Tunnel Magnetoresistance Sensors Research Articles

Temperature Compensation Method for Tunnel Magnetoresistance Micro-Magnetic Sensors Through Reference Magnetic Field.

The sensitivity of Tunnel Magnetoresistance (TMR) sensors is characterized by significant temperature drift and poor sensitivity drift repeatability, which severely impairs measurement accuracy. Conventional temperature compensation techniques are often hindered by low compensation precision, inadequate real-time performance, and an inability to effectively address the issue of poor repeatability in temperature drift characteristics. To overcome these challenges, this paper introduces a novel method for suppressing temperature drift in TMR sensors. In this method, an alternating reference magnetic field is applied to TMR sensors, and the output amplitude at the frequency of the reference magnetic field is calculated to compensate the sensitivity temperature drift in real time. Temperature characteristic tests were conducted in a non-magnetic temperature test chamber, and the results revealed that the proposed method significantly reduced the TMR sensitivity drift coefficient from 985.39 ppm/°C to 59.08 ppm/°C. Additionally, the repeatability of sensitivity temperature characteristic curves was enhanced, with a reduction in root mean square error from 0.84 to 0.21. This approach effectively mitigates temperature-induced sensitivity drift without necessitating the use of a temperature sensor, and has the advantages of real-time performance and repeatability, providing a new approach for the high-precision temperature drift suppression of TMR.

Tunnel magnetoresistive sensors with non-hysteretic resistance–magnetic field curves using noncollinear interlayer exchange coupling through RuFe spacers

Tunnel magnetoresistive sensors with non-hysteretic resistance–magnetic field curves using noncollinear interlayer exchange coupling through RuFe spacers

High-Resolution Rotation-Measuring System for MEMS Ultrasonic Motors Using Tunneling Magnetoresistance Sensors.

This study proposes a high-resolution rotation-measuring system for miniaturized MEMS ultrasonic motors using tunneling magnetoresistance (TMR) sensors for the first time. Initially, the architecture and principle of the rotation-measuring system are described in detail. Then, the finite element simulation is implemented to determine the miniaturized permanent magnet's residual magnetization, dimensions, and TMR sensor position. Finally, the experiments are implemented to evaluate the performance. Using calibration based on a high-precision servo motor, it is found that the relationship between the output and rotational angle is highly linear and immune to the rotor's out-of-plane movement. Meanwhile, the angle-detecting resolution is higher than 0.1°. After the calibration, the continuous rotation of the MEMS ultrasonic motor is tested. It is found that the angle testing result varies with a period close to 360°, which indicates that the rotation-measuring system has successfully detected the motor's rotation.

Development and Comprehensive Evaluation of TMR Sensor-Based Magnetrodes.

Due to their compact size and exceptional sensitivity at room temperature, magnetoresistance (MR) sensors have garnered considerable interest in numerous fields, particularly in the detection of weak magnetic signals in biological systems. The "magnetrodes", integrating MR sensors with needle-shaped Si-based substrates, are designed to be inserted into the brain for local magnetic field detection. Although recent research has predominantly focused on giant magnetoresistance (GMR) sensors, tunnel magnetoresistance (TMR) sensors exhibit a significantly higher sensitivity. In this study, we introduce TMR-based magnetrodes featuring TMR sensors at both the tip and midsection of the probe, enabling detection of local magnetic fields at varied spatial positions. To enhance detectivity, we designed and fabricated magnetrodes with varied aspect ratios of the free layer, incorporating diverse junction shapes, quantities, and serial arrangements. Utilizing a custom-built magnetotransport and noise measurement system for characterization, our TMR-based magnetrode demonstrates a limit of detection (LOD) of 300pT/ at 1 kHz. This implies that neuronal spikes can be distinguished with minimal averaging, thereby facilitating the elucidation of their magnetic properties.

