A Low-Equivalent Magnetic Noise and High-Sensitivity Magneto-Electric Coupling Sensor Based on Series Structure
Magneto-electric (ME) coupled sensors are a new type of magneto-mechanical-electrical-based sensors for weak magnetic (0–100 Hz, less than 100 nT) measurements. In practical applications, sensor performance parameters such as sensitivity, noise, and detection limit affect the test results. In this article, the ferroelectric hysteresis loop of the piezoelectric material (PZT) and the hysteresis curve of the magnetostrictive material (Metglas) were first tested. Two ME sensors with the same dimensions and similar resonance conditions were then connected in series, and the measurements showed a large improvement in the performance of the sensors. It is found that the series-connected sensor reduces the equivalent magnetic noise (EMN) from 6 to 3.4 pT/Hz<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{1/2}$ </tex-math></inline-formula> with no change in the inflection point of the low-frequency 1/f spectrum, and the performance is improved by 44%. The ME coupling effect is more obvious after the series connection, the ME coupling coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {ME}} =333.15$ </tex-math></inline-formula> V/cm<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>Oe, the detection limit can be as low as 60 pT, and the sensitivity of the series sensor is improved from 115 to 76 pT, which improves the performance by 33%. Finally, the forced vibration of the electrical signal when the sensors are connected in series is investigated.
Authors
- Zhihao Jiang (ORCID: https://orcid.org/0000-0002-2688-6255)
- Zhao Yao (ORCID: https://orcid.org/0000-0003-3030-4614)
- Zhejun Jin (ORCID: https://orcid.org/0000-0001-6686-5312)
- Quanming Gao (ORCID: https://orcid.org/0000-0002-4564-2790)
- Shandong Li (ORCID: https://orcid.org/0000-0001-8105-7612)
- Xiang Li (ORCID: https://orcid.org/0000-0002-9851-6376)
- Ming Liu (ORCID: https://orcid.org/0000-0002-6310-948X)
- Shipeng Zhang (ORCID: https://orcid.org/0000-0003-4973-5086)
- Yuheng Wang (ORCID: https://orcid.org/0000-0003-2999-2202)
- Xiaoxu Liu (ORCID: https://orcid.org/0000-0002-8705-2000)
Institutions
- Qingdao University (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- IEEE Transactions on Magnetics
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1109/tmag.2024.3520254
- Citations
- 1
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Key Research and Development Program of China