Azab, Eman ; Hegazy, Yasser G. ; Reeg, Hansjoerg ; Schwickert, Marcus ; Hofmann, Klaus (2022)
Tunneling Magnetoresistance DC Current Transformer for Ion Beam Diagnostics.
In: Sensors, 2022, 21 (9)
doi: 10.26083/tuprints-00019596
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Tunneling Magnetoresistance DC Current Transformer for Ion Beam Diagnostics |
Language: | English |
Date: | 2 February 2022 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2022 |
Publisher: | MDPI |
Journal or Publication Title: | Sensors |
Volume of the journal: | 21 |
Issue Number: | 9 |
Collation: | 15 Seiten |
DOI: | 10.26083/tuprints-00019596 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | In this paper, open loop and closed loop Tunneling Magnetoresistance (TMR) DC Current Transformers (DCCTs) for ion beam diagnostics are presented. The DCCTs employ MR sensors to measure the DC component of the accelerator’s ion beam. A comparative study between Giant Magnetoresistance (GMR) and TMR sensors is presented to illustrate the sensor selection criterion for the DCCT application. The two proposed DCCTs are studied in open and closed loop configurations. A closed loop feedback electronic system is designed to generate a feedback current equivalent to the ion beam current such that the sensor operates at zero flux. Furthermore, theoretical and experimental results for the TMR-based DCCT including noise analysis are presented for both open loop and closed loop configurations. Both configurations’ minimum detectable currents are in the range of microampere. The proposed closed loop hardware prototype has a settling time of less than 15 µs. The measured minimum detectable currents for the open and closed loop TMR-based DCCTs are 128.2 µA/√ Hz and 10.14 µA/√ Hz at 1 Hz, respectively. |
Uncontrolled Keywords: | DC current transformer, giant MR sensor, ion beam diagnostics, particle accelerators, tunneling MR sensor |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-195967 |
Classification DDC: | 600 Technology, medicine, applied sciences > 600 Technology 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
Divisions: | 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Integrated Electronic Systems (IES) |
Date Deposited: | 02 Feb 2022 13:17 |
Last Modified: | 14 Nov 2023 19:04 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/19596 |
PPN: | 505619547 |
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