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Gyroscope calibration tips

What type of information do you have on calibrating a MEMS gyroscope?

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  • Also, just so that you are aware, ADI offers discrete and fully-integrated gyroscope solutions. The ADXRS620 and ADXRS646 are examples of discrete MEMS gyroscopes that provide high-quality, analog outputs. The ADIS16265 and ADIS16136 are examples of fully-integrated, digital MEMS gyroscopes, which include unit-calibration that addresses bias and sensitivity errors, with respect to temperature and power supply. The following table offers some insights on the parameters that are impacted the most, by a factory-calibration process.

    Parameter ADXRS620 ADIS16265 ADXRS646 ADIS16136
    Sensitivity Temperature Coefficient (ppm per degC) ~258 25 387 35
    Bias Temperature Coefficient (deg/sec per degC) ~0.1 0.005 0.1 0.00125

    NOTE: ADXRS620 and ADXRS646 numbers were calculated using typical parameters in their datasheets and under the assumption of a linear relationship across the temperature range. This was for comparison purposes only and does not imply a purely linear relationship between bias and temperature. We have found that there can be value in higher-order, polynomial fits for this relationship, along with observing behaviors at multiple temperatures.

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  • Also, just so that you are aware, ADI offers discrete and fully-integrated gyroscope solutions. The ADXRS620 and ADXRS646 are examples of discrete MEMS gyroscopes that provide high-quality, analog outputs. The ADIS16265 and ADIS16136 are examples of fully-integrated, digital MEMS gyroscopes, which include unit-calibration that addresses bias and sensitivity errors, with respect to temperature and power supply. The following table offers some insights on the parameters that are impacted the most, by a factory-calibration process.

    Parameter ADXRS620 ADIS16265 ADXRS646 ADIS16136
    Sensitivity Temperature Coefficient (ppm per degC) ~258 25 387 35
    Bias Temperature Coefficient (deg/sec per degC) ~0.1 0.005 0.1 0.00125

    NOTE: ADXRS620 and ADXRS646 numbers were calculated using typical parameters in their datasheets and under the assumption of a linear relationship across the temperature range. This was for comparison purposes only and does not imply a purely linear relationship between bias and temperature. We have found that there can be value in higher-order, polynomial fits for this relationship, along with observing behaviors at multiple temperatures.

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