Miniature high-sensitivity magnetic field sensor and preparation method

Abstract

The present invention relates to the field of magnetic induction technology, and discloses a miniature high-sensitivity magnetic field sensor and a preparation method. The miniature high-sensitivity magnetic field sensor includes multiple sensing detection microprobes and a sensitivity correction processor. The sensing detection microprobes are used to obtain detected magnetic field signals and send the detected magnetic field signals to the sensitivity correction processor; the sensitivity correction processor is used to calculate at least one deviation signal based on the magnetic field signals detected by the multiple sensing detection microprobes, and the sensitivity correction processor is also used to calculate at least two correction signals for each deviation signal based on the magnetic field signals detected by the multiple sensing detection microprobes; the sensitivity correction processor is also used to calculate and select at least one correct correction signal based on the at least two correction signals for each deviation signal and the magnetic field intensity distribution law.

Description

Technical Field

[0001] The present invention belongs to the field of magnetic induction technology, and in particular relates to a miniature high-sensitivity magnetic field sensor and a preparation method thereof. Background Art

[0002] In the current technological landscape, miniature, highly sensitive magnetic field sensors play a vital role in numerous fields, such as biomedicine, geological exploration, aerospace, and automated control. They are widely used to monitor changes in weak magnetic fields to achieve precise positioning, navigation, material detection, and other functions. However, while pursuing higher sensitivity, existing magnetic field sensor technology faces several key challenges that limit its performance and application scope:

[0003] Accuracy and stability issues: Traditional single-point magnetic field sensors are susceptible to environmental factors such as temperature fluctuations, electromagnetic interference, and mechanical vibration, resulting in large errors and instability in measurement results. These errors are particularly significant when the magnetic field strength is weak or high-resolution measurements are required.

[0004] Individual sensor differences: Even sensors from the same batch can exhibit subtle performance variations due to manufacturing tolerances and material inhomogeneities. This makes it difficult to directly combine data in multi-sensor array applications, affecting overall measurement accuracy. Inadequate adaptability to complex magnetic field environments: In complex or non-uniform magnetic field environments, a single sensor struggles to accurately distinguish between background magnetic fields and target signals, and is sensitive to local magnetic field disturbances, making misjudgments more likely.

[0005] Limitations of data processing algorithms: Existing algorithms often focus on simple signal amplification or filtering, lacking advanced algorithms for multi-sensor data fusion to effectively identify and correct measurement deviations, and are unable to fully utilize the potential advantages of sensor arrays. Summary of the Invention

[0006] The basic concept of the technical solution adopted in the present invention is:

[0007] A miniature high-sensitivity magnetic field sensor includes a plurality of sensing detection microprobes and a sensitivity correction processor. The sensing detection microprobes are used to obtain detected magnetic field signals and send the detected magnetic field signals to the sensitivity correction processor. The sensitivity correction processor is used to calculate at least one deviation signal based on the magnetic field signals detected by the plurality of sensing detection microprobes. The sensitivity correction processor is also used to calculate at least two correction signals for each deviation signal based on the magnetic field signals detected by the plurality of sensing detection microprobes.

[0008] The sensitivity correction processor is also used to calculate and select at least one correct correction signal based on at least two correction signals of each deviation signal and the magnetic field intensity distribution law, and the correction signal is used to correct the sensing sensitivity.

[0009] As a preferred embodiment of the present invention, the sensing detection micro probe adopts a Hall effect sensor, a magnetoresistive element, or an optical fiber magneto-optical Kerr effect sensor.

[0010] As a preferred embodiment of the present invention, the sensitivity correction processor adopts a microcontroller / microprocessor or DSP.

