Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2403.14301

Picotesla-sensitivity microcavity optomechanical magnetometry

Zhi-Gang Hu·Yi-Meng Gao·Jian-Fei Liu·Hao Yang·Min Wang·Yuechen Lei·Xin Zhou·Jincheng Li·Xuening Cao·Jinjing Liang·Chao-Qun Hu·Zhilin Li·Yong-Chang Lau·Jian-Wang Cai·Bei-Bei Li
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cavity optomechanical systems have enabled precision sensing of magnetic fields, by leveraging the optical resonance-enhanced readout and mechanical resonance-enhanced response. Previous studies have successfully achieved scalable and reproducible microcavity optomechanical magnetometry (MCOM) by incorporating Terfenol-D thin films into high-quality ($Q$) factor whispering gallery mode (WGM) microcavities. However, the sensitivity was limited to 585 pT/Hz$^{1/2}$, over 20 times inferior to those using Terfenol-D particles. In this work, we propose and demonstrate a high-sensitivity and scalable MCOM approach by sputtering a FeGaB thin film onto a high-$Q$ SiO$_2$ WGM microdisk. Theoretical studies are conducted to explore the magnetic actuation constant and noise-limited sensitivity by varying the parameters of the FeGaB film and SiO$_2$ microdisk. Multiple magnetometers with different radii are fabricated and characterized. By utilizing a microdisk with a radius of 355 $μ$m and a thickness of 1 $μ$m, along with a FeGaB film with a radius of 330 $μ$m and a thickness of 1.3 $μ$m, we have achieved a remarkable peak sensitivity of 1.68 pT/Hz$^{1/2}$ at 9.52 MHz. This represents a significant improvement of over two orders of magnitude compared with previous studies employing sputtered Terfenol-D film. Notably, the magnetometer operates without a bias magnetic field, thanks to the remarkable soft magnetic properties of the FeGaB film. Furthermore, as a proof-of-concept, we have demonstrated the real-time measurement of a pulsed magnetic field simulating the corona current in a high-voltage transmission line using our developed magnetometer. These high-sensitivity magnetometers hold great potential for various applications, such as magnetic induction tomography and corona current monitoring.

Explore related subjects

physics.opticsphysics.app-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhi-Gang Hu, Yi-Meng Gao, Jian-Fei Liu, Hao Yang, Min Wang, Yuechen Lei, Xin Zhou, Jincheng Li, Xuening Cao, Jinjing Liang, Chao-Qun Hu, Zhilin Li, Yong-Chang Lau, Jian-Wang Cai, Bei-Bei Li. 2024-03-21. Picotesla-sensitivity microcavity optomechanical magnetometry. https://doi.org/10.1038/s41377-024-01643-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Active Fourier Spectroscopy: From fixed sampling protocols to adaptive measurement

Fourier-transform spectroscopy (FTS) is based on the Nyquist-Shannon theorem, which dictates a strict sampling protocol for the measurement of continuous signals. Here we introduce Active Fourier Spectroscopy (AFS), a generalization of FTS in which measurements are still taken in the Fourier-conjugate delay domain, but where the most informative acquisition points are selected adaptively and the spectrum is reconstructed by sequential, uncertainty-aware inference rather than by fixed-grid Fourier inversion. Our approach relies on a theoretical insight that the Nyquist condition applies only under complete ignorance about the system under investigation, and shows how to systematically improve upon it when prior knowledge exists. Because AFS recovers conventional FTS whenever the prior is uninformative, its performance never falls below that of conventional sampling. We demonstrate AFS for optical spectroscopy in three settings vital to research and medicine: clinical FTIR-based blood plasma analysis, optical metrology of structured laser beams, and hyperspectral imaging with an RGB camera. In each case AFS reaches a higher target reconstruction quality from far fewer acquisitions and provides complete uncertainty quantification in real time, propagated rigorously through downstream analysis.

physics.optics↗
arXiv2026

Memory in Integrated Photonic Neural Networks: From Physical Mechanisms to Neuromorphic Architectures

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biological neural systems inherently co-localize computation and memory through distributed, dynamical processes. Neuromorphic computing seeks to emulate this paradigm by leveraging physical substrates whose intrinsic dynamics simultaneously encode and process information. Among emerging platforms, silicon photoncis offer a compelling approach due to its high bandwidth, low-loss propagation, and inherent parallelism. This review examines the role of memory in integrated photonic neuromorphic systems, with emphasis on the physical mechanisms that provide volatile (short-term) and non-volatile (long-term) memory in silicon-on-insulator and hybrid silicon-on-insulator platforms. Drawing inspiration from digital, biological, and photonic memory architectures, we classify existing approaches based on their underlying physical principles. We cover implementations ranging from delay lines and slow-light structures to multistable dynamics and structural memory based on charge trapping and phase-change materials. We then discuss how these mechanisms support photonic neural network architectures, including feed-forward, reservoir computing, spiking and hybrid optoelectronic recurrent systems, and assess their relevance for time-dependent singal-processing tasks such as channel equalization in telecommunications. This review aims to establish a unified framework for understanding memory and learning in neuromorphic photonics and outlines key challenges and opportunities for scalable, energy-efficient neuromorphic hardware.

physics.optics↗
arXiv2026

Exoplanet Detection Using Adaptive Quantum-Optimal Measurement

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to \(10^{-8}\) -- five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.

physics.optics↗