Development of Tunnel Magneto-Resistive Sensors

Development of Tunnel Magneto-Resistive Sensors

Dual-range TMR current sensor based on magnetic shunt/aggregation effects utilizing single magnetic ring structure

In this paper, we propose and design a novel dual-range tunnel magnetoresistance (TMR) current sensor with a single magnetic ring structure. This design incorporates two distinct magnetic guiding effects, namely magnetic shunt and magnetic aggregation, within the same magnetic ring. By integrating a high-sensitivity TMR sensor chip with a closed-loop feedback circuit, we achieve a TMR current sensor with excellent linearity, high resolution, as well as high frequency response. The magnetic ring structure is first modeled and simulated, establishing a correlation between the distribution of magnetic induction intensity and the parameters of the magnetic ring and feedback coils. Through simulation optimization and theoretical calculations, we determine the optimal positions for TMR sensor chips in the magnetic ring, suitable for both current ranges. When a signal current is present, the TMR sensor chip generates a weak differential voltage signal, which is subsequently amplified, processed, and automatically transmitted to the laptop via a serial port. Furthermore, the sensor allows for automatic switching between the two current ranges. The results demonstrate that our designed dual-range current sensor exhibits outstanding performance characteristics, including a high resolution of 500 μA in the small range, accuracy of 0.10%, excellent linearity of 0.011%, and a fast frequency response of 500 kHz. These features make it highly applicable in various fields such as new energy vehicles and smart grids, indicating promising prospects for its widespread utilization.

Fabrication of half-metallic Co2FeAlxSi1–x thin film with small magneto-crystalline anisotropy constant K1

A tunnel magnetoresistance (TMR) sensor is a highly sensitive magnetic sensor workable at room temperature. To achieve smaller magnetic field detection, both a high TMR ratio and small magnetic anisotropy of the free layer are required. In this study, we developed single crystalline Co-based Heusler alloy Co2FeAlxSi1-x films as the free layer of TMR sensors and systematically investigated their crystalline and magnetic properties. The magnetic tunnel junction (MTJ) with these alloys is a promising device for highly sensitive TMR sensors due to their half-metallicity. We evaluated the B2 and L21 ordering parameters SB2 and SL21. SB2 was above 80 % for all the samples and SL21 was 48 % at a maximum, which indicates that all the samples exhibit half-metallicity. In addition, we evaluated their first magneto-crystalline anisotropy constant K1 from magnetization curves. K1 changed from positive to negative as Al component x increases and K1 was about 1200 erg/cc at x = 0.33. These results indicate that Co2FeAlxSi1-x films around x = 0.33 have a good candidate for the free layer of TMR sensors because of their half-metallicity and small magnetic anisotropy.

Design and characteristics of contactless small-magnetic-metal detector base on tunnel magnetoresistance sensors in differential circuit

Unexpected metal pieces are significant problems for food and pharmaceutical manufacturers. Commonly, conventional coil metal detectors are used to detect these metal pieces. However, these detectors can only detect cm-size metal pieces, making it challenging to detect mm-size metal pieces. This paper presents an advanced contactless small-magnetic-metal detector based on tunnel magnetoresistance sensors (TMR). To improve the system's sensitivity, the differential circuit combined with a simple AC-bias lock-in amplifier was designed to reduce the noise of TMR sensors. The sensitivity of this designed detection system is 0.44V/mm with a good linearity of R2=0.82674. The experimental results demonstrate that the fabricated prototype can detect the 100 – μm diameter and 1-mm length metal wire. Due to high sensitivity and low-cost fabrication, this system is promising to apply on the manufacturing lines for detecting unexpected tiny metal pieces.

Magnetoelectric Sensor Operating in d15 Thickness-Shear Mode for High-Frequency Current Detection.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d15 thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d15 thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

Air gap flux density measurement of PMSLM based on TMR sensing external stray magnetic field and CNN-LSTM

This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by “substituting external to internal.” Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method.

Evaluation of Pipe Thickness by Magnetic Hammer Test with a Tunnel Magnetoresistive Sensor.

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect weak magnetic fields generated by the natural vibration of a pipe with a high signal-to-noise ratio. In this study, several steel pipes with different wall thicknesses were measured using a TMR sensor to demonstrate the superiority of MHT. The results of the measurement show that wall thickness can be evaluated with the accuracy of several tens of microns from the change in the natural vibration frequency of the specimen pipe. The pipes were also inspected underwater using a waterproofed TMR sensor, which demonstrated an accuracy of less than 100 μm. The validity of these results was by simulating the shielding of magnetic fields and vibration of the pipes with the finite element method (FEM) analysis. The proposed noncontact, fast, and accurate method for thickness testing of long-distance pipes will contribute to unmanned, manpower-saving nondestructive testing (NDT) in the future.

