Lightweight Thermal Compensation Technique for MEMS Capacitive Accelerometer Oriented to Quasi-Static Measurements

Abstract

The application of MEMS capacitive accelerometers is limited by its thermal dependence, and each accelerometer must be individually calibrated to improve its performance. In this work, a light calibration method based on theoretical studies is proposed to obtain two characteristic parameters of the sensor’s operation: the temperature drift of bias and the temperature drift of scale factor. This method requires less data to obtain the characteristic parameters, allowing a faster calibration. Furthermore, using an equation with fewer parameters reduces the computational cost of compensation. After studying six accelerometers, model LIS3DSH, their characteristic parameters are obtained in a temperature range between 15 °C and 55 °C. It is observed that the Temperature Drift of Bias (TDB) is the parameter with the greatest influence on thermal drift, reaching 1.3 mg/°C. The Temperature Drift of Scale Factor (TDSF) is always negative and ranges between 0 and −400 ppm/°C. With these parameters, the thermal drifts are compensated in tests with 20 °C of thermal variation. An average improvement of 47% was observed. In the axes where the thermal drift was greater than 1 mg/°C, the improvement was greater than 80%. Other sensor behaviors have also been analyzed, such as temporal drift (up to 1 mg/h for three hours) and self-heating (2–3 °C in the first hours with the corresponding drift). Thermal compensation has been found to reduce the effect of the latter in the first hours after power-up of the sensor by 43%.

Factory Oriented Technique for Thermal Drift Compensation in MEMS Capacitive Accelerometers

“…When the device is powered on, its temperature rises, due to internal ohmic losses [7]. This self-heating effect forces a delay between the device powering up and the test start, as some uncontrolled thermal variations appear.…”

“…Finally, the characteristic parameters TDSF and TDB can be computed for each axis using (7) and (8), respectively.…”

“…Software compensation can be achieved with multiple techniques: surfaces [4], curves [5], splines [6], behavior models [7] or neural networks [8]. However, all these techniques require the device under test (DUT) to be analyzed in multiple orientations and temperatures in order to obtain the compensation parameters, using laboratory equipment and software as standard.…”

“…The thermal behavior of a MEMS capacitive accelerometer can be modeled with two characteristic parameters, Equation (1): the Temperature Drift of Bias (TDB) and the Temperature Drift of Scale Factor (TDSF) [7]. Using this model for thermal compensation reduces the number of parameters that need to be computed for each unit.…”

Tilt Sensor with Recalibration Feature Based on MEMS Accelerometer

“…Despite numerous advantages of these microsensors, including low price, robustness, small size, high shock-resistance and easy integration with electronics, there are a few disadvantages that characterize their performance. These are mainly: misalignments of the sensitivity axes (including their non-perpendicularity) [8]; thermal and long-term drifts of the output signals (affecting both the offset and the scale factor) [9]; errors related to factory calibration of the accelerometer and aging of the silicon structure [8]; and attenuation of amplitude and phase shift over frequency [10].…”