arXiv subjects

Damanpreet Kaur

Publications and source records attributed to Damanpreet Kaur.

4 recordsLinked to original sources

arXiv2026

Symmetry Breaking by Interfacial Dead Layers: Observation of Forbidden Self-Induced Spin-Orbit Torque in Symmetric Ferromagnets

Conventionally, spin-orbit torques (SOTs) in ferromagnets require heavy-metal layers or engineered structural asymmetry to break inversion symmetry. In this work, we report the observation of robust, self-generated SOTs in a nominally symmetric, heavy-metal-free MgO/NiFe/MgO trilayer - a geometry where such torques are theoretically forbidden. By combining harmonic Hall measurements with SQUID magnetometry and X-ray photoelectron spectroscopy, we identify the symmetry-breaking origin: a 1.8 nm magnetic dead layer at the bottom interface. Crucially, we demonstrate a quantitative agreement between our data and the drift-diffusion theory predicted by Kim and Lee, yielding a theoretically extracted dead-layer thickness (1.2 nm) which matches structural characterization. Furthermore, density-functional calculations confirm that NiFe possesses sufficient intrinsic spin Hall conductivity to support the observed spin currents. These results reframe the parasitic dead layer as a functional spintronic component, establishing a universal, all-ferromagnetic route to SOTs in standard magnetic heterostructures.

cond-mat.mes-hall↗

arXiv2026

Roadmap on UV-C photodetectors: materials, applications and industry perspectives

UV-C photodetectors are poised to play an increasingly important role in future photonic technologies, driven by the rapid emergence of UV-C light sources and new wide bandgap semiconductors. These advances are enabling new levels of spectral selectivity, radiation hardness, sensitivity, and device integration, while opening opportunities across a broad range of applications. This roadmap provides a comprehensive overview of the current landscape of UV-C photodetection, spanning established and emerging material platforms (Ga2O3, AlGaN, BN, diamond, MgZnO, 2-dimensional materials, metal halide perovskites, micro-electromechanical systems), and their applications in metrology, astronomy, communications, environmental monitoring, fire detection, missile warning, gas sensing, and medical diagnostics. By identifying opportunities, bottlenecks, and future directions, this roadmap aims to support both newcomers and established researchers, with the aim of accelerating the translation of UV-C photodetectors into impactful technologies.

physics.app-ph↗

arXiv2026

Harnessing Native Chromium Oxidation for Giant Orbital Torque and Field-Free Magnetization Switching in NiFe/Cr Bilayers

Orbital currents offer charge-to-spin conversion beyond the efficiency limit of conventional heavy-metal Spin Hall sources. However, harnessing them has so far required either thick orbital-Hall materials or additional heavy-metal conversion layers. Here, we show that the native oxide of chromium, typically regarded as parasitic, transforms a simple NiFe\Cr bilayer into a self-contained dual-channel orbital-current source without the need for any conversion layer. First-principles calculations predict a nearly threefold enhancement of the orbital Hall conductivity upon surface oxygenation, driven by Cr(3d)-O(2p) hybridization. Experimentally, naturally oxidized NiFe\Cr heterostructures exhibit a giant damping-like torque efficiency of $3.9 \times 10^{6}$ $\Omega^{-1}$ m$^{-1}$, exceeding Pt (Ta) by one (two) orders of magnitude. The torque depicts a non-monotonic Cr-thickness dependence which cannot be explained by a conventional model. We have developed a drift-diffusion model with an oxidation-gated interfacial source which quantitatively reproduces the data, revealing that the Cr-CrO$_x$ interface generates orbital currents over an order of magnitude stronger than the bulk orbital Hall channel with an orbital transport length of $\approx 4$ nm. The enhanced torque enables field-free magnetization switching at $1.58 \times 10^{11}$ A m$^{-2}$, outperforming heavy-metal and CuO$_x$ benchmarks. These results establish native oxidation as a scalable strategy for realizing efficient orbital-torque devices.

cond-mat.mes-hall↗

arXiv2022

TableQuery: Querying tabular data with natural language

This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have various limitations, such as having to feed the entire table as input into a neural network model, making them unsuitable for most real-world applications. Since real-world data might contain millions of rows, it may not entirely fit into the memory. Moreover, data could be stored in live databases, which are updated in real-time, and it is impractical to serialize an entire database to a neural network-friendly format each time it is updated. In TableQuery, we use deep learning models pre-trained for question answering on free text to convert natural language queries to structured queries, which can be run against a database or a spreadsheet. This method eliminates the need for fitting the entire data into memory as well as serializing databases. Furthermore, deep learning models pre-trained for question answering on free text are readily available on platforms such as HuggingFace Model Hub (7). TableQuery does not require re-training; when a newly trained model for question answering with better performance is available, it can replace the existing model in TableQuery.

cs.CL↗