[0011] As a preferred embodiment of the present invention, the specific process of "calculating at least one deviation signal based on magnetic field signals detected by multiple sensing microprobes" includes the following steps:

[0012] All the sensing micro-probes measure the magnetic field strength in the same magnetic field environment simultaneously or sequentially within a preset time; and record the magnetic field strength value detected by each sensing micro-probe;

[0013] The magnetic field strength values ​​of all sensing detection microprobes are averaged to obtain a representative magnetic field strength estimate; for the measurement value of each sensing detection microprobe, the difference between it and the average magnetic field strength is calculated. If the difference between the measurement value of a certain sensing detection microprobe and the average value exceeds a preset threshold, the measurement value of the sensing detection microprobe is considered to constitute a "deviation signal".

[0014] As a preferred embodiment of the present invention, in the process of calculating at least two correction signals for each deviation signal based on the magnetic field signals detected by multiple sensing detection microprobes, for each deviation signal, the measurement values ​​of other sensing detection microprobes relative to the deviation signal are first determined, and the measurement values ​​of the sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are selected. The measurement values ​​of the sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are directly used as the correction signal, or the weighted average of "the measurement values ​​of the sensing detection microprobes adjacent to the sensing detection microprobe generating the deviation signal" and the weighted average of "the measurement values ​​of the sensing detection microprobes symmetrically positioned with the sensing detection microprobe generating the deviation signal" are used as the correction signal.

[0015] As a preferred embodiment of the present invention, at least one correct correction signal is selected based on at least two correction signals of each deviation signal and the distribution law of magnetic field intensity. In the specific process of correcting the sensor sensitivity, the correction signal is used to evaluate the consistency of each correction signal with the theoretical model in combination with the magnetic field distribution model, and the magnetic field distribution model is interpolated or fitted to match the actual probe layout and measurement points, and at least one correction signal with the best consistency with the theoretical model is selected as the correct correction signal.

[0016] Combined with the magnetic field distribution model, the consistency of each correction signal with the theoretical model is evaluated, the magnetic field distribution model is interpolated or fitted to match the actual probe layout and measurement points, and at least one correction signal with the best consistency with the theoretical model is selected as the correct correction signal. Specifically, the following steps are performed:

[0017] Establish the theoretical magnetic field distribution function B th (x, y, z), this function describes the distribution of the magnetic field in space under ideal conditions, where (x, y, z) represents the spatial coordinates; for each sensing microprobe, let its position be (x i ,y i , z j ), the measured magnetic field strength is B meas，i , for each sensing microprobe, the deviation signal is defined as ΔB i =B meas，i -B avg , where B avg is the average of all probe measurements;

[0018] For the deviation signal ΔB i , at least two correction signals have been calculated and Specifically, it is the measurement values ​​of adjacent and symmetrical probes or their weighted average;

[0019] Use interpolation or fitting in the theoretical magnetic field distribution model B th Find or generate the theoretical magnetic field strength value B corresponding to each probe position on (x, y, z) th，i ;

[0020] For each deviation signal corresponding to the correction signal Calculate its difference with the theoretical value B th，i difference measure;

[0021] The residual sum of squares is used Or absolute error For each deviation signal, compare the error metrics corresponding to the two correction signals or The correction signal with the smallest error is selected as the "correct correction signal", that is, or

[0022] The method for preparing a micro high-sensitivity magnetic field sensor comprises the following steps:

[0023] Choose Hall effect sensors, magnetoresistive elements, fiber optic magneto-optical Kerr effect sensors, and write customized firmware using a microcontroller / microprocessor or DSP to achieve the following functions:

[0024] Implement synchronous or sequential control logic to ensure that all probes collect data at the same time or in a predetermined sequence to ensure the time consistency of the data; automatically identify and record deviation signals; implement the calculation logic of the correction signal, including the direct use or weighted average of the measurement values ​​of adjacent and symmetrical probes; combine the magnetic field distribution model to perform data fitting and interpolation, evaluate the consistency of the correction signal with the theoretical model, and automatically select the optimal correction signal.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] By tightly coupling the theoretical magnetic field distribution model with actual measurement data, the system uses interpolation or fitting techniques to match the most appropriate theoretical magnetic field intensity value for each measurement point within the theoretical framework. Furthermore, by calculating the residual sum of squares or absolute error between the correction signal and the theoretical value, the system automatically selects the correction signal that best matches the theoretical model as the final calibration basis, optimizing the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the method for preparing a micro high-sensitivity magnetic field sensor of this application. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments will be clearly and completely described below in conjunction with the accompanying drawings. The following embodiments are used to illustrate the present invention. The miniature high-sensitivity magnetic field sensor disclosed in this application includes multiple sensing microprobes and a sensitivity calibration processor. The sensing microprobes can use the following specific hardware in applications:

