Mayank K. Singh | Searcharxiv

arXiv2026

Efficient magnetization switching driven by orbital torque originating from light 3d-transition-metal nitrides

The orbital Hall effect (OHE) in light transition metals offers a promising route to generate orbital torques for efficient magnetization control, providing an alternative to conventional spin Hall effect approaches that rely on heavy metals. We demonstrate perpendicular magnetization switching in [Co/Pt]3 multilayers driven by the OHE in a light 3d transition metal nitride, VN, with 111-texture of face-center cubic structure. Second harmonic Hall measurement reveals a large torque efficiency of -0.41 in the VN(7.5 nm)/[Co(0.35nm)/Pt(0.3 nm)]3, which significantly surpasses that in the control samples with Co, Py, and CoFeB ferromagnets, suggesting strong conversion of orbital current originating from VN to spin current by [Co/Pt]3 ferromagnet. Full switching by in-plane current is achieved with an in-plane magnetic field, while partial field-free switching occurs without it. The critical current density for the switching is found to be comparable to that of the W-based spin-orbit torque device. First-principles calculations confirm a large orbital Hall conductivity in VN, with a small spin Hall conductivity around the Fermi energy. Our results highlight the potential in the combination of light 3d transition metal nitrides and Co/Pt ferromagnetic multilayer with 111-texture to maximize the magnetization switching efficiency of orbitronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Cross-modal Face- and Voice-style Transfer

Image-to-image translation and voice conversion enable the generation of a new facial image and voice while maintaining some of the semantics such as a pose in an image and linguistic content in audio, respectively. They can aid in the content-creation process in many applications. However, as they are limited to the conversion within each modality, matching the impression of the generated face and voice remains an open question. We propose a cross-modal style transfer framework called XFaVoT that jointly learns four tasks: image translation and voice conversion tasks with audio or image guidance, which enables the generation of ``face that matches given voice" and ``voice that matches given face", and intra-modality translation tasks with a single framework. Experimental results on multiple datasets show that XFaVoT achieves cross-modal style translation of image and voice, outperforming baselines in terms of quality, diversity, and face-voice correspondence.

cs.CV↗