Mark D. Stiles

We present a theoretical investigation of epitaxial strain effects on the magnetic fluctuation properties of Mn₃Sn noncollinear antiferromagnets. Employing density functional theory (DFT), we uncover significant strain-induced modifications to key magnetic parameters, including magnetic anisotropy and both bilinear and biquadratic exchange interactions. Our findings reveal that the biquadratic exchange, often neglected, plays a crucial role in defining the magnetic energy landscape and its response to strain. These microscopic changes directly impact the energy barriers governing magnetic switching, thereby influencing thermal stability and fluctuation rates. Using macrospin-based simulations based on DFT-derived parameters, we provide a quantitative analysis of the macroscopic magnetic fluctuations influenced by these microscopic interactions. These insights are particularly relevant for applications requiring precisely controlled magnetic behavior, such as hardware for probabilistic computing.

We present a theoretical investigation of epitaxial strain effects on the magnetic fluctuation properties of Mn$_3$Sn noncollinear antiferromagnets. Employing density functional theory (DFT), we uncover significant strain-induced modifications to key magnetic parameters, including magnetic anisotropy and both bilinear and biquadratic exchange interactions. Our findings reveal that the biquadratic exchange, often neglected, plays a crucial role in defining the magnetic energy landscape and its response to strain. These microscopic changes directly impact the energy barriers governing magnetic switching, thereby influencing thermal stability and fluctuation rates. Using macrospin-based simulations based on DFT-derived parameters, we provide a quantitative analysis of the macroscopic magnetic fluctuations influenced by these microscopic interactions. These insights are particularly relevant for applications requiring precisely controlled magnetic behavior, such as hardware for probabilistic computing.

Orbital angular momentum is a central quantity in the burgeoning field of orbitronics. Researchers have interpreted optical measurements of current-driven accumulation versus film thickness as determining the depth dependence of orbital angular momentum accumulation. The authors compute here both the current-induced orbital angular momentum spatial profile at the surface of a Pt thin film and the film-thickness dependence of the circular dichroism, and show that the latter is set by the mean free path while the former is considerably shorter.

Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing qualitatively accurate analog dynamics of these devices will be important as the technology scales up. Here we present results using a one-dimensional overdamped Langevin equation that captures statistical properties of measured time traces, including voltage histograms, drift and diffusion characteristics as measured with Kramers-Moyal coefficients, and dwell-time distributions. While common macrospin models are more physically motivated magnetic models than the Langevin model, we show that for the device measured here, they capture even fewer of the measured experimental behaviors.

The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann bottleneck by performing computation in or near memory. However, an inevitability of transferring neural networks onto hardware is the fact that nonidealities, such as device-to-device variations or poor device yield impact performance. Methods, such as hardware-aware training, where substrate nonidealities are incorporated during network training, are one way to recover performance at the cost of solution generality. In this work, we demonstrate inference on hardware-based neural networks consisting of 20 000 magnetic tunnel junction (MTJ) arrays integrated on CMOS chips in a form that closely resembles scalable and market-ready spin transfer-torque magnetoresistive random access memory (STT-MRAM) technology. Using 36 dies, each containing a MTJ-CMOS crossbar array with its own nonidealities, we show that even a small number of defects in physically mapped networks significantly degrades the performance of networks trained without defects and show that, at the cost of generality, hardware-aware training accounting for specific defects on each die can recover to comparable performance with ideal networks. We then demonstrate a robust training method that extends hardware-aware training to statistics-aware training, producing network weights that perform well on most defective dies regardless of their specific defect locations. When evaluated on the 36 physical dies, statistics-aware trained solutions can achieve a mean misclassification error on the MNIST dataset that differs from the software-baseline by only 2%. This statistics-aware training method could be generalized to networks with many layers that are mapped to hardware suited for industry-ready applications.