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Mechanical strain provides a knob for controlling the magnetization of the magnetostrictive-free layer of magnetic tunnel junctions (MTJs), with many applications for energy-efficient memory and computing. This requires integrating materials with high magnetostriction coefficient into MTJs, while still preserving the CoFeB-MgO tunnel barrier for high tunnel magnetoresistance (TMR). One way to accomplish this is to replace the CoFeB free layer of the MTJ with an exchange-coupled bilayer of CoFeB and a highly magnetostrictive ferromagnet like Galfenol (FeGa). Here, FeGa, a thermally stable magnetostrictive material, is integrated into CoFeB-based MTJs. We show that engineering a thin layer of CoFeB and FeGa provides a means of controlling the magnetic properties and switching field in FeGa-based MTJs, and that the exchange-coupled FeGa-CoFeB layer can be used as both a free layer and a fixed layer in the MTJ stack with TMR as high as 100%. 

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1} 10 f b − 1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.