Self-current induced spin-orbit torque in FeMn/Pt multilayers

Extensive efforts have been devoted to the study of spin-orbit torque in ferromagnetic metal/heavy metal bilayers and exploitation of it for magnetization switching using an in-plane current. As the spin-orbit torque is inversely proportional to the thickness of the ferromagnetic layer, sizable effect has only been realized in bilayers with an ultrathin ferromagnetic layer. Here we demonstrate that, by stacking ultrathin Pt and FeMn alternately, both ferromagnetic properties and current induced spin-orbit torque can be achieved in FeMn/Pt multilayers without any constraint on its total thickness. The critical behavior of these multilayers follows closely three-dimensional Heisenberg model with a finite Curie temperature distribution. The spin torque effective field is about 4 times larger than that of NiFe/Pt bilayer with a same equivalent NiFe thickness. The self-current generated spin torque is able to switch the magnetization reversibly without the need for an external field or a thick heavy metal layer. The removal of both thickness constraint and necessity of using an adjacent heavy metal layer opens new possibilities for exploiting spin-orbit torque for practical applications.Comment: 28 pages, 5 figure

An overview of current situations of robot industry development

As an industry of emerging technology, robot industry has become one of important signs to evaluate a country’s level in science and technology innovation and high-end manufacturing, and an important strategic field to take the preemptive opportunities in development of intelligent society. Developed countries such as the USA, Germany, France and Japan have formulated their robot R&D strategies and planning in succession. China boasts good industrial foundation and has made encouraging progress in the course of development of robot technology. This paper briefly discusses the application type of robot industry and current situations of robot industry development in countries around the world, and makes detailed explanation of current situations of robot industry development in China

Anomalous Hall magnetoresistance in a ferromagnet

The anomalous Hall effect, observed in conducting ferromagnets with broken time-reversal symmetry, offers the possibility to couple spin and orbital degrees of freedom of electrons in ferromagnets. In addition to charge, the anomalous Hall effect also leads to spin accumulation at the surfaces perpendicular to both the current and magnetization direction. Here we experimentally demonstrate that the spin accumulation, subsequent spin backflow, and spin-charge conversion can give rise to a different type of spin current related magnetoresistance, dubbed here as the anomalous Hall magnetoresistance, which has the same angular dependence as the recently discovered spin Hall magnetoresistance. The anomalous Hall magnetoresistance is observed in four types of samples: co-sputtered (Fe1-xMnx)0.6Pt0.4, Fe1-xMnx and Pt multilayer, Fe1-xMnx with x = 0.17 to 0.65 and Fe, and analyzed using the drift-diffusion model. Our results provide an alternative route to study charge-spin conversion in ferromagnets and to exploit it for potential spintronic applications

Optimal prediction of Markov chains with and without spectral gap

We study the following learning problem with dependent data: Observing a trajectory of length $n$ from a stationary Markov chain with $k$ states, the goal is to predict the next state. For $3 \leq k \leq O(\sqrt{n})$, using techniques from universal compression, the optimal prediction risk in Kullback-Leibler divergence is shown to be $\Theta(\frac{k^2}{n}\log \frac{n}{k^2})$, in contrast to the optimal rate of $\Theta(\frac{\log \log n}{n})$ for $k=2$ previously shown in Falahatgar et al., 2016. These rates, slower than the parametric rate of $O(\frac{k^2}{n})$, can be attributed to the memory in the data, as the spectral gap of the Markov chain can be arbitrarily small. To quantify the memory effect, we study irreducible reversible chains with a prescribed spectral gap. In addition to characterizing the optimal prediction risk for two states, we show that, as long as the spectral gap is not excessively small, the prediction risk in the Markov model is $O(\frac{k^2}{n})$, which coincides with that of an iid model with the same number of parameters.Comment: 52 page