A novel dual-core current transformer based on TMR sensor and its measuring characteristics

Current transformers (CTs) are the most widely used measurement instrumentation in power grids. Nevertheless, CT saturation gives rise to large measurement errors, which may have bad repercussions for the power system. In order to deal with the problem of CT saturation, a novel dual-core current transformer based on tunnel magnetoresistance sensor (DTCT) is proposed. Theoretical analyses, finite element method (FEM) simulations and accuracy tests are carried out to validate the practicability and effectiveness of the proposed DTCT. The simulation and experimental results manifest that the steady-state and transient characteristics of the DTCT are outstanding.

TMR-high-temperature superconductor composite magnetic sensor and its performance optimization

Tunnel magnetoresistance (TMR), recognized for its high sensitivity and low power consumption, holds significant promise in the domain of weak magnetic field detection. Using superconducting materials as magnetic concentrators can achieve several hundred to even a thousandfold amplification of magnetic fields, making it one of the most effective approaches to enhance the magnetic field resolution of TMR sensors. This paper utilized the flip-chip bonding process to integrate TMR with the high-temperature superconductor YBCO (YBa2Cu3O7-δ), and successfully developed TMR-YBCO composite magnetic sensor. Building upon this foundation, through optimization of the fabrication process and the pioneering use of a structural design incorporating the filling of annular holes with superconducting concentrators, the sensitivity of the sensor was further enhanced. Finally, the magnetic field resolution was increased by 1030 times compared to TMR sensors, reached to 2.9pT/Hz1/2 at 1 Hz. Simultaneously, results from frequency band testing indicated excellent frequency band characteristics, with a frequency response range exceeding kHz.

A novel TMR sensor based on digital lock-in amplifier technology for portable oil multipollutant detection system

Metal particles identification in lubrication oil has become a popular technology to perform the fault diagnosis and health monitoring of machinery equipment. Unfortunately, the magnetic signals induced by particles are generally weak or even submerged in heavy noise. The sensitivity of conventional inductive sensors for detecting non-ferrous particles can still be improved to better distinguish between different non-ferrous particles. Based on the above issues, this paper proposed a novel tunnel magnetoresistance (TMR) sensor based on digital lock-in amplifier (DLIA) technology to detect ferrous and multiple nonferrous metallic particles in oil under noisy conditions. Moreover, compared to conventional inductive sensor structures, the proposed method can effectively reduce the sensor volume, which is particularly necessary when considering the design of portable oil multipollutant detection system. Owing to the high resolution of TMR and outstanding versatility of DLIA for extracting weak signal overwhelmed in noise, the detection sensitivity can be improved tremendously. Extensive simulation and experimental results validated that the proposed approach was practical and effective. Based on the experimental results, the minimum values of detectable diameter could be computed as 24.76 µm for iron particles, 81.34 µm and 82.32 µm for copper and aluminium particles respectively.

Synchronous Imaging and Multimodal Fusion of Optical and Electromagnetic Measurements for Overlapping Defects Inspection

It is a challenging problem to accurately detect overlapping defects. This paper proposes a system that can obtain optical and electromagnetic signals synchronously. It contains an industrial camera, a chromatic confocal displacement sensor (CCDS) and an eddy current testing (ECT) probe with high-resolution array tunneling magnetoresistance (TMR) sensors. A multimodal fusion algorithm is proposed to combine the advantages of the optical testing and ECT. The fusion of the visual image and CCDS measurement results in morphology of the surface defects. To recognize the overlapping buried defect, a finite element method model is developed to estimate the surface defect signal using parameters from the optical measurements, and then the surface defect signal is subtracted from the experimental image, based on which the overlapping defects are precisely detected. This method can be widely used in industry to comprehensively assess the health and integrity of structures.

A Novel Tunneling Magnetoresistive Current Sensor Based on Reference Magnetic Field Source

A Novel Tunneling Magnetoresistive Current Sensor Based on Reference Magnetic Field Source