[0029] Hall effect sensors: Due to their small size, fast response speed, and high sensitivity, they are suitable for detecting weak magnetic fields. For sensing detection microprobes, you can choose the highly sensitive CMOSA130250X series or similar micro Hall effect sensors.

[0030] Magnetoresistive elements (GMR or AMR): Giant magnetoresistive or anisotropic magnetoresistive elements offer higher sensitivity and better temperature stability, making them suitable for precise measurements. Sensing microprobes can use magnetoresistive sensor elements such as TMR sensors, which can provide high-resolution magnetic field measurements in a small footprint.

[0031] Fiber-optic magneto-optical Kerr effect sensors: These sensors utilize optical fiber to transmit light signals, are immune to electromagnetic interference, and offer high sensitivity and long-distance transmission capabilities. Sensor detection microprobes can include integrated fiber Bragg grating magneto-optical Kerr sensors.

[0032] The sensitivity correction processor can use the following specific hardware in the application:

[0033] Microcontroller / microprocessor: ARM Cortex-M series (such as STM32 / STM32, STM32L4) or ESP32 series, with powerful processing capabilities, suitable for real-time data processing and complex algorithm operations.

[0034] DSP (Digital Signal Processor): Designed for high-speed, large-scale data processing, such as TI's TMS328x series, suitable for fast signal filtering and correction calculations.

[0035] The sensing detection micro probe is used to obtain the detected magnetic field signal and send the detected magnetic field signal to the sensitivity calibration processor;

[0036] The sensitivity correction processor is used to calculate at least one deviation signal based on the magnetic field signals detected by the plurality of sensing detection micro-probes,

[0037] A "deviation signal" refers to a signal that significantly deviates from the expected or average magnetic field strength value. This deviation may be caused by local magnetic field disturbances, individual sensor differences, noise interference, or other non-ideal factors. The specific process of "calculating at least one deviation signal based on magnetic field signals detected by multiple sensing microprobes" includes the following steps:

[0038] 1. Data Collection

[0039] Synchronous measurement: All sensing microprobes measure the magnetic field strength in the same magnetic field environment simultaneously or sequentially within a preset time to ensure the temporal and spatial consistency of the data.

[0040] The magnetic field strength values ​​detected by each sensing microprobe are recorded, which may contain noise and measurement errors.

[0041] 2. Mean calculation

[0042] Calculate the average magnetic field strength: Average the magnetic field strength values ​​from all sensing probes to get a representative estimate of the magnetic field strength. This average value should theoretically be closer to the true magnetic field strength because it reduces the impact of random errors.

[0043] 3. Difference Analysis

[0044] Deviation Identification: For each sensor probe's measurement value, the difference (deviation) between it and the average magnetic field strength is calculated. If the difference between a sensor probe's measurement value and the average value exceeds a preset threshold, the sensor probe's measurement value is considered to constitute a "deviation signal."

