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ispartof: pages:73-76 ispartof: ESSCIRC 2022- IEEE 48TH EUROPEAN SOLID STATE CIRCUITS CONFERENCE (ESSCIRC) pages:73-76 ispartof: 48th IEEE European Solid State Circuits Conference (ESSCIRC) location:ITALY, Milan date:19 Sep - 22 Sep 2022 status: published

In this paper, the self-calibration of micromechanical acceleration sensors is considered, specifically, based solely on user-generated movement data without the support of laboratory equipment or external sources. The autocalibration algorithm itself uses the fact that under static conditions, the squared norm of the measured sensor signal should match the magnitude of the gravity vector. The resulting nonlinear optimization problem is solved using robust statistical linearization instead of the common analytical linearization for computing bias and scale factors of the accelerometer. To control the forgetting rate of the calibration algorithm, artificial process noise models