Development of Doped Silicon Multi-Element Stress Sensor Rosette With Temperature Compensation

Kayed, Mohammed O.; Balbola, Amr A.; Lou, Edmond; Moussa, Walied A.

doi:10.1109/jsen.2019.2948129

Cited by 7 publications

(9 citation statements)

References 22 publications

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“…(i) Sensors responding linearly to a single measurand, such as diode-based temperature sensors with output proportional to absolute temperature [4], [73], [74]. (ii) Sensors with linear response experiencing temperature cross-sensitivity, such as diffused piezoresistive stress sensors [75], force/moment sensors based on piezoresistive field-effect transistors [76], [77], and pressure sensors [78], [79]. A pressure sensor case was already successfully analyzed in this spirit [8].…”

Section: A Comments On the Present Gaussian Approachmentioning

confidence: 99%

Bayesian Sensor Calibration

Berger

Schott²,

Oliver

2022

IEEE Sensors J.

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The calibration of multisensor systems can cause significant costs in terms of time and resources, in particular when crosssensitivities to parasitic influences are to be compensated. Successful calibration ensures the trustworthy subsequent operation of a sensor system, guaranteeing that one or several measurands of interest can be inferred from its output signals with specified uncertainty. As the present study shows, this goal can be reached by reduced calibration procedures with fewer calibration conditions than parameters needed to model the device response. This is achieved using Bayesian inference by combining calibration data of a sensor system with statistical prior information about the ensemble to which it belongs. Optimal reduced sets of calibration conditions are identified by the method of Bayesian experimental design. The method is demonstrated on a Hall-temperature sensor system whose nonlinear response model requires seven parameters in the temperature range between -30 °C and 150 °C and for magnetic field values B between -25 mT and 25 mT. For the prior, a multivariate normal distribution of the model parameters is acquired using 14 specimens of the sensor ensemble. I-optimal calibration at one, two, and three temperatures reduces the rms standard deviation of B inferred from sensor output signals from 203 µT before calibration down to 78 µT, 41 µT, and 34 µT, respectively. Similar conclusions apply to G-optimal calibration. The paper describes how to implement the Bayesian prior acquisition, inference, and experimental design. The proposed approach can help save resources and cut costs in sensor calibration.

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Section: A Comments On the Present Gaussian Approachmentioning

confidence: 99%

Bayesian Sensor Calibration

Berger

Schott²,

Oliver

2022

IEEE Sensors J.

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“…There is no fundamental restriction regarding the set of basis functions ϕ(x). Sensor types that may benefit from Bayesian sensor calibration in the present form include ion-selective chemical sensors [90], [91], inertial sensors [22], [23], [92], and mechanical sensors [5], [8], [10], [17], [18], [93]. By using the method of multivariate Bayesian regression and inference [77], [88], we expect the method to be generalizable to multisensor systems designed to provide values of more than a single measurand.…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, we define V = (v − (V S , V T ), v + (V S , V T )). Thereupon, σ 1 (v, V ) is obtained using ( 8) and (10). This then serves to evaluate f G (V ) and f I (V ) (cf.…”

Section: Calibration At Near-optimal Stress-temperature Conditionsmentioning

confidence: 99%

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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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“…To reduce the temperature effect on the accuracy of PR sensors readout, different techniques were developed utilizing software schemes [23][24][25], new designs [26,27], and fabrication processes [28,29] for PR stress sensor. For doped silicon-based PR 3D stress sensors, all the developed temperature compensation techniques, to the authors' knowledge, are only to compensate for the thermal effect on the resistance [7,11,30]. This is considered partial temperature compensation since the temperature has an impact on the sensitivity (i.e.…”

Section: Introductionmentioning

confidence: 99%

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

Kayed

Balbola

Lou

et al. 2021

J. Micromech. Microeng.

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This paper presents the microfabrication and testing of a membrane-free eight-element single-polarity (n-type) sensing rosette integrated with strained silicon technology over (111) silicon plane to measure the full 3D stress/strain tensor with full temperature compensation. Such n-type piezoresistive (PR) sensor has low sensitivity to the out-of-plane components compared to the in-plane components. To improve the sensitivity of such sensors to the out-of-plane components, a strained silicon technique was integrated into the sensing rosette during the microfabrication process using a highly compressive film produced by plasma enhanced chemical vapor deposition silicon nitride. For experimental verification, a prototype device featuring the proposed sensing rosette was microfabricated using semiconductors fabrication processes. The experimental analysis applied both, in-plane and out-of-plane stresses at different temperatures over a range from −20 °С to 60 °С. In this work, a smart sensing calibration algorithm, utilizing machine learning, is employed to reduce the temperature impact on both sensitivity and resistance of PR coefficients during stress measurement. The developed sensor is capable of accurately extracting the applied stress/strain components with temperature compensation.

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Development of Doped Silicon Multi-Element Stress Sensor Rosette With Temperature Compensation

Cited by 7 publications

References 22 publications

Bayesian Sensor Calibration

Bayesian Sensor Calibration

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

Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation

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