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Abstract All-electrical driven magnetization switching attracts much attention in next-generation spintronic memory and logic devices, particularly in magnetic random-access memory (MRAM) based on the spin–orbit torque (SOT), i.e. SOT-MRAM, due to its advantages of low power consumption, fast write/read speed, and improved endurance, etc. For conventional SOT-driven switching of the magnet with perpendicular magnetic anisotropy, an external assisted magnetic field is necessary to break the inversion symmetry of the magnet, which not only induces the additional power consumption but also makes the circuit more complicated. Over the last decade, significant effort has been devoted to field-free magnetization manipulation by using SOT. In this review, we introduce the basic concepts of SOT. After that, we mainly focus on several approaches to realize the field-free deterministic SOT switching of the perpendicular magnet. The mechanisms mainly include mirror symmetry breaking, chiral symmetry breaking, exchange bias, and interlayer exchange coupling. Furthermore, we show the recent progress in the study of SOT with unconventional origin and symmetry. The final section is devoted to the industrial-level approach for potential applications of field-free SOT switching in SOT-MRAM technology. 

Abstract Giant spin-orbit torque (SOT) from topological insulators (TIs) provides an energy efficient writing method for magnetic memory, which, however, is still premature for practical applications due to the challenge of the integration with magnetic tunnel junctions (MTJs). Here, we demonstrate a functional TI-MTJ device that could become the core element of the future energy-efficient spintronic devices, such as SOT-based magnetic random-access memory (SOT-MRAM). The state-of-the-art tunneling magnetoresistance (TMR) ratio of 102% and the ultralow switching current density of 1.2 × 10⁵ A cm⁻²have been simultaneously achieved in the TI-MTJ device at room temperature, laying down the foundation for TI-driven SOT-MRAM. The charge-spin conversion efficiencyθ_SHin TIs is quantified by both the SOT-induced shift of the magnetic switching field (θ_SH = 1.59) and the SOT-induced ferromagnetic resonance (ST-FMR) (θ_SH = 1.02), which is one order of magnitude larger than that in conventional heavy metals. These results inspire a revolution of SOT-MRAM from classical to quantum materials, with great potential to further reduce the energy consumption. 

Abstract MotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Availability and implementationJava source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary informationSupplementary data are available at Bioinformatics online.