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Katherine Le

Publications and source records attributed to Katherine Le.

2 recordsLinked to original sources

Amorphous and Nanocrystalline Topological Semimetal YPtBi/W/CoFeB Heterostructures for BEOL-Compatible Spin-Orbit Torque Devices

Spin-orbit torque (SOT) devices require spin-source materials that combine efficient charge-to-spin conversion with back-end-of-line (BEOL) thermal compatibility. Here, we show that YPtBi/W/CoFeB heterostructures deposited directly on Si/SiOx remain predominantly amorphous or weakly nanocrystalline from room temperature to 400 {\deg}C while preserving a large effective damping-like SOT response. Anomalous Hall and harmonic Hall measurements, together with X-ray diffraction, cross-sectional transmission electron microscopy, X-ray reflectivity, and electron energy-loss spectroscopy, show that the response does not correlate with bulk crystallization of YPtBi. Instead, the interfacial analysis indicates that the strongest trend of the spin Hall angle is associated with the chemistry of the upper YPtBi/W boundary: the effective SOT response tracks the integrated W concentration at that YPtBi surface. Meanwhile, a two-spin source analysis shows that the Pt-W-rich interlayer provides only a small positive correction, insufficient to explain the large negative effective spin Hall angle by itself. The dominant control variable is therefore inferred to be the incorporation of W into the upper YPtBi interface, which plausibly modifies the local electronic structure of YPtBi and amplifies the stack-level response. These results provide a more physically constrained interpretation of the stack behavior and identify a BEOL-compatible route to disordered topological spin-source layers for scaled SOT memory and compute-in-memory hardware.

cond-mat.mtrl-sci

Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves

Accurate real-time tracking of dexterous hand movements and interactions has numerous applications in human-computer interaction, metaverse, robotics, and tele-health. Capturing realistic hand movements is challenging because of the large number of articulations and degrees of freedom. Here, we report accurate and dynamic tracking of articulated hand and finger movements using stretchable, washable smart gloves with embedded helical sensor yarns and inertial measurement units. The sensor yarns have a high dynamic range, responding to low 0.005 % to high 155 % strains, and show stability during extensive use and washing cycles. We use multi-stage machine learning to report average joint angle estimation root mean square errors of 1.21 and 1.45 degrees for intra- and inter-subjects cross-validation, respectively, matching accuracy of costly motion capture cameras without occlusion or field of view limitations. We report a data augmentation technique that enhances robustness to noise and variations of sensors. We demonstrate accurate tracking of dexterous hand movements during object interactions, opening new avenues of applications including accurate typing on a mock paper keyboard, recognition of complex dynamic and static gestures adapted from American Sign Language and object identification.

cs.HC