Abstract
In this paper, the problem of modeling memristors is studied. Two types of memristors with carbon and tungsten doping fabricated by the Knowm Inc. are tested. The memristors have been examined with either sinusoidal or triangle voltage wave periodic excitation. Some different frequencies, amplitudes and signal shapes have been applied. The collected data have been averaged and subjected to high frequency filtering. The quality of measurement data has also been discussed. The averaged measurement has been modeled using three popular memristor models: Strukov, Biolek and VTEAM. Some additional feathers to the considered models have been proposed and tested. Memristor is usually modeled by a set of algebraic-differential equations which link both electrical values (i.e., voltage and current) and the internal variable(s) responsible for the element dynamics. The interior-point with box constrains optimization method has been used to obtain the optimal parameters of the memristor model that fit best to the collected data. The results of the optimization process have been discussed and compared. The sensitivity to the different frequency range has been also examined and reviewed. Some conclusions and future work ideas have been postulated.
Highlights
Memristor devices are an emerging topic that have attracted many researchers to study the possibility of using them in applications including in-memory computations, neuromorphic computing and as non-volatile memory elements
Note that the transition is more stable in case of switching to low resistance state (LRS)
The main goal of this work was to check the performance of the memristor models
Summary
Memristor devices are an emerging topic that have attracted many researchers to study the possibility of using them in applications including in-memory computations, neuromorphic computing and as non-volatile memory elements. This attraction has boosted up right after the discovery of the real memristors in May 2008 by a team of scientists from HP Labs, under the leadership of R. The in-memory computation concept is very interesting considering the problem well known as the von Neumann bottleneck, i.e., the restrictive limit of data bandwidth between the CPU and RAM [3]. The bio-inspired or even brain-inspired computation concept seems to be very attractive for the solution of many complex tasks with higher efficiency contemporary computer systems [6,7]
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