In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We develop a novel input and output processing engine for our network and demonstrate the spatio-temporal information processing capability. We demonstrate an accuracy of a 100% with our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations. / M.S. / In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. Artificial Neural Networks (ANNs) are models that mimic biological neurons where artificial neurons or neurodes are connected together via synapses, similar to the nervous system in the human body. here are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing capability. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We demonstrate the accuracy of our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations.

Die Dissertation beschäftigt sich mit der Analyse von oxidischen Nanostrukturen. Die Grundlage der Bauelemente stellt dabei die LaAlO3/SrTiO3-Heterostruktur dar. Hierbei entsteht an der Grenzfläche beider Übergangsmetalloxide ein quasi zweidimensionales Elektronengas, welches wiederum eine Fülle von beachtlichen Eigenschaften und Charakteristika zeigt. Mithilfe lithographischer Verfahren wurden zwei unterschiedliche Bauelemente verwirklicht. Dabei handelt es sich einerseits um einen planaren Nanodraht mit lateralen Gates, welcher auf der Probenoberfläche prozessiert wurde und eine bemerkenswerte Trialität aufweist. Dieses Bauelement kann unter anderem als ein herkömmlicher Feldeffekttransistor agieren, wobei der Ladungstransport durch die lateral angelegte Spannung manipuliert wird. Zusätzlich konnten auch Speichereigenschaften beobachtet werden, sodass das gesamte Bauelement als ein sogenannter Memristor fungieren kann. In diesem Fall hängt der Ladungstransport von der Elektronenakkumulation auf den lateralen potentialfreien Gates ab. Die Memristanz des Nanodrahts lässt sich unter anderem durch Lichtleistungen im Nanowattbereich und mithilfe von kurzen Spannungspulsen verändern. Darüber hinaus kann die Elektronenakkumulation auch in Form einer memkapazitiven Charakteristik beobachtet werden. Neben dem Nanodraht wurde auch eine Kreuzstruktur, die eine ergänzende ferromagnetischen Elektrode beinhaltet, realisiert. Mit diesem neuartigen Bauteil wird die Umwandlung zwischen Spin- und Ladungsströmen innerhalb der nanoskaligen Struktur untersucht. Hierbei wird die starke Spin-Bahn-Kopplung im quasi zweidimensionalen Elektronengas ausgenutzt. / The dissertation focuses on the analysis of oxide nanostructures. The basis of the devices consists of the LaAlO3/SrTiO3 heterostructure. A quasi two-dimensional electron gas is formed at the interface of the two transition metal oxides, which in turn exhibits a plethora of remarkable properties and characteristics. Two different components were realized using lithographic processes. The first is a planar nanowire with lateral gates, which was processed on the sample surface and exhibits remarkable triality. Among other things, this device can act as a conventional field-effect transistor, whereby the charge transport is manipulated by the laterally applied voltage. In addition, storage properties could also be observed, so that the entire component can function as a so-called memristor. In this case, the charge transport depends on the accumulation of electrons on the floating gates. The memristance of the nanowire can be altered using light power in the nanowatt range and with the aid of short voltage pulses. In addition, electron accumulation can also be observed in the form of a memcapacitive characteristic. In addition to the nanowire, a cross structure containing a complementary ferromagnetic electrode was also realized. This novel device is used to investigate the conversion between spin and charge currents within the nanoscale structure. Here, the strong spin-orbit coupling in the quasi two-dimensional electron gas is utilized.

The main focus of this paper is the evolution of complex behavior in a system of coupled nonlinear memristor circuits depending on the applied coupling conditions. Thereby, the parameter space for the local activity and the edge-of-chaos domain will be determined to enable the emergence of the pattern formation in locally coupled cells according to Chua's principle. Each cell includes a Niobium oxide-based memristor, which may feature a locally active behavior once it is suitably biased on the negative differential resistance region of its DC current-voltage characteristic. It will be shown that there exists a domain of parameters under which each uncoupled cell may become locally active around a stable bias state. More specifically, under these conditions, the coupled cells are on the edge-of-chaos, and can support the static and dynamic pattern formation. The emergence of such complex spatio-temporal behavior in homogeneous structures is a prerequisite for information processing. The theoretical results are confirmed by

Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics. Computations for such applications typically rely on spatial and temporal associations in the input patterns and need to be robust against noise and incomplete patterns. The conventional method for implementing AMs is through Artificial Neural Networks (ANNs). Improving the density of ANN based on conventional circuit elements poses a challenge as devices reach their physical scalability limits. Furthermore, stored information in AMs is vulnerable to destructive input signals. Novel nano-scale components, such as memristors, represent one solution to the density problem. Memristors are non-linear time-dependent circuit elements with an inherently small form factor. However, novel neuromorphic circuits typically use memristors to replace synapses in conventional ANN circuits. This sub-optimal use is primarily because there is no established design methodology to exploit the memristor's non-linear properties in a more encompassing way. The objective of this thesis is to explore denser and more robust AM designs using memristor networks. We hypothesize that such network AMs will be more area-efficient than the traditional ANN designs if we can use the memristor's non-linear property for spatial and time-dependent temporal association. We have built a comprehensive simulation framework that employs Genetic Programming (GP) to evolve AM circuits with memristors. The framework is based on the ParadisEO metaheuristics API and uses ngspice for the circuit evaluation. Our results show that we can evolve efficient memristor-based networks that have the potential to replace conventional ANNs used for AMs. We obtained AMs that a) can learn spatial and temporal correlation in the input patterns; b) optimize the trade-off between the size and the accuracy of the circuits; and c) are robust against destructive noise in the inputs. This robustness was achieved at the expense of additional components in the network. We have shown that automated circuit discovery is a promising tool for memristor-based circuits. Future work will focus on evolving circuits that can be used as a building block for more complicated intelligent computing architectures.

Dans cette thèse, nous étudions les applications potentielles des nano-dispositifs mémoires émergents dans les architectures de calcul. Nous montrons que des architectures neuro-inspirées pourraient apporter l'efficacité et l'adaptabilité nécessaires à des applications de traitement et de classification complexes pour la perception visuelle et sonore. Cela, à un cout moindre en termes de consommation énergétique et de surface silicium que les architectures de type Von Neumann, grâce à une utilisation synaptique de ces nano-dispositifs. Ces travaux se focalisent sur les dispositifs dit "memristifs", récemment (ré)-introduits avec la découverte du memristor en 2008 et leur utilisation comme synapse dans des réseaux de neurones impulsionnels. Cela concerne la plupart des technologies mémoire émergentes : mémoire à changement de phase - "Phase-Change Memory" (PCM), "Conductive-Bridging RAM" (CBRAM), mémoire résistive - "Resistive RAM" (RRAM)... Ces dispositifs sont bien adaptés pour l'implémentation d'algorithmes d'apprentissage non supervisés issus des neurosciences, comme "Spike-Timing-Dependent Plasticity" (STDP), ne nécessitant que peu de circuit de contrôle. L'intégration de dispositifs memristifs dans des matrices, ou "crossbar", pourrait en outre permettre d'atteindre l'énorme densité d'intégration nécessaire pour ce type d'implémentation (plusieurs milliers de synapses par neurone), qui reste hors de portée d'une technologie purement en "Complementary Metal Oxide Semiconductor" (CMOS). C'est l'une des raisons majeures pour lesquelles les réseaux de neurones basés sur la technologie CMOS n'ont pas eu le succès escompté dans les années 1990. A cela s'ajoute la relative complexité et inefficacité de l'algorithme d'apprentissage de rétro-propagation du gradient, et ce malgré tous les aspects prometteurs des architectures neuro-inspirées, tels que l'adaptabilité et la tolérance aux fautes. Dans ces travaux, nous proposons des modèles synaptiques de dispositifs memristifs et des méthodologies de simulation pour des architectures les exploitant. Des architectures neuro-inspirées de nouvelle génération sont introduites et simulées pour le traitement de données naturelles. Celles-ci tirent profit des caractéristiques synaptiques des nano-dispositifs memristifs, combinées avec les dernières avancées dans les neurosciences. Nous proposons enfin des implémentations matérielles adaptées pour plusieurs types de dispositifs. Nous évaluons leur potentiel en termes d'intégration, d'efficacité énergétique et également leur tolérance à la variabilité et aux défauts inhérents à l'échelle nano-métrique de ces dispositifs. Ce dernier point est d'une importance capitale, puisqu'il constitue aujourd'hui encore la principale difficulté pour l'intégration de ces technologies émergentes dans des mémoires numériques.

