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A simple and unambiguous test has been recently suggested [J. Phys. D: Applied Physics, 52, 01LT01 (2018)] to check experimentally if a resistor with memory is indeed a memristor, namely a resistor whose resistance depends only on the charge that flows through it, or on the history of the voltage across it. However, although such a test would represent the litmus test for claims about memristors (in the ideal sense), it has yet to be applied widely to actual physical devices. In this paper, we experimentally apply it to a current-carrying wire interacting with a magnetic core, which was recently claimed to be a memristor (so-called `$Phi$ memristor) [J. Appl. Phys. 125, 054504 (2019)]. The results of our experiment demonstrate unambiguously that this `$Phi$ memristor is not a memristor: it is simply an inductor with memory. This demonstration casts further doubts that ideal memristors do actually exist in nature or may be easily created in the lab.
After a decade of research, we developed a prototype device and experimentally demonstrated that the direct phi q interaction could be memristive, as predicted by Chua in 1971. With a constant input current to avoid any parasitic inductor effect, our
Efficient generation of spin currents from charge currents is of high importance for memory and logic applications of spintronics. In particular, generation of spin currents from charge currents in high spin-orbit coupling metals has the potential to
It has been suggested that all resistive-switching memory cells are memristors. The latter are hypothetical, ideal devices whose resistance, as originally formulated, depends only on the net charge that traverses them. Recently, an unambiguous test h
Progress in spintronics has been aided by characterization tools tailored to certain archetypical materials. New device structures and materials will require characterization tools that are material independent, provide sufficient resolution to image
In this paper, we introduce some interesting features of a memristor CNN (Cellular Neural Network). We first show that there is the similarity between the dynamics of memristors and neurons. That is, some kind of flux-controlled memristors can not re