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Probabilistic spin logic (PSL) has recently been proposed as a novel computing paradigm that leverages random thermal fluctuations of interacting bodies in a system rather than deterministic switching of binary bits. A PSL circuit is an interconnected network of thermally unstable units called probabilistic bits (p-bits), whose output randomly fluctuates between bits 0 and 1. While the fluctuations generated by p-bits are thermally driven, and therefore, inherently stochastic, the output probability is tunable with an external source. Therefore, information is encoded through probabilities of various configuration of states in the network. Recent studies have shown that these systems can efficiently solve various types of combinatorial optimization problems and Bayesian inference problems that modern computers are unfit for. Previous experimental studies have demonstrated that a single magnetic tunnel junctions (MTJ) designed to be thermally unstable can operate tunable random number generator making it an ideal hardware solution for p-bits. Most proposals for designing an MTJ to operate as a p-bit involve patterning the MTJ as a circular nano-pillar to make the device thermally unstable and then use spin transfer torque (STT) as a tuning mechanism. However, the practical realization of such devices is very challenging since the fluctuation rate of these devices are very sensitive to any device variations or defects caused during fabrication. Despite this challenge, MTJs are still the most promising hardware solution for p-bits because MTJs are very unique in that they can be tuned by multiple other mechanisms such spin orbit torque, magneto-electric coupling, and voltage-controlled exchange coupling. Furthermore, multiple forces can be used simultaneously to drive stochastic switching signals in MTJs. This means there are a large number of methods to tune, or termed as bias, MTJs that can be implemented in p-bit circuits that can alleviate the current challenges of conventional STT driven p-bits. This article serves as a review of all of the different methods that have been proposed to drive random fluctuations in MTJs to operate as a probabilistic bit. Not only will we review the single-biasing mechanisms, but we will also review all the proposed dual-biasing methods, where two independent mechanisms are employed simultaneously. These dual-biasing methods have been shown to have certain advantages such as alleviating the negative effects of device variations and some biasing combinations have a unique capability called ‘two-degrees of tunability’, which increases the information capacity in the signals generated. 

Superparamagnetic tunnel junctions (sMTJs) are emerging as promising components for stochastic units in neuromorphic computing owing to their tunable random switching behavior. Conventional MTJ control methods, such as spin-transfer torque (STT) and spin–orbit torque (SOT), often require substantial power. Here, we introduce the voltage-controlled exchange coupling (VCEC) mechanism, enabling the switching between antiparallel and parallel states in sMTJs with an ultralow power consumption of only 40 nW, approximately 2 orders of magnitude lower than conventional STT-based sMTJs. This mechanism yields a sigmoid-shaped output response, making it ideally suited to neuromorphic computing applications. Furthermore, we validate the feasibility of integrating VCEC with SOT current control, offering an additional dimension for magnetic state manipulation. This work marks the first practical demonstration of the VCEC effect in sMTJs, highlighting its potential as a low-power control solution for probabilistic bits in advanced computing systems. 

Crystal symmetry plays an important role in the Hall effects. Unconventional spin Hall effect (USHE), characterized by Dresselhaus and out-of-plane spins, has been observed in materials with low crystal symmetry. Recently, antisymmetric planar Hall effect (APHE) was discovered in rutile RuO2 and IrO2 (101) thin films, which also exhibit low crystal symmetry. In this study, we report the observation of both USHE and APHE in IrO2 (111) films, using spin-torque ferromagnetic resonance and harmonic Hall measurements, respectively. Notably, the unconventional spin-torque efficiency from Dresselhaus spin was more than double that of a previous report. Additionally, the temperature dependence of APHE suggests that it arises from the Lorentz force, constrained by crystal symmetry. Symmetry analysis supports the coexistence of USHE and APHE and demonstrates that both originate from the crystal symmetry of IrO2 (111), paving the way for a deeper understanding of Hall effects and related physical phenomena. 

The use of magnetic tunnel junction (MTJ)-based devices constitutes an important basis of modern spintronics. However, the switching layer of an MTJ is widely believed to be an unmodifiable setup, instead of a user-defined option, posing a restriction to the function of spintronic devices. In this study, we realized a reliable electrical control of the switching layer in perpendicular MTJs with 0.1 nm Ir dusting. Specifically, a voltage pulse with a higher amplitude drives the magnetization switching of the MTJ's bottom electrode, while a lower voltage amplitude switches its top electrode. We discussed the origin of this controllability and excluded the possibility of back-hopping. Given the established studies on enhancing the voltage-controlled magnetic anisotropy effect by adopting Ir, we attribute this switching behavior to the significant diffusion of Ir atoms into the top electrode, which is supported by scanning transmission electron microscopy with atomic resolution. 

Abstract Voltage‐Gated Spin‐Orbit‐Torque (VGSOT) Magnetic Random‐Access Memory (MRAM) is a promising candidate for reducing writing energy and improving writing speed in emerging memory and in‐memory computing applications. However, conventional Voltage Controlled Magnetic Anisotropy (VCMA) approaches are often inefficient due to the low VCMA coefficient at the CoFeB/MgO interface. Additionally, traditional heavy metal/perpendicular magnetic anisotropy (PMA) ferromagnet bilayers require an external magnetic field to overcome symmetry constraints and achieve deterministic SOT switching. Here, a novel and industry‐compatible SOT underlayer for next‐generation VGSOT MRAM by employing a composite heavy metal tri‐layer with a high work function is presented. This approach achieves a VCMA coefficient exceeding 100 fJ V⁻¹m⁻¹through electron depletion effects, which is ten times larger than that observed with a pure W underlayer. Furthermore, it is demonstrated that this composite heavy metal SOT underlayer facilitates the integration of VCMA with opposite spin Hall angles, enabling field‐free SOT switching in industry‐compatible PMA CoFeB/MgO systems. 

With the dramatic growth of data in both amount and scale, distributed machine learning has become an important tool for the massive data to finish the tasks as prediction, classification, etc. However, due to the practical physical constraints and the potential privacy leakage of data, it is infeasible to aggregate raw data from all data owners or the learning purpose. To tackle this problem, the distributed privacy-preserving learning approaches are introduced to learn over all distributed data without exposing the real information. However, existing approaches have limits on the complicated distributed system. On the one hand, traditional privacy-preserving learning approaches rely on heavy cryptographic primitives on training data, in which the learning speed is dramatically slowed down due to the computation overheads. On the other hand, the complicated system architecture becomes a barrier in the practical distributed system. In this paper, we propose an efficient privacy-preserving machine learning scheme for hierarchical distributed systems. We modify and improve the collaborative learning algorithm. The proposed scheme not only reduces the overhead for the learning process but also provides the comprehensive protection for each layer of the hierarchical distributed system. In addition, based on the analysis of the collaborative convergency in different learning groups, we also propose an asynchronous strategy to further improve the learning efficiency of hierarchical distributed system. At the last, extensive experiments on real-world data are implemented to evaluate the privacy, efficacy, and efficiency of our proposed schemes.