[0045] The sensitivity correction processor is further configured to calculate at least two correction signals for each deviation signal based on magnetic field signals detected by a plurality of sensing detection microprobes; in the process of calculating at least two correction signals for each deviation signal based on magnetic field signals detected by a plurality of sensing detection microprobes, specifically, for each deviation signal, the measurement values ​​of other sensing detection microprobes relative to the deviation signal are first determined, and the measurement values ​​of sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are selected, and the measurement values ​​of the sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are directly used as the correction signals, or the weighted average of the “measurement values ​​of the sensing detection microprobes adjacent to the sensing detection microprobe generating the deviation signal” and the weighted average of the “measurement values ​​of the sensing detection microprobes symmetrically positioned with the sensing detection microprobe generating the deviation signal” are used as the correction signals;

[0046] The sensitivity correction processor is further used to calculate and select at least one correct correction signal based on at least two correction signals of each deviation signal and a magnetic field intensity distribution rule, wherein the correction signal is used to correct the sensor sensitivity;

[0047] At least one correct correction signal is selected based on at least two correction signals of each deviation signal and the magnetic field intensity distribution law. The correction signal is used to correct the sensor sensitivity. In the specific process, the consistency of each correction signal with the theoretical model is evaluated in combination with the magnetic field distribution model. The magnetic field distribution model is interpolated or fitted to match the actual probe layout and measurement points. At least one correction signal with the best consistency with the theoretical model is selected as the correct correction signal. Specifically,

[0048] 1. Establishment of magnetic field distribution model

[0049] Establish the theoretical magnetic field distribution function B th (x, y, z), this function describes the distribution of the magnetic field in space under ideal conditions, where (x, y, z) represents the spatial coordinates;

[0050] 2. Measured data and deviation signals

[0051] For each sensing microprobe, let its position be (x i ,y i , z i ), the measured magnetic field strength is B meas，i , for each sensing microprobe, the deviation signal is defined as ΔB i =B meas，i -B avg , where B avg is the average of all probe measurements;

[0052] 3. Correction Signal Selection and Evaluation

[0053] For the deviation signal ΔB i , at least two correction signals have been calculated and Specifically, it is the measurement values ​​of adjacent and symmetrical probes or their weighted average;

[0054] 4. Model matching and consistency assessment

[0055] Interpolation / fitting: Use interpolation or fitting to calculate the theoretical magnetic field distribution model B. th Find or generate the theoretical magnetic field strength value B corresponding to each probe position on (x, y, z) th，i ;

[0056] Consistency assessment: For each deviation signal corresponding to the correction signal

[0057] Calculate its difference with the theoretical value B th，i difference measure;

[0058] The residual sum of squares is used Or absolute error

[0059] 5. Optimal correction signal selection

[0060] For each deviation signal, compare the error metrics corresponding to the two correction signals or Select the correction signal with the smallest error as the "correct correction signal". or

[0061] The mathematical expression of the example is as follows:

[0062] Known theoretical model B th (x, y, z) = B0·f(x, y, z), where f(x, y, z) is a position-dependent function, B o is a constant; for the deviation signal ΔB i , the correction signal and Calculate the theoretical value B th，i =B0·f(x i ,y i , z i )choose The mathematical expression can be summarized as: Through this process, combining theoretical models and actual measurements, accurate correction of deviation signals was achieved, improving the measurement accuracy and reliability of the miniature high-sensitivity magnetic field sensor.

[0063] The technical effects of this application are summarized as follows:

[0064] Multi-source fusion and deviation identification: The sensor array utilizes a variety of technologies, including Hall-effect sensors, magnetoresistive elements, and fiber-optic magneto-optical Kerr-effect sensors, ensuring wide-spectrum, highly sensitive magnetic field detection. Through synchronized or sequential measurements, the system captures the immediate response of each probe under identical magnetic field conditions. Based on this data, it can identify deviation signals caused by local disturbances, device differences, or environmental noise. This is the first step to improving measurement accuracy.

[0065] Intelligent Correction Algorithm: The core of the algorithm lies in its unique deviation signal correction mechanism, which not only calculates at least one deviation signal but also generates at least two candidate correction signals for each deviation signal. This mechanism is based on the measurement values ​​of adjacent and symmetrical probes or their weighted average. It cleverly exploits the symmetry and proximity of the probe layout, using reliable measurements from physically close proximity to correct deviations, significantly enhancing the data's adaptive correction capabilities.

