Towards no-drift sensors with on-chip self-calibration

Sensor drift is a major problem for inertial sensors and limits their usage in autonomous navigation applications. Inertial sensor data is integrated to find the position and drift leads to error accumulation. A common drift suppression approach is temperature calibration, but ovenized state of the art sensors still exhibit drift. Instead of using temperature as a drift indicator, I have pursued a non-conventional approach and measured on-chip stress that directly correlates with drift. The device interacts with its surroundings through the anchors and on-chip stress accurately estimates drift. I am the leading researcher in the stress compensation field, and I have recently demonstrated that MEMS gyroscope drift could be eliminated with stress compensation. My long-term stability results at 2 days of averaging are unrivaled, but the calibration algorithm is not practical. Different from temperature calibration, stress calibrating a device is difficult. I propose a sensor system that would convert my proof of concept work into a practical 0-drift sensor with self-calibration. The proposed system consists of a circular MEMS sensor with multiple (~100) distributed stress sensors and piezoelectric stress transducers, a machine learning supported analytical calibration model, a custom ASIC for superior noise, and an FPGA for system control and self-calibration. If successful, the proposed approach would improve the MEMS gyroscope stability by >100X to the levels of 10-4 10-5/h, enabling error-free, only gravity-referenced inertial navigation. Unlike GPS or camera, inertial navigation works under all weather, light, and location conditions providing a stable reference to navigation algorithms. With further miniaturization, 0-drift sensors could fit into smartphones, and reliable indoor navigation would become a reality. The compact, low-cost sensor could also disrupt the precision inertial market dominated by bulky and expensive fiber-optic and laser sensors.