3 research outputs found
Memcapacitors
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 caracterÃsticas 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 crÃticas 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
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