[0066] Combining theoretical models with practical optimization: The key to this technical solution lies in the tight coupling of the theoretical magnetic field distribution model with actual measurement data. Using interpolation or fitting techniques, the optimal theoretical magnetic field intensity value is matched to each measurement point within the theoretical framework. Furthermore, by calculating the residual sum of squares or absolute error between the correction signal and the theoretical value, the system automatically selects the correction signal that best aligns with the theoretical model as the basis for final calibration. This dynamic optimization process ensures the scientific and effective nature of the calibration, significantly reducing errors introduced by sensor non-idealities.

[0067] Improving measurement accuracy and reliability: The entire technical solution, from hardware selection to software algorithm design, is centered around improving the accuracy and reliability of magnetic field measurements. This multi-level, multi-dimensional signal processing and correction strategy effectively suppresses external interference and optimizes measurement results. This enables the miniature, high-sensitivity magnetic field sensor to provide more accurate and stable magnetic field information in a variety of fields, including scientific research, medical testing, and industrial monitoring, meeting the growing demand for high-precision magnetic field measurements.

[0068] This application also discloses a method for preparing a micro high-sensitivity magnetic field sensor, referring to Figure 1 , including the steps of:

[0069] 1. Microprobe array design and integration

[0070] Design and Selection: First, carefully design the sensor detection microprobe array layout based on the application scenario. Select appropriate types of Hall-effect sensors, GMR / AMR magnetoresistive elements, and fiber-optic magneto-optical Kerr-effect sensors to achieve wide-spectrum, high-sensitivity magnetic field detection capabilities. For example, for weak magnetic field detection, the highly sensitive CMOS A130250X series Hall-effect sensors or TRAMR magnetoresistive sensor elements are preferred.

[0071] 2. Sensitivity correction processor development and programming

[0072] Hardware configuration: Select a high-performance microcontroller / microprocessor (such as the STM32 series) or DSP (such as the TI TMS328x series), and customize the hardware platform based on processing speed and power consumption requirements to ensure efficient operation of real-time data analysis and correction algorithms.

[0073] Write customized firmware to implement the following functions:

[0074] Synchronous data acquisition: Programming implements synchronous or sequential control logic to ensure that all probes collect data at the same time or in a predetermined sequence, ensuring the time consistency of the data.

[0075] Deviation signal recognition: Automatically recognize and record deviation signals.

[0076] Correction signal calculation: Implements the calculation logic of the correction signal, including direct adoption or weighted averaging of adjacent and symmetrical probe measurement values.

[0077] Model matching: Combines the magnetic field distribution model to perform data fitting and interpolation, evaluates the consistency of the correction signal with the theoretical model, and automatically selects the optimal correction signal.

[0078] 3. Construction and optimization of magnetic field distribution model

[0079] Theoretical model establishment: Establish a magnetic field distribution model and adjust the model parameters according to the actual sensor array layout to ensure that the model is highly consistent with the actual application scenario.

