Related papers: Smart Material Implication Using Spin-Transfer Tor…

Probabilistic spin logic (PSL), based on networks of binary stochastic neurons (or p-bits), has been shown to provide a viable framework for many functionalities including Ising computing, Bayesian inference, invertible Boolean logic and…

Graphical probabilistic circuit models of stochastic computing are more powerful than the predominant deep learning models, but also have more demanding requirements. For example, they require "programmable stochasticity", e.g. generating…

Altermagnets with nonrelativistic momentum-dependent spin splitting and compensated net magnetic moments have recently garnered significant interest in spintronics, particularly as pinning layers in magnetic tunnel junctions (MTJs).…

The high current density required by Magnetic Tunneling Junction (MTJ) switching driven by Spin Transfer Torque (STT) effect leads to large power consumption and severe reliability issues therefore hinder the timetable for STT Magnetic…

We propose an innovative, learnable two-sided short-time Laplace transform (STLT) mechanism to supplant the traditional self attention in transformer-based LLMs. Our STLT introduces trainable parameters for each Laplace node, enabling…

Spin Transfer Torque Random Access Memory (STT-RAM) is an emerging Non-Volatile Memory (NVM) technology that has garnered attention to overcome the drawbacks of conventional CMOS-based technologies. However, such technologies must be…

Various constraints of Static Random Access Memory (SRAM) are leading to consider new memory technologies as candidates for building on-chip shared last-level caches (SLLCs). Spin-Transfer Torque RAM (STT-RAM) is currently postulated as the…

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring…

A new proposal is given for designing a non-volatile, completely spin logic device, that can be reprogrammed for different functional classical logical operations. We use the concept of bias driven spin dependent circular current and…

Voltage-gate assisted spin-orbit torque (VGSOT) writing scheme combines the advantages from voltage control of magnetic anisotropy (VCMA) and spin-orbit torque (SOT) effects, enabling multiple benefits for magnetic random access memory…

Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to improve their internal reasoning ability by integrating external tools. However, models employing TIR often display suboptimal behaviors, such as insufficient or…

Data-driven model interpretability is a requirement to gain the acceptance of process engineers to rely on the prediction of a data-driven model to regulate industrial processes in the ironmaking industry. In the research presented in this…

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system…

Delocalized carbon-based radical species with unpaired spin, such as phenalenyl (PLY) radical, opened avenues for developing multifunctional organic spintronic devices. Here we develop a novel technique based on a three-dimensional shadow…

The figures-of-merit for reservoir computing (RC), using spintronics devices called magnetic tunnel junctions (MTJs), are evaluated. RC is a type of recurrent neural network. The input information is stored in certain parts of the…

The realistic modeling of STT-MRAM for the simulations of hybrid CMOS/Spintronics devices in comprehensive simulation environments require a full description of stochastic switching processes in state of the art STT-MRAM. Here, we derive an…

The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights. This challenge can be resolved by emulating analog…

Magneto-transport properties in hybrid magnetic tunnel junctions (MTJs) integrating self-assembled monolayers (SAMs) as tunnel barriers are critically influenced by spinterface effects, which arise from the electronic properties at…

Memristive Processing In-Memory (PIM) is one of the promising techniques for overcoming the Von-Neumann bottleneck. Reduction of data transfer between processor and memory and data processing by memristors in data-intensive applications…

We demonstrate the reduction of critical spin-transfer torque (STT) switching currents in Co-Fe-B/MgO based magnetic tunnel junctions (MTJ) with perpendicular magnetization anisotropy (PMA). The junctions yield tunnel magnetoresistance…