Milli-Tesla Level Magnetic Field Vector Detection Based on YIG Microcavity Optical Sensor

A millitesla-level magnetic field vector detection using a yttrium iron garnet (YIG) microcavity is proposed. Under the magnetic field strength of 95.49 mT, the YIG microsphere cavity-based sensor realizes the direction sensitivity of 0.0717 pm/°.

Authors

Institutions

Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/acp/ipoc63121.2024.10809970
Primary Topic
Magneto-Optical Properties and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Milli-Tesla Level Magnetic Field Vector Detection Based on YIG Microcavity Optical Sensor

Yanran Wu, Daquan Yang, Songyi Liu, Yong‐Pan Gao
Magneto-Optical Properties and Applications
article

Milli-Tesla Level Magnetic Field Vector Detection Based on YIG Microcavity Optical Sensor

Yanran Wu, Daquan Yang, Songyi Liu, Yong‐Pan Gao
article en

Abstract

A millitesla-level magnetic field vector detection using a yttrium iron garnet (YIG) microcavity is proposed. Under the magnetic field strength of 95.49 mT, the YIG microsphere cavity-based sensor realizes the direction sensitivity of 0.0717 pm/°.

Beijing University of Posts and Telecommunications (CN), State Key Laboratory of Information Photonics and Optical Communications
Natural Science Foundation of Beijing Municipality, State Key Laboratory of Information Photonics and Optical Communications
Affordable and clean energy
Openalex Percentile: Top 77%
Magneto-Optical Properties and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.