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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.
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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 device meets three criteria for an ideal memristor: a single valued, nonlinear, continuously differentiable, and strictly monotonically increasing constitutive phi q curve, a pinched v i hysteresis loop, and a charge only dependent resistance. Our work represents a step forward in terms of experimentally verifying the memristive flux charge interaction but we have not reached the final because this prototype still suffers from two serious limitations: 1, a superficial but dominant inductor effect (behind which the above memristive fingerprints hide) due to its inductor-like core structure, and 2. bistability and dynamic sweep of a continuous resistance range. In this article, we also discuss how to make a fully functioning ideal memristor with multiple or an infinite number of stable states and no parasitic inductance, and give a number of suggestions, such as open structure, nanoscale size, magnetic materials with cubic anisotropy (or even isotropy), and sequential switching of the magnetic domains. Additionally, we respond to a recent challenge from arXiv.org that claims that our device is simply an inductor with memory since our device did not pass their designed capacitor-memristor circuit test. Contrary to their conjecture that an ideal memristor may not exist or may be a purely mathematical concept, we remain optimistic that researchers will discover an ideal memristor in nature or make one in the laboratory based on our current work.
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 provide a scalable solution for embedded memory. We demonstrate a net reduction in critical charge current for spin torque driven magnetization reversal via using spin-orbit mediated spin current generation. We scaled the dimensions of the spin-orbit electrode to 400 nm and the nanomagnet to 270 nm x 68 nm in a three terminal spin-orbit torque, magnetic tunnel junction (SOT-MTJ) geometry. Our estimated effective spin Hall angle is 0.15-0.20 using the ratio of zero temperature critical current from spin Hall switching and estimated spin current density for switching the magnet. We show bidirectional transient switching using spin-orbit generated spin torque at 100 ns switching pulses reliably followed by transient read operations. We finally compare the static and dynamic response of the SOT-MTJ with transient spin circuit modeling showing the performance of scaled SOT-MTJs to enable nanosecond class non-volatile MTJs.
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 has been proposed [J. Phys. D: Appl. Phys. {bf 52}, 01LT01 (2019)] to determine whether a given physical system is indeed a memristor or not. Here, we experimentally apply such a test to both in-house fabricated Cu-SiO2 and commercially available electrochemical metallization cells. Our results unambiguously show that electrochemical metallization memory cells are not memristors. Since the particular resistance-switching memories employed in our study share similar features with many other memory cells, our findings refute the claim that all resistance-switching memories are memristors. They also cast doubts on the existence of ideal memristors as actual physical devices that can be fabricated experimentally. Our results then lead us to formulate two memristor impossibility conjectures regarding the impossibility of building a model of physical resistance-switching memories based on the memristor model.
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 locally-varying spin properties and enable subsurface imaging. Here we report the demonstration of a novel spin-microscopy tool based on the variation of a global spin-precession signal in response to the localized magnetic field of a scanned probe. We map the local spin density in optically pumped GaAs from this spatially-averaged signal with a resolution of 5.5 microns. This methodology is also applicable to other spin properties and its resolution can be improved. It can extend spin microscopy to device structures not accessible by other techniques, such as buried interfaces and non-optically active materials, due to the universal nature of magnetic interactions between the spins and the probe.
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 respond to the sinusoidal voltage source quickly, namely, they can not switch `on rapidly. Furthermore, these memristors have refractory period after switch `on, which means that it can not respond to further sinusoidal inputs until the flux is decreased. We next show that the memristor-coupled two-cell CNN can exhibit chaotic behavior. In this system, the memristors switch `off and `on at irregular intervals, and the two cells are connected when either or both of the memristors switches `on. We then propose the modified CNN model, which can hold a binary output image, even if all cells are disconnected and no signal is supplied to the cell after a certain point of time. However, the modified CNN requires power to maintain the output image, that is, it is volatile. We next propose a new memristor CNN model. It can also hold a binary output state (image), even if all cells are disconnected, and no signal is supplied to the cell, by memristors switching behavior. Furthermore, even if we turn off the power of the system during the computation, it can resume from the previous average output state, since the memristor CNN has functions of both short-term (volatile) memory and long-term (non-volatile) memory. The above suspend and resume feature are useful when we want to save the current state, and continue work later from the previous state. Finally, we show that the memristor CNN can exhibit interesting two-dimensional waves, if an inductor is connected to each memristor CNN cell.
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