Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model.

Yutao Li,Zheng Qian,Hao Yu,Liliang Wang

doi:10.3390/s21227672

Abstract

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.

Highlights

Tunneling magnetoresistive (TMR) is an emerging commercial magnetoresistive sensor with good performance.It has a broad application prospect in automobiles, electronic storage, biomedicine, and aerospace
Current investigations on TMR mainly focuses on its performance, while there is relatively little research on its magnetic hysteresis
The test results show that the error of the probability model is smaller than that of the Preisach model, especially under the condition of a high-order reversal curve

Summary

Introduction

TMR is an emerging commercial magnetoresistive sensor with good performance. It has a broad application prospect in automobiles, electronic storage, biomedicine, and aerospace. The other type of phenomenon model is the white-box method, which tries to describe the characteristics of the hysteresis based on a particular equation. The Preisch model-based methods have been widely adopted to describe and analyze the characteristics of the hysteresis of TMR, these methods are either complicated or require more data. Based on the Preisach model, we explores the input and output relationship of the TMR sensor from the perspective of probability, and proposes a static hysteresis model based on probability; this model has higher accuracy than the classical Preisach model, especially in the case of a high-order rotation curve.

Preisach Introduction

Results and Discussion

A3 A1 A3

Conclusions