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In advancing the study of magnetization dynamics in STT-MRAM devices, we employ the spin drift–diffusion model to address the back-hopping effect. This issue manifests as unwanted switching either in the composite free layer or in the reference layer in synthetic antiferromagnets—a challenge that becomes more pronounced with device miniaturization. Although this miniaturization aims to enhance memory density, it inadvertently compromises data integrity. Parallel to this examination, our investigation of the interface exchange coupling within multilayer structures unveils critical insights into the efficacy and dependability of spintronic devices. We particularly scrutinize how exchange coupling, mediated by non-magnetic layers, influences the magnetic interplay between adjacent ferromagnetic layers, thereby affecting their magnetic stability and domain wall movements. This investigation is crucial for understanding the switching behavior in multi-layered structures. Our integrated methodology, which uses both charge and spin currents, demonstrates a comprehensive understanding of MRAM dynamics. It emphasizes the strategic optimization of exchange coupling to improve the performance of multi-layered spintronic devices. Such enhancements are anticipated to encourage improvements in data retention and the write/read speeds of memory devices. This research, thus, marks a significant leap forward in the refinement of high-capacity, high-performance memory technologies.

Abstract Designing advanced single-digit shape-anisotropy MRAM cells requires an accurate evaluation of spin currents and torques in magnetic tunnel junctions (MTJs) with elongated free and reference layers. For this purpose, we extended the analysis approach successfully used in nanoscale metallic spin valves to MTJs by introducing proper boundary conditions for the spin currents at the tunnel barrier interfaces, and by employing a conductivity locally dependent on the angle between the magnetization vectors for the charge current. The experimentally measured voltage and angle dependencies of the torques acting on the free layer are thereby accurately reproduced. The switching behavior of ultra-scaled MRAM cells is in agreement with recent experiments on shape-anisotropy MTJs. Using our extended approach is absolutely essential to accurately capture the interplay of the Slonczewski and Zhang-Li torque contributions acting on a textured magnetization in composite free layers with the inclusion of several MgO barriers.

Although the miniaturization of metal–oxide–semiconductor field effect transistors (MOSFETs)—the main driver behind an outstanding increase in the speed, performance, density, and complexity of modern integrated circuits—is continuing, numerous outstanding technological challenges in complimentary metal–oxide–semiconductor (CMOS) device miniaturization are slowly bringing the downscaling to saturation [...]

We employ a fully three-dimensional model coupling magnetization, charge, spin, and temperature dynamics to study temperature effects in spin-orbit torque (SOT) magnetoresistive random access memory (MRAM). SOTs are included by considering spin currents generated through the spin Hall effect. We scale the magnetization parameters with the temperature. Numerical experiments show several time scales for temperature dynamics. The relatively slow temperature increase, after a rapid initial temperature rise, introduces an incubation time to the switching. Such a behavior cannot be reproduced with a constant temperature model. Furthermore, the critical SOT switching voltage is significantly reduced by the increased temperature. We demonstrate this phenomenon for switching of field-free SOT-MRAM. In addition, with an external-field-assisted switching, the critical SOT voltage shows a parabolic decrease with respect to the voltage applied across the magnetic tunnel junction (MTJ) of the SOT-MRAM cell, in agreement with recent experimental data.

Because of their nonvolatile nature and simple structure, the interest in MRAM devices has been steadily growing in recent years. Reliable simulation tools, capable of handling complex geometries composed of multiple materials, provide valuable help in improving the design of MRAM cells. In this work, we describe a solver based on the finite element implementation of the Landau–Lifshitz–Gilbert equation coupled to the spin and charge drift-diffusion formalism. The torque acting in all layers from different contributions is computed from a unified expression. In consequence of the versatility of the finite element implementation, the solver is applied to switching simulations of recently proposed structures based on spin-transfer torque, with a double reference layer or an elongated and composite free layer, and of a structure combining spin-transfer and spin-orbit torques.

As scaling of the feature size - the main driving force behind an outstanding increase of the performance of modern electronic circuits - displays signs of saturation, the main focus of engineering research in microelectronics shifts towards finding new paradigms. Any future solution must be scalable and energy efficient while delivering high performance, superior to that of CMOS-based circuits. In order to benefit from the outstanding potential of highly advanced silicon processing technology, any new solution must be CMOS compatible. Emerging nonvolatile memories, including magnetoresistive memories, satisfy the necessary requirements: purely electrical addressability, simple structure, high endurance and fast operation. In this work we present the recent developments in the research of spin-transfer torque and spin-orbit torque random access memories and give a brief overview of spin-based logic. Here, the advantages and challenges of these two main contenders in the magnetic memory field are described and the current technological trends are noted. Areas facing computational challenges due to the long-range interaction of the demagnetizing field are highlighted and an existing solution is presented. The use of reinforcement learning to optimize handling of a purely electrically controllable spin-orbit torque memory cell is introduced and first results showing the switching reliability of an optimized switching pulse sequence under thermal fluctuations are reported.

As scaling of semiconductor devices displays signs of saturation, the focus of research in microelectronics shifts towards finding new computing paradigms based on novel physical principles [...]

The steady increase in performance and speed of modern integrated circuits is continuously supported by constant miniaturization of complementary metal-oxide semiconductor (CMOS) devices. However, a rapid growth of the dynamic and stand-by power due to transistor leakages becomes a pressing issue. A promising way to stop this trend is to introduce non-volatility. The development of an electrically addressable non-volatile memory combining high speed and high endurance is essential to achieve these goals. It is particularly promising to employ non-volatility in the main computer memory as a replacement of conventional volatile CMOS-based DRAM. To further reduce the energy consumption, it is essential to replace caches (SRAM) in modern hierarchical multi-level processor memory structures with a non-volatile memory technology. The spin-orbit torque magnetic random access memory (SOT-MRAM) combines non-volatility, high speed, high endurance, and is thus suitable for applications in caches. However, its development is still impeded by the necessity of a static in-plane magnetic field. We propose a magnetic field-free perpendicular SOT-MRAM, based on a cross-bar architecture and the use of two consecutive orthogonal sub-nanosecond current pulses. In this way small layout footprint and high integration density are guaranteed.

Spin-orbit torque memory is a suitable candidate for next generation nonvolatile magnetoresistive random access memory. It combines high-speed operation with excellent endurance, being particularly promising for application in caches. In this work, a two-current pulse magnetic field-free spin-orbit torque switching scheme is combined with reinforcement learning in order to determine current pulse parameters leading to the fastest magnetization switching for the scheme. Based on micromagnetic simulations, it is shown that the switching probability strongly depends on the configuration of the current pulses for cell operation with sub-nanosecond timing. We demonstrate that the implemented reinforcement learning setup is able to determine an optimal pulse configuration to achieve a switching time in the order of 150 ps, which is 50% shorter than the time obtained with non-optimized pulse parameters. Reinforcement learning is a promising tool to automate and further optimize the switching characteristics of the two-pulse scheme. An analysis of the impact of material parameter variations has shown that deterministic switching can be ensured for all cells within the variation space, provided that the current densities of the applied pulses are properly adjusted.