The memristor is a two-terminal semiconductor device that is able to mimic the conductance response of synapses and can be utilized in next-generation computing platforms that will compute similarly to the mammalian brain. The initial memristor implementation is operated by the digital formation and dissolution of a highly conductive filament. However, an analog memristor is necessary to mimic analog synapses in the mammalian brain. To understand the mechanisms of operation and impact of different device designs, analog memristors were fabricated, modeled, and characterized. To realize analog memristors, lithiated transition metal oxides were grown by molecular beam epitaxy, RF sputtering, and liquid phase electro-epitaxy. Analog memristors were modeled using a finite element model simulation and characterized with X-ray absorption spectroscopy, impedance spectroscopy, and other electrical methods. It was shown that lithium movement facilitates analog memristance and nanoscopic ionic-electronic memristors with ion-soluble electrodes can be key enabling devices for brain-inspired computing.

Ce manuscrit présente mes activités de recherche sur la conception et l'utilisation de systèmes analogiques neuromorphiques.

Orientador: Prof. Dr. Gustavo Martini Dalpian / Tese (doutorado) - Universidade Federal do ABC, Programa de Pós-Graduação em Nanociências e Materiais Avançados, 2015. / A propriedade de chaveamento da resitência ou memoristiva é a habilidade de um material de alterar seu estado de resistência elétrica devido a um campo elétrico. O memoristor é um dispositivo de dois terminais com tal propriedade capaz de armazenar informação através de sua resistência, constituído de uma estrutura metal/isolante/metal. Este dispositivo pode revolucionar a indústria de memórias por apresentar tempos de chaveamento rápidos e de retenção longos, assim como altas densidades. Entretanto, seu princípio de funcionamento não é totalmente entendido a nível atômico, logo sua aplicação é impedida. Dois mecanismos são propostos: o mecanismo de difusão-deriva de íons afirma que campos elétricos e gradientes de temperatura formam e dissolvem canais condutores, alterando a resistividade. Por outro lado, modelos eletrônicos consideram o aprisionamento e liberação de cargas como causa da mudança da resistividade. Neste trabalho utilizamos uma abordagem heurística¿cálculos de teoria do funcional da densidade e soluções numéricas¿para entender os processos ocorrendo em escala atômica no interior de dispositivos baseados em TiO2. Os resultados mostram que a dificência em oxigênio neste caso leva à formação de fases TinO2n..1 que apresentam uma banda intermediária, a qual pode se tornar carregada quando propriamente interfaceada. A resolução numérica da equação de Poisson apresenta múltiplas soluções relacionadas a diferentes estados de resistência, estas soluções são usadas em um código de transmissão que fornece curvas teóricas i X V para o memoristor. / The resistive switching or memristive property is the ability of a material to change its electrical resistance due to the application of an electric field. The memristor is a two-terminal device with this property that is capable of storing information as its resistance state, being architectured in a metal/insulator/metal stacking. This device can revolutionize the memory industry by providing fast switching and large retention times as well as high-density capabilities. However, its working principle is not completely understood at an atomic level, thus its application as next-generation resistive memories is hindered. Two mechanisms are proposed: ion drift mechanisms claim that the electric field and temperature gradients inside the device can form and dissolve a conducting filament, changing the electrical resistivity. On the other hand, electronic models consider charge trapping and de-trapping inside the insulator layer as the cause of the resistivity change. In this work we use a heuristic computational approach¿density functional theory calculations and other numerical solutions¿to understand the processes developing at the atomic scale inside TiO2-based devices. Our results show that the oxygen deficiency in this material leads to the formation of a series of phases TinO2n..1 that present an intermediate band which can become charged when properly interfaced. The self-consistent-numerical solver of the Poisson equation shows multiple solutions that are related to the resistance states, and finally the potential is used in a transmission code that results in theoretical i X V curves for the memristor.

abstract: Alternative computation based on neural systems on a nanoscale device are of increasing interest because of the massive parallelism and scalability they provide. Neural based computation systems also offer defect finding and self healing capabilities. Traditional Von Neumann based architectures (which separate the memory and computation units) inherently suffer from the Von Neumann bottleneck whereby the processor is limited by the number of instructions it fetches. The clock driven based Von Neumann computer survived because of technology scaling. However as transistor scaling is slowly coming to an end with channel lengths becoming a few nanometers in length, processor speeds are beginning to saturate. This lead to the development of multi-core systems which process data in parallel, with each core being based on the Von Neumann architecture. The human brain has always been a mystery to scientists. Modern day super computers are outperformed by the human brain in certain computations. The brain occupies far less space and consumes a fraction of the power a super computer does with certain processes such as pattern recognition. Neuromorphic computing aims to mimic biological neural systems on silicon to exploit the massive parallelism that neural systems offer. Neuromorphic systems are event driven systems rather than being clock driven. One of the issues faced by neuromorphic computing was the area occupied by these circuits. With recent developments in the field of nanotechnology, memristive devices on a nanoscale have been developed and show a promising solution. Memristor based synapses can be up to three times smaller than Complementary Metal Oxide Semiconductor (CMOS) based synapses. In this thesis, the Programmable Metallization Cell (a memristive device) is used to prove a learning algorithm known as Spike Time Dependant Plasticity (STDP). This learning algorithm is an extension to Hebb’s learning rule in which the synapses weight can be altered by the relative timing of spikes across it. The synaptic weight with the memristor will be its conductance, and CMOS oscillator based circuits will be used to produce spikes that can modulate the memristor conductance by firing with different phases differences. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Ce manuscrit présente une étude expérimentale de phase isolante non-conventionnelle, l'isolant d'Anderson, induit par le désordre, l'isolant de Mott, induit par les interactions de Coulomb, et les isolants topologiques.Dans une première partie du manuscrit, je décrirais le développement d'une méthode pour étudier la réponse de charge de nanoparticules par Microscopie à Force Electrostatique (EFM). Cette méthode a été appliquée à des nanoparticules de magnétite (Fe3O4), un matériau qui présente une transition métal-isolant, i.e. la transition de Verwey, lors de son refroidissement en dessous d'une température TV~120 K.Dans une seconde partie, ce manuscrit présente une étude détaillée de l'évolution de la densité d'états au travers de la transition métal-isolant entre un isolant de type Anderson-Mott et une phase métallique dans le matériau SrTiO3, et ceci, en fonction de la concentration de dopants, les lacunes d'oxygènes. Nous avons trouvé que dans un dispositif memoresistif de type Au-SrTiO3-Au, la concentration de dopants pouvait être ajustée par migration des lacunes d'oxygènes à l'aide d'un champ. Dans cette jonction tunnel, l'évolution de la densités d'états au travers de la transition métal-isolant peut être étudiée de façon continue. Finalement, dans une troisième partie, le manuscrit présente le développement d'une méthode pour la microfabrication d'anneaux de Aharonov-Bohm avec l'isolant topologique, Bi2Se3, déposée par épitaxie à jet moléculaire. Des résultats préliminaires sur les propriétés de transport quantique de ces dispositifs seront présentés. / This manuscript presents an experimental study of unconventional insulating phases, which are the Anderson insulator, induced by disorder, the Mott insulator, induced by Coulomb interactions, and topological insulators.In a first part of the manuscript, I will describe the development of a method to study the charge response of nanoparticles through Electrostatic Force Microscopy (EFM). This method has been applied to magnetite Fe3O4 nanoparticles, a material that presents a metal-insulator transition, i.e. the Verwey transition, upon cooling the system below a temperature Tv=120K. In a second part, this manuscript presents a detailed study of the evolution of the Density Of States (DOS) across the metal-insulator transition between an Anderson-Mott insulator and a metallic phase in the material SrTiO3 and this, as function of dopant concentration, i.e. oxygen vacancies. We found that in this memristive type device Au-SrTiO3-Au, the dopant concentration could be fine-tuned through electric-field migration of oxygen vacancies. In this tunnel junction device, the evolution of the DOS can be followed continuously across the metal-insulator transition. Finally, in a third part, the manuscript presents the development of a method for the microfabrication of Aharonov-Bohm rings with the topological insulator material, Bi2Se3, grown by molecular beam epitaxy. Preliminary results on the quantum transport properties of these devices will be presented.