Magnetic Sensor System Using Machine Learning Calibration
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Solution Overview
Problem
Magnetic sensor systems face challenges in accurately measuring multiple physical quantities, such as force components or tilting angles, while being less sensitive to external disturbance fields and temperature variations, without requiring complex analytical formulas or explicit mathematical expressions.
Innovation Solution
A magnetic sensor system comprising an integrated circuit with multiple magnetic sensors and a movable permanent magnet, using a predefined algorithm that processes magnetic field components to determine physical quantities, with constants determined by machine learning, and employing an elastomer for mechanical mounting to reduce sensitivity to external disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple magnetic sensors are used to measure multiple physical quantities, then measurement capability is improved, but sensitivity to external disturbance fields increases
Solution Approach 1:
The system segments the measurement task by using multiple magnetic sensors positioned at different locations around the permanent magnet. Each sensor measures magnetic field components in specific directions, and the processing circuit combines these segmented measurements to determine multiple physical quantities (force components, tilting angles) while rejecting external disturbance fields through differential measurement techniques.
Solution Approach 2:
The system converts the harmful effect of external disturbance fields into a beneficial differential measurement approach. By measuring magnetic field components at multiple sensor locations and calculating differences between these measurements, the system eliminates common-mode external disturbances while preserving the differential signal containing the desired physical quantity information.
2Measurement precision
If complex analytical formulas are used to determine physical quantities, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The system replaces complex analytical mathematical models with a data-driven machine learning approach. During a calibration phase, the system learns the relationship between sensor measurements and physical quantities by exposing the permanent magnet to known forces and orientations. The processing circuit then uses this learned model to determine physical quantities from sensor readings without requiring explicit analytical formulas, reducing computational complexity while maintaining accuracy.
3Reliability
If magnetic sensors are made robust against temperature variations, then reliability is improved, but manufacturing precision requirements increase
Solution Approach 1:
The system performs preliminary calibration during manufacturing or initial operation to compensate for temperature effects and positioning variations. The calibration process characterizes the sensor system's response under controlled conditions, storing correction data that the processing circuit applies during normal operation. This preliminary action eliminates the need for extremely tight manufacturing tolerances while ensuring reliable temperature-compensated measurements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively measures multiple physical quantities with high accuracy and robustness against external disturbances and temperature variations, achieving reliable results without the need for explicit analytical models.
Implementation Method 1
a permanent magnet which is movable relative to the integrated circuit, and configured for generating a magnetic field
Implementation Method 2
a plurality of magnetic sensors configured for measuring at least two first magnetic field components (Bx1, Bx2) oriented in a first direction (X), and for measuring at least two second magnetic field components (Bz1, Bz2) oriented in a second direction (Y; Z)
Data Source
AI summary
A magnetic sensor system includes: an integrated circuit having a semiconductor substrate, which has a plurality of magnetic sensors configured for measuring at least two first magnetic field components oriented in a first direction, and for measuring at least two second magnetic field components oriented in a second direction; a permanent magnet movable relative to the integrated circuit and configured for generating a magnetic field; a processing circuit configured for determining at least two physical quantities related to a position of the magnet, using a predefined algorithm based on the measured first and second magnetic field components or values derived therefrom, as inputs, and that uses a plurality of at least eight constants which are determined using machine learning. A force sensor system, a joystick or thumbstick system, and a method employ features of the magnetic sensor system.