In this paper, a variation-aware simulation framework for hybrid circuits comprising MOS transistors and magnetic tunnel junction (MTJ) devices is presented. The framework is based on one-time characterization via micromagnetic multi-domain simulations, overcoming the inaccuracies introduced by single-domain analysis, which most of the existing frameworks are based on. As further distinctive capability, non-Gaussian stochastic variations of the MTJ write switching time are explicitly modeled through a Skew Normal distribution, making the model suitable for low-power and low-voltage designs. The framework involves the use of a Verilog-A look-up table-based model, which assures easy integration with commercial design tools. Our strategy is evaluated on a spin-transfer torque-magnetoresistive random access memory (MRAM) working in RAM and true random number generator (TRNG) modes. In RAM mode, while the commonly-used approach based on Gaussian-distributed switching statistics tends to underestimate the pulse width required for a write error rate of 10⁻⁶ by about 20%, our Skew Normal-based strategy allows tracking reference micromagnetic results with an average error less than 4%. In TRNG mode, our approach also allows a better tracking of the reference 50%-switching probability contour with an error less than 1%. However, the incorporation of the Skew Normal distribution in our Verilog-A model results in an increase of the CPU time by 50% on average as compared with the use of the built-in statistical functions.

Recently discovered exotic magnetic configurations, namely magnetic solitons appearing in the presence of bulk or interfacial Dzyaloshinskii-Moriya Interaction (i-DMI), have excited scientists to explore their potential applications in emerging spintronic technologies such as race-track magnetic memory, spin logic, radio frequency nano-oscillators and sensors. Such studies are motivated by their foreseeable advantages over conventional micro-magnetic structures due to their small size, topological stability and easy spin-torque driven manipulation with much lower threshold current densities giving way to improved storage capacity, and faster operation with efficient use of energy. In this work, we show that in the presence of i-DMI in Pt/CoFeB/Ti multilayers by tuning the magnetic anisotropy (both in-plane and perpendicular-to-plane) via interface engineering and postproduction treatments, we can stabilize a variety of magnetic configurations such as Néel skyrmions, horseshoes and most importantly, the recently predicted isolated radial vortices at room temperature and under zero bias field. Especially, the radial vortex state with its absolute convergence to or divergence from a single point can potentially offer exciting new applications such as particle trapping/detrapping in addition to magnetoresistive memories with efficient switching, where the radial vortex state can act as a source of spin-polarized current with radial polarization.

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.

The realistic modeling of spin-transfer torque (STT)-magnetoresistive random access memory (MRAM) for the simulations of hybrid CMOS/Spintronics devices in comprehensive simulation environments requires a full description of stochastic switching processes in the state-of-the-art STT-MRAMs. Here, we compare micromagnetic simulations with an analytical formulation that takes into account the spin-torque asymmetry of the spin-polarization function by considering the mean, standard deviation, skewness, and kurtosis. We find that, while the first and second statistical moments exhibit a very similar behavior, skewness and kurtosis are substantially different and must be taken into account in order to provide an accurate prediction of the switching performance. In fact, a reasonable fit of the probability density function (PDF) of the switching time is given by a Pearson Type IV PDF. The main achievements of this paper underline the need of data-driven design of STT-MRAM that uses a full micromagnetic simulation framework for the statistical proprieties PDF of switching processes.