3 research outputs found

    Memcapacitors

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    Mestrado em Engenharia Eletrónica e TelecomunicaçõesThe present work aims to continue the study of memory devices, initiated with the prediction of the existence of memristors by Leon Chua in 1971, with the study and characterization of memcapacitors as a semiconductor two-terminal device, characterized by the non-linear relation between charge and voltage, which also present the ability to remember the voltage or charge that passes through the device, graphically represented by a graphic with hysteresis characteristics, also presenting a variable capacitance in function of the charge applied in its terminals. Here, a characterizationof the response functions to a sinusoidal periodic input with variable frequency to three mathematical models of memcapacitive systems is performed: given a memcapacitor in series with an ac input voltage source, the respective hysteresis charge-voltage plots are studied by simulations in the MATLAB environment. Next, a classification of the hysteresis plots in function of its geometry is performed, given that the crossing of such graph in the (0.0) point defines it as a type I or type II hysteresis loop. The analysis continues with the morphological identification of the area of the hysteresis curve of the first model, by varying amplitude and frequency of the input source, in such a way to compare the other models with the ideal one, as well as to take the critical frequencis from which the memcapacitance becomes constant, and thus the system becomes linear, by making the hysteresis curve to become a straight line. The area of the first model was taken by calculations with the Green theorem.O presente trabalho propõe-se a continuar o estudo dos dispositivos de memória, iniciado com a predição dos memristors por Leon Chua em 1971, por meio do estudo e caracterização dos memcapacitores como dispositivos semicondutores de dois terminais, caracterizados pela relação não linear entre carga e tensão, que apresentam capacidade de recordar a tensão ou corrente que passa pelo dispositivo, graficamente representado em forma de um gráfico com características de histerese, aprensentando também capacitância variável em função da carga aplicada em seus terminais. Aqui, uma caracterização das funções de resposta a uma entrada periódica sinusoidal com frequência variável, para três modelos matemáticos de sistemas memcapacitivos, é realizada: dado um memcapacitor em série com uma tensão de entrada ac, estuda-se as respectivas funções de histerese carga-tensão por meio de simulação em MATLAB. Em seguida, é realizada uma classificação das curvas de histerese em função da sua geometria, em que a passagem do gráfico no ponto (0,0), de origem dos planos, o define como tipo I ou tipo II. A análise prossegue com a identificação morfológica da área das curvas de histerese obtidas dos primeiro modelo teóricos em causa, variando-se, para isso, amplitude e frequência de entradas, de modo a se comparar os outros dois modelos restantes com este modelo ideal, ao mesmo tempo em que se deseja obter as frequências críticas de cada modelo, ou seja, as frequências e amplitudes a partir das quais a memcapacitância torna-se constante, e o sistema em causa, linear, fazendo então a curva de histerese degenerar para uma reta. A área do primeiro modelo foi calculada através de um algoritmo que calcula a área da curva por meio do Teorema de Green

    Reservoir Computing in Materio

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    Reservoir Computing first emerged as an efficient mechanism for training recurrent neural networks and later evolved into a general theoretical model for dynamical systems. By applying only a simple training mechanism many physical systems have become exploitable unconventional computers. However, at present, many of these systems require careful selection and tuning by hand to produce usable or optimal reservoir computers. In this thesis we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from complex material substrates. We argue that many physical substrates, even systems that in their natural state might not form usable or "good" reservoirs, can be configured into working reservoirs given some stimulation. To achieve this we apply techniques from evolution in materio whereby configuration is through evolved input-output signal mappings and targeted stimuli. In preliminary experiments the combined model and configuration method is applied to carbon nanotube/polymer composites. The results show substrates can be configured and trained as reservoir computers of varying quality. It is shown that applying the reservoir model adds greater functionality and programmability to physical substrates, without sacrificing performance. Next, the weaknesses of the technique are addressed, with the creation of new high input-output hardware system and an alternative multi-substrate framework. Lastly, a substantial effort is put into characterising the quality of a substrate for reservoir computing, i.e its ability to realise many reservoirs. From this, a methodological framework is devised. Using the framework, radically different computing substrates are compared and assessed, something previously not possible. As a result, a new understanding of the relationships between substrate, tasks and properties is possible, outlining the way for future exploration and optimisation of new computing substrates
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