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Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields. Breaking the taboo, this study explores a novel technique to systematically control magnon interference using asymmetric wavevectors from two different SW modes (magnetostatic surface SWs and backward volume magnetostatic SWs) in a microstructured yttrium iron garnet crossbar. Using this system, we demonstrate phase reconfigurability in the interference pattern by modulating the thermal landscape, modifying the dispersion of the interfering SW modes. Thus, we manifest that such a tunable interference can be used to implement reconfigurable logic gates operating between the XNOR and XOR modes by using symmetric and asymmetric interference, respectively.

Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y3Fe5O12 bilayer structure. We demonstrated that the CoFeB/Y3Fe5O12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10–2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10–6 T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y3Fe5O12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y3Fe5O12 and Y3Fe5O12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of $${10}^{-9}$$ 10 - 9 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe.

Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ($${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.