A CMOS Hall-based Magnetic Multisensor System Free from Parasitic Effects of Temperature and Package Stress

Huber, Samuel; Lazăr, Z; Berger, Moritz; Oliver, Paul

doi:10.1109/transducers.2019.8808322

Cited by 2 publications

(1 citation statement)

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“…The cointegration of temperature and stress sensors together with a Hall sensor has enabled the analog compensation of the cross-sensitivities [3], [62], [63]. Alternatively, the digital signal processing of the sensor signals has also allowed to obtain Hall sensor signals largely cleared of the parasitic contributions [3], [45], [54], [64], [65]. As stated in [45], accuracies better than 1% for the temperature range required by automotive applications can be achieved only by compensating the long-term drift of the Hall sensitivity associated with mechanical stress.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities

Berger

Schott²,

Oliver

2023

IEEE Sensors J.

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For the first time, Bayesian sensor calibration is used to identify efficient calibration procedures for a sensor cross-sensitive to 2 parasitic influences. The object under study is a thermomechanically cross-sensitive sensor system for determining the magnetic induction B. The packaged system comprises a Hall sensor, a stress sensor, and a temperature sensor. The three sensor signals are combined in a polynomial sensor response model with 11 parameters to determine B compensated for offset and cross-sensitivities. For the calibration, sensors are exposed to mechanical stress values between 0 and −68 MPa, temperatures between −40 and 100 °C, and B values between and 25 mT. A sample of 35 sensors serves to extract the prior model parameter distribution of their fabrication run. Bayesian experimental design is applied to identify sets of 2 to 8 optimal calibration conditions under Ioptimality and G-optimality. Bayesian inference then allows to obtain the posterior model parameter distribution of any uncalibrated sensor from the same run. Any such sensor is thereby turned into a B measuring device with individually quantified accuracy. The method was successfully applied to 15 validation sensors. In the case of I-optimality, the median root-mean-square (rms) σ values of the ±1σ confidence intervals for the extracted B values were found to be 113 to 71 µT after near-I-optimal calibrations based on 2 to 8 measurements, respectively. Over the entire range of temperature and mechanical stress and for applied |B| ≤25 mT, corresponding experimentally determined medians of the rms deviations between predicted and applied B values were found to be 89 to 71 µT. Analogous observations apply to G-optimality. In short, Bayesian calibration made it possible to obtain functional B sensors of known accuracy with significantly fewer calibration measurements than model parameters. This was enabled by prior knowledge collected by the thorough characterization of 35 prior-generating specimens.

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