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

A geomagnetic field is a vector field in which the strength and direction are related to geographical location. Geomagnetic navigation technology, which uses collected geomagnetic field information to achieve positioning and navigation, has the advantages of reliability, stability, accuracy, and concealment. With the deepening research on geomagnetic navigation, bioinspired geomagnetic navigation technology has also been developed, which mainly studies and imitates the magnetic sensing mechanism and navigation behavior of animals, providing new research ideas for geomagnetic navigation technology. The magnetic particle hypothesis and free radical pair hypothesis are two mainstream mechanisms of biological sensing using the geomagnetic field, and studies have shown that these two mechanisms may be coupled within organisms. In this study, we propose a bioinspired weak magnetic vector (BWMV) sensor based on the joint sensing mechanism of magnetic particles and free radicals. It consists of a magnetic rod made of soft magnetic material and a tunnel magnetoresistance (TMR) sensor array. A magnetic rod was used to simulate magnetic particles to convert magnetic field angle information into magnetic field intensity distribution information, and the TMR sensor array was used to simulate the perception of the magnetic field distribution by free radicals. In addition, artificial neural networks (ANNs) were used for BWMV sensors to obtain the mapping relationship between the magnetic field distribution and parameters, which can be used for geomagnetic navigation. To verify the navigation effect of the BWMV sensor in the laboratory, a simulated geomagnetic navigation device was built, and the high-precision mapping relationship from geomagnetic parameters to latitude and longitude information of the selected navigation area was obtained through another ANN. Finally, the effectiveness of the BWMV sensor based on ANNs for geomagnetic navigation is verified using simulated navigation experiments.

Research on Residual-Current Measurement System of Substation Considering Magnetic Shielding Effect.

Residual current is an important monitoring quantity of a power system, and a current sensor plays an important role in detecting current. The substation environment is complex. In addition to the power frequency signal, residual current also has AC and DC components. But it is also affected by the stray magnetic field of the substation. Therefore, the accuracy of the current sensor demands higher requirements. The tunnel magnetoresistive sensor has the advantages of a stable operation, high efficiency, and energy saving, but it is easily affected by the external stray magnetic field during measurements, resulting in a large error. Therefore, this paper proposes a residual-current sensing monitoring system considering the magnetic shielding effect. The root mean square error of the magnetic shielding structure is only 0.572 mA, which can effectively reduce the influence of the external magnetic field and improve the detection accuracy. At the same time, the DC measurement error is less than 1%, the AC measurement error is less than 5%, and the hybrid AC/DC error is less than 8%. It has good response ability and can accurately detect residual current.

High resolution magnetic field imaging probe with two rows of TMR sensors for small defects inspection

It is still a challenging problem to detect small defects for eddy current array probes, which requires the probes to possess excellent sensitivity, as well as high spatial resolution. This paper presents a novel high-resolution magnetic field imaging probe with two rows of tunneling magnetoresistance (TMR) array sensors. The bare die sensors are integrated on a printed circuit board by golden wire bonding technology. The two rows of sensors are placed staggered with each other. The data of the two arrays are merged into a matrix, in which way the image pixel pitch is increased to 0.25 mm. The probe employs a differential scheme to suppress the noise, so as to detect the weak signal of small defects. To highlight the weak defect indications, feature extraction and segmentation algorithms are developed. The experimental results confirm that the proposed method can inspect a small defect with dimensions 1 mm (length) × 0.1 mm (width) × 0.1 mm (depth) on a stainless-steel sample.

Non-Contact Current Measurement for Three-Phase Rectangular Busbars Using TMR Sensors.

This paper proposes a non-contact current measurement method for three-phase rectangular busbars based on TMR (tunneling magneto-resistance) sensors, due to their advantages of large dynamic range, wide bandwidth, light weight, and easy installation. A non-contact current sensor composed of only three TMR sensors is developed and the TMR sensors are respectively placed at a location with a certain distance from the surface of each rectangular busbar to measure the magnetic fields generated by the busbar currents. To calibrate the developed current sensor, i.e., to establish the relationship between the magnetic fields measured by the TMR sensors and the currents flowing in the three-phase rectangular busbars, we designed a thyristor-controlled resistive load as a calibrator, which is connected to a downstream branch of the distribution cabinet. By switching the resistive load, a calibration current, which can be identified from the background current, is generated in one rectangular busbar and its value is measured at the location of the calibrator, and transmitted wirelessly to the location of the TMR sensors. A new and robust method is proposed to extract the voltage components, corresponding to the calibration current, from the voltage waveforms of the TMR sensors. By calculating the proportional coefficients between the calibration currents and the extracted voltage components, online calibration of the current sensor is achieved. We designed and implemented a current measurement system consisting of a current sensor using TMR sensors, a thyristor-controlled resistive load for current sensor calibration, and a data acquisition circuit based on a multi-channel analog-to-digital converter (ADC). Current measurement experiments were performed in a practical distribution cabinet installed in our laboratory. Compared to the measurement results using a commercial current probe with an accuracy of 1%, the relative error of the measured currents in RMS is less than 2.5% and the phase error is less than 1°, while the nonlinearity error of the current sensor is better than 0.8%.