Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model

Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hys...

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Autores principales:	Yutao Li, Liliang Wang, Hao Yu, Zheng Qian
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tunneling magnetoresistive (TMR) hysteresis Preisach model probability hysteresis model Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/1aadae8e65de4c5aa48534c9bbaef1cf
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spelling	oai:doaj.org-article:1aadae8e65de4c5aa48534c9bbaef1cf2021-11-25T18:58:22ZResearch of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model10.3390/s212276721424-8220https://doaj.org/article/1aadae8e65de4c5aa48534c9bbaef1cf2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7672https://doaj.org/toc/1424-8220Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hysteresis of TMR accurately. Preisach model is one of the popular models to describe the behavior of inherent hysteresis for TMR, whereas it presents low accuracy in high-order hysteresis reversal curves. Furthermore, the traditional Preisach model has strict congruence constraints, and the amount of data seriously affects the accuracy. This paper proposes a hysteresis model from a probability perspective. This model has the same computational complexity as the classic Preisach model while presenting higher accuracy, especially in high-order hysteresis reversal curves. When measuring a small amount of data, the error of this method is significantly reduced compared with the classical Preisach model. Besides, the proposed model’s congruence in this paper only needs equal vertical chords.Yutao LiLiliang WangHao YuZheng QianMDPI AGarticletunneling magnetoresistive (TMR)hysteresisPreisach modelprobability hysteresis modelChemical technologyTP1-1185ENSensors, Vol 21, Iss 7672, p 7672 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tunneling magnetoresistive (TMR) hysteresis Preisach model probability hysteresis model Chemical technology TP1-1185
spellingShingle	tunneling magnetoresistive (TMR) hysteresis Preisach model probability hysteresis model Chemical technology TP1-1185 Yutao Li Liliang Wang Hao Yu Zheng Qian Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
description	Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hysteresis of TMR accurately. Preisach model is one of the popular models to describe the behavior of inherent hysteresis for TMR, whereas it presents low accuracy in high-order hysteresis reversal curves. Furthermore, the traditional Preisach model has strict congruence constraints, and the amount of data seriously affects the accuracy. This paper proposes a hysteresis model from a probability perspective. This model has the same computational complexity as the classic Preisach model while presenting higher accuracy, especially in high-order hysteresis reversal curves. When measuring a small amount of data, the error of this method is significantly reduced compared with the classical Preisach model. Besides, the proposed model’s congruence in this paper only needs equal vertical chords.
format	article
author	Yutao Li Liliang Wang Hao Yu Zheng Qian
author_facet	Yutao Li Liliang Wang Hao Yu Zheng Qian
author_sort	Yutao Li
title	Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
title_short	Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
title_full	Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
title_fullStr	Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
title_full_unstemmed	Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model
title_sort	research of probability-based tunneling magnetoresistive sensor static hysteresis model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1aadae8e65de4c5aa48534c9bbaef1cf
work_keys_str_mv	AT yutaoli researchofprobabilitybasedtunnelingmagnetoresistivesensorstatichysteresismodel AT liliangwang researchofprobabilitybasedtunnelingmagnetoresistivesensorstatichysteresismodel AT haoyu researchofprobabilitybasedtunnelingmagnetoresistivesensorstatichysteresismodel AT zhengqian researchofprobabilitybasedtunnelingmagnetoresistivesensorstatichysteresismodel
_version_	1718410449118035968

Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model

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