Claims

1. A miniature high-sensitivity magnetic field sensor, characterized in that: The system comprises a plurality of sensing detection micro-probes and a sensitivity calibration processor, wherein the sensing detection micro-probes are used to obtain detected magnetic field signals and send the detected magnetic field signals to the sensitivity calibration processor; The sensitivity correction processor is used to calculate at least one deviation signal based on the magnetic field signals detected by the multiple sensing detection micro-probes, and the sensitivity correction processor is further used to calculate at least two correction signals for each deviation signal based on the magnetic field signals detected by the multiple sensing detection micro-probes; The sensitivity correction processor is further used to calculate and select at least one correct correction signal based on at least two correction signals of each deviation signal and a magnetic field intensity distribution rule, wherein the correction signal is used to correct the sensor sensitivity; In the specific process of correcting the sensor sensitivity, the consistency of each correction signal with the theoretical model is evaluated in combination with the magnetic field distribution model, the magnetic field distribution model is interpolated or fitted to match the actual probe layout and measurement points, and at least one correction signal with the best consistency with the theoretical model is selected as the correct correction signal; Combined with the magnetic field distribution model, the consistency of each correction signal with the theoretical model is evaluated, the magnetic field distribution model is interpolated or fitted to match the actual probe layout and measurement points, and at least one correction signal with the best consistency with the theoretical model is selected as the correct correction signal. Specifically, the following steps are performed: Establishing a theoretical magnetic field distribution function , which describes the distribution of the magnetic field in space under ideal conditions, where (X, Y, Z) represents the spatial coordinates; for each sensing microprobe, let its position be , the measured magnetic field strength is B meas,i ,For each sensing microprobe, the deviation signal is defined as , where B avg is the average of all probe measurements; For the deviation signal , at least two correction signals have been calculated and , specifically the measurements of adjacent and symmetrical probes or their weighted average; Use interpolation or fitting in theoretical magnetic field distribution models Find or generate the theoretical magnetic field strength value B corresponding to each probe position th,i ; For each deviation signal corresponding to the correction signal , , Calculate its difference with the theoretical value B th,i difference measure; The residual sum of squares is used Or absolute error ; For each deviation signal, compare the error metrics corresponding to the two correction signals: or , select the correction signal with the smallest error as the "correct correction signal", that is, or .

2. The miniature high-sensitivity magnetic field sensor according to claim 1, characterized in that: The sensing detection micro probe adopts Hall effect sensor, magnetoresistive element and fiber optic magneto-optical Kerr effect sensor.

3. The miniature high-sensitivity magnetic field sensor according to claim 1, characterized in that: The sensitivity correction processor adopts a microcontroller / microprocessor and a DSP.

4. The miniature high-sensitivity magnetic field sensor according to claim 1, characterized in that: The specific process of "calculating at least one deviation signal based on magnetic field signals detected by a plurality of sensing microprobes" includes the following steps: All the sensing micro-probes measure the magnetic field strength in the same magnetic field environment simultaneously or sequentially within a preset time; and record the magnetic field strength value detected by each sensing micro-probe; The magnetic field strength values ​​of all sensing microprobes are averaged to obtain a representative magnetic field strength estimate; for each sensing microprobe's measurement value, the difference between it and the average magnetic field strength is calculated. If the difference between the measurement value of a sensing microprobe and the average value exceeds a preset threshold, the measurement value of the sensing microprobe is considered to constitute a "deviation signal." 5. The miniature high-sensitivity magnetic field sensor according to claim 1, characterized in that: In the process of calculating at least two correction signals for each deviation signal based on the magnetic field signals detected by multiple sensing detection microprobes, specifically, for each deviation signal, the measurement values ​​of other sensing detection microprobes relative to the deviation signal are first determined, and the measurement values ​​of the sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are selected. The measurement values ​​of the sensing detection microprobes adjacent to and symmetrically positioned with the sensing detection microprobe generating the deviation signal are directly used as the correction signals, or the weighted average of the "measurement values ​​of the sensing detection microprobes adjacent to the sensing detection microprobe generating the deviation signal" and the weighted average of the "measurement values ​​of the sensing detection microprobes symmetrically positioned with the sensing detection microprobe generating the deviation signal" are used as the correction signal.

6. The method for preparing a micro high-sensitivity magnetic field sensor according to claim 1, characterized in that: Including steps: Choose Hall effect sensors, magnetoresistive elements, fiber optic magneto-optical Kerr effect sensors, and write customized firmware using a microcontroller / microprocessor or DSP to achieve the following functions: Implement synchronous or sequential control logic to ensure that all probes collect data at the same time or in a predetermined sequence to ensure the time consistency of the data; automatically identify and record deviation signals; implement the calculation logic of the correction signal, including the direct use or weighted average of the measurement values ​​of adjacent and symmetrical probes; combine the magnetic field distribution model to perform data fitting and interpolation, evaluate the consistency of the correction signal with the theoretical model, and automatically select the optimal correction signal.

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