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Topologically protected spin textures, such as magnetic skyrmions, have thepotential for dense data storage as well as energy-efficient computing due totheir small size and a low driving current. The evaluation of the writing andreading of the skyrmion's magnetic and electrical characteristics is a key steptoward the implementation of these devices. In this paper, we present themagnetic heterostructure Hall bar device and study the anomalous Hall andtopological Hall signals in the device. Using the combination of differentmeasurements like magnetometry at different temperatures, Hall effectmeasurement from 2K to 300K, and magnetic force microscopy imaging, weinvestigate the magnetic and electrical characteristics of the magneticstructure. We measure the skyrmion topological resistivity at differenttemperatures as a function of the magnetic field. The topological resistivityis maximum around the zero magnetic field and it decreases to zero at thesaturating field. This is further supported by MFM imaging. Interestingly theresistivity decreases linearly with the field, matching the behavior observedin the corresponding micromagnetic simulations. We combine the experimentalresults with micromagnetic simulations, thus propose a skyrmion-based synapticdevice and show spin-orbit torque-controlled potentiation/depression in thedevice. The device performance as the synapse for neuromorphic computing isfurther evaluated in a convolutional neural network CNN. The neural network istrained and tested on the MNIST data set we show devices acting as synapsesachieving a recognition accuracy close to 90%, on par with the idealsoftware-based weights which offer an accuracy of 92%.

Anomalous and Topological Hall Resistivity in Ta/CoFeB/MgO Magnetic Systems for Neuromorphic Computing Applications