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.

The multidisciplinary field of memristors calls for the necessity for theoreticallyinclined researchers and experimenters to join forces, merging complementary expertise and technical know-how, to develop and implement rigorous and systematic techniques to design variability-aware memristor-based circuits and systems. The availability of a predictive physics-based model for a memristor is a necessary requirement before commencing these investigations. An interesting dynamic phenomenon, occurring ubiquitously in non-volatile memristors, is fading memory. The latter may be defined as the appearance of a unique steady-state behavior, irrespective of the choice of the initial condition from an admissible range of values, for each stimulus from a certain family, for example, the DC or the purely-AC periodic input class. This paper first provides experimental evidence for the emergence of fading memory effects in the response of a TaOₓ redox-based random access memory cell to inputs from both of these classes. Leveraging the predictive capability of a physics-based device model, called JART VCM v1, a thorough system-theoretic analysis, revolving around the Dynamic Route Map graphic tool, is presented. This analysis allows to gain a better understanding of the mechanisms, underlying the emergence of history erase effects, and to identify the main factors, that modulate this nonlinear phenomenon, toward future potential applications.

<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks. </p> <p> </p> <p>Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.</p> <p> </p> <p>We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver. </p> <p> </p> <p>Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.</p>