Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés

This PhD project takes place in the context of the study of neuromorphic circuits using memristor devices as synapses. The main objective is to evaluate a new class of organic memories developed at LICSEN (CEA Saclay/IRAMIS) and particularly their compatibility with the learning rules and the implementation strategy proposed by the Nanoarchi group at IEF (Univ. Paris-Sud, Orsay). These new memristors are based on the electro-grafting of organic redox complexes thin films to form robust and scalable metal/molecules/metal junctions. In addition to memristor fabrication, this work includes detailed electrical characterization studies (speed, retention property, scalability, robustness, etc.) aiming at, on the one hand, establishing the commutation mechanism in these new memristors and, on the other hand, evaluating their potential as synapses. This work also proposes a preparatory study of a neural-network type mixed-circuit demonstrator combining nano-memristors and conventional electronic (programmability of devices by spikes, fabrication of assemblies of memristors, variability). Moreover the demonstration of the compatibility of such memristors with the STDP (Spike Timing Dependent Plasticity) property and of the learning of a “conditioned reflex” opens the way to future unsupervised learning studies.Cette thèse s'inscrit dans le contexte de l'étude des circuits neuromorphiques utilisant des dispositifs memristifs comme synapses. Son objectif principal est d'évaluer les mérites d'une nouvelle classe de mémoires organiques développées au LICSEN (CEA Saclay/IRAMIS) et, plus particulièrement, leur adéquation avec les propositions d'implémentation et les règles d'apprentissage proposées par l'équipe NanoArchi de l'IEF (Univ. Paris-Sud, Orsay). Les memristors étudiés sont basés sur l'electro-greffage en films minces de complexes organiques redox pour la formation de jonctions métal/molécules/métal robustes et scalables. Outre la fabrication de memristors, le travail inclut d'importants efforts de caractérisation électrique (vitesse, non-volatilité, scalabilité, robustesse, etc.) visant d'une part à étudier les mécanismes de commutation dans ces nouveaux matériaux memristifs organiques, et d'autres part, à évaluer leur potentiel en tant que synapses. Cette thèse présente également une étude préparatoire à la réalisation d'un démonstrateur de circuit mixte de type réseaux de neurones combinant nano-memristors et électronique conventionnelle (programmabilité des dispositifs en mode impulsionnel, réalisation d'assemblées de dispositifs, variabilité). De plus, la démonstration de la compatibilité de ces memristors avec la propriété STDP (Spike Timing Dependent Plasticity) ainsi que de l’apprentissage d’un « réflexe conditionné » ouvrent la voie aux apprentissages non-supervisés

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

International audienceMultiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations

Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits

Cette thèse s'inscrit dans le contexte de l'étude des circuits neuromorphiques utilisant des dispositifs memristifs comme synapses. Son objectif principal est d'évaluer les mérites d'une nouvelle classe de mémoires organiques développées au LICSEN (CEA Saclay/IRAMIS) et, plus particulièrement, leur adéquation avec les propositions d'implémentation et les règles d'apprentissage proposées par l'équipe NanoArchi de l'IEF (Univ. Paris-Sud, Orsay). Les memristors étudiés sont basés sur l'electro-greffage en films minces de complexes organiques redox pour la formation de jonctions métal/molécules/métal robustes et scalables. Outre la fabrication de memristors, le travail inclut d'importants efforts de caractérisation électrique (vitesse, non-volatilité, scalabilité, robustesse, etc.) visant d'une part à étudier les mécanismes de commutation dans ces nouveaux matériaux memristifs organiques, et d'autres part, à évaluer leur potentiel en tant que synapses. Cette thèse présente également une étude préparatoire à la réalisation d'un démonstrateur de circuit mixte de type réseaux de neurones combinant nano-memristors et électronique conventionnelle (programmabilité des dispositifs en mode impulsionnel, réalisation d'assemblées de dispositifs, variabilité). De plus, la démonstration de la compatibilité de ces memristors avec la propriété STDP (Spike Timing Dependent Plasticity) ainsi que de l’apprentissage d’un « réflexe conditionné » ouvrent la voie aux apprentissages non-supervisés.This PhD project takes place in the context of the study of neuromorphic circuits using memristor devices as synapses. The main objective is to evaluate a new class of organic memories developed at LICSEN (CEA Saclay/IRAMIS) and particularly their compatibility with the learning rules and the implementation strategy proposed by the Nanoarchi group at IEF (Univ. Paris-Sud, Orsay). These new memristors are based on the electro-grafting of organic redox complexes thin films to form robust and scalable metal/molecules/metal junctions. In addition to memristor fabrication, this work includes detailed electrical characterization studies (speed, retention property, scalability, robustness, etc.) aiming at, on the one hand, establishing the commutation mechanism in these new memristors and, on the other hand, evaluating their potential as synapses. This work also proposes a preparatory study of a neural-network type mixed-circuit demonstrator combining nano-memristors and conventional electronic (programmability of devices by spikes, fabrication of assemblies of memristors, variability). Moreover the demonstration of the compatibility of such memristors with the STDP (Spike Timing Dependent Plasticity) property and of the learning of a “conditioned reflex” opens the way to future unsupervised learning studies

Hybrid photocathodes for solar hydrogen production : organic photovoltaics as a sensitizer of catalysts for water reduction

International audienc

Electro-grafted organic thin films in Nano-memristor device

International audienc

Graphene Oxide Memristors for Artificial Neural Networks

International audienc

Assembling organic solar cells and catalysts for water reduction: hybrid photocathode for solar hydrogen production

International audienc

Electrografted organic memristors: scaling study yusing carbon nanotube electrodes and prospects for artificial neural networks

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Supervised Learning with Organic Memristor Devices and Prospects for Neural Crossbar Arrays

International audienceThe integration of memristive nanodevices within transistor-based electronic systems offers the potential for computing structures smaller, lower power and cheaper than traditional high-performance systems. Among emerging memristive technologies, a novel device based on organic materials distinguishes itself, in that it can feature several threshold voltages on the same die, and possesses unipolar behavior. In this work, we highlight that these two features can be beneficial for neural network-inspired learning systems. An on-chip supervised learning method for hybrid memristors / CMOS systems — an analogue synaptic array paired with a hybrid learning cell — is extended to the case of this novel organic memristor device. The organic device can be trained with only one pulse per row (two for the entire array) per presentation of input — as compared to four for a bipolar memristor array. The device also works universally- in both the synaptic grid as well as learning cell-paving the way to single die integration. The proposed scheme learns successfully, even while incorporating non-ideal circuit phenomena such as a wide range of parasitic wire resistances and associated sneak paths. These encouraging first results suggest that these multi-threshold, unipolar organic memristive devices are a useful species for inclusion in adaptive next generation electronic system

Multiscaled simulation methodology for neuro-inspired circuits demonstrated with an organic memristor

International audienceOrganic memristors are promising molecular electronic devices for neuro-inspired on-chip learning applications. In this paper, we present a numerically efficient compact model suitable for Fe(bpy)2+3 organic memristors operating according to an intramolecular charge transfer switching mechanism. This compact model, being physics-based and relying on electrical characterizations and parametric extractions performed on test structures, is especially efficient in pulsed mode and describes the conductance variations for both SET and RESET regimes. Using this model, a dynamic multiscale simulation approach has been set-up to extend the model from individual devices to larger model systems that learn progressively through time. To verify the soundness and highlight emergent properties of the organic memristors, instances of the compact model have been simulated within a simple neuromorphic design that co-integrates with CMOS neurons. In addition, a larger supervised learning system using the new compact model is demonstrated. These successful tests suggest our model might be of interest to neuromorphic designers