggest challenges for the future of information and communication technologies. Such an ambitious goal requires research efforts from the architecture level to the basic device level (i.e., investigating the opportunities offered by emerging nanotechnologies to build such systems). Nanodevices, or, more precisely, memory or memristive devices, have been proposed for the implementation of synaptic functions, offering the required features and integration in a single component. In this paper, we demonstrate that the basic physics involved in the filamentary switching of electrochemical metallization cells can reproduce important biological synaptic functions that are key mechanisms for information processing and storage. The transition from short- to long-term plasticity has been reported as a direct consequence of filament growth (i.e., increased conductance) in filamentary memory devices. In this paper, we show that a more complex filament shape, such as dendritic paths of variable density and width, can permit the short- and long-term processes to be controlled independently. Our solid-state device is strongly analogous to biological synapses, as indicated by the interpretation of the results from the framework of a phenomenological model developed for biological synapses. We describe a single memristive element containing a rich panel of features, which will be of benefit to future neuromorphic hardware systems

Accessing the strong interaction between Λ baryons and charged kaons with the femtoscopy technique at the LHC

The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=-2 sector of the meson–baryon interaction at low energies. In particular, the Lambda-Kbar might help in understanding the origin of states such as the Csi(1620), whose nature and properties are still under debate. Experimental data on Lambda-K and Lambda-Kbar systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ–KK− and Λ–KK+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at sqrt(s) = 13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednický–Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ–KK+ correlations show the presence of several structures at relative momenta k* above 200 MeV/c, compatible with the Ω baryon, the , and resonances decaying into Λ–K− pairs. The low k* region in the Λ–KK+ also exhibits the presence of the state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the decaying into ΛK−

Développement des dispositifs memristifs filamentaires pour l'implémentation de la plasticité synaptique

Reproduire les fonctionnalités du cerveau représente un défi majeur dans le domaine des technologies de l’information et de la communication. Plus particulièrement, l’ingénierie neuromorphique, qui vise à implémenter au niveau matériel les propriétés de traitement de l’information du cerveau, apparait une direction de recherche prometteuse. Parmi les différentes stratégies poursuivies dans ce domaine, la proposition de composant memristif a permis d’envisager la réalisation des fonctionnalités des synapses et de répondre potentiellement aux problématiques d’intégration. Dans cette dissertation, nous présenterons comment les fonctionnalités synaptiques avancées peuvent être réalisées à partir de composants mémoires memristifs. Nous présentons une revue de l’état de l’art dans le domaine de l’ingénierie neuromorphique. En nous intéressant à la physique des composants mémoires filamentaires de type cellules électrochimiques, nous démontrons comment les processus de mémoire à court terme et de mémoire à long terme présents dans les synapses biologiques peuvent être réalisés en contrôlant la croissance de filaments de type dendritiques. Ensuite nous implémentons dans ces composants une fonctionnalité synaptique basée sur la corrélation temporelle entre les signaux provenant des neurones d’entrée et de sortie. Ces deux approches sont ensuite analysées à partir d’un modèle inspiré de la biologie permettant de mettre l’accent sur l’analogie entre synapses biologiques et composants mémoires filamentaires. Finalement, à partir de cette approche de modélisation, nous évaluons les potentialités de ces composants mémoires pour la réalisation de fonctions neuromorphiques concrètes.Replicating the computational functionalities of the brain remains one of the biggest challenges for the future of information and communication technologies. In this context, neuromorphic engineering appears a very promising direction. In this context memristive devices have been recently proposed for the implementation of synaptic functions, offering the required features and integration potentiality in a single component. In this dissertation, we present how advanced synaptic features can be implemented in memristive nanodevices. By exploiting the physical properties of filamentary switching, we successfully implemented a non-Hebbian plasticity form corresponding to the synaptic adaptation. We demonstrate that complex filament shape, such as dendritic paths of variable density and width, can reproduce short- and long- term processes observed in biological synapses and can be conveniently controlled by achieving a flexible way to program the device memory state and the relative state volatility. Then, we show that filamentary switching can be additionally controlled to reproduce a Hebbian plasticity form that corresponds to an increase of the synaptic weight when time correlation between pre- and post-neuron firing is experienced at the synaptic connection. We interpreted our results in the framework of a phenomenological model developed for biological synapses. Finally, we exploit this model to investigate how spike-based systems can be realized for memory and computing applications. These results pave the way for future engineering of neuromorphic computing systems, where complex behaviors of memristive physics can be exploited

Atomic switch : synaptic functionalities and integration strategies

In recent years, research in the field of neuro-inspired computing has generated a lot of interest and seems to be a promising candidate to complement and to provide enhanced performances and new functionalities to the existing CMOS/Von Neumann processor, such as image recognition or datas classification. Engineering of memristive nano devices with specific functionalities for the implementation of synaptic operation and their integration into high density parallel network are still two big challenges for the development of neuromorphic hardware. We focus on a particular class of CB-RAM, the atomic switch that is composed by an ionic conductor material, the Ag2S, sandwiched between two metal electrodes (Ag and Pt). We characterize the tunable device volatility due to the formation/disruption of silver filaments into the ionic-conductor material under applied bias leading to short-term memory (STM) and long-term memory (LTM). Based on this simple two terminal synaptic device, we then investigate alternative route toward their integration into crossbar network. We propose a bottom-up approach of self-assembly of nanowires, an easily and faster solution with respect to the conventional e-beam lithography. Different techniques are used from surface functionalization by self-assembled monolayer to dielectrophoresis techniques in order to control the organization of the nanowires network

Narrow Heater Bottom Electrode-Based Phase Change Memory as a Bidirectional Artificial Synapse

International audiencePhase change memory can provide a remarkable artificial synapse for neuromorphic systems, as it features excellent reliability and can be used as an analog memory. However, this approach is complicated by the fact that crystallization and amorphization differ radically: crystallization can be realized in a very gradual manner, very similarly to synaptic potentiation, while the amorphization process tends to be abrupt, unlike synaptic depression. Addressing this non-biorealism of amorphization requires system-level solutions that have considerable energy cost or limit the generality of the approach. This work demonstrates experimentally that an adaptation of the memory structure associated with an initialization electrical pulse followed by a sequence of identical fast pulses can overcome this challenge. A single device can then naturally implement gradual long-term potentiation and depression, much like synapses in biology. This study evidences through statistical measurements the reproducibility of the approach, discusses its physical origin, as well as the importance of the device architecture and of the initial electrical pulse. Through the use of system-level simulation, it is shown that this device is especially adapted to a neuroscience-inspired learning. These results highlight how nanodevices can be suitable for bioinspired applications while retaining the qualities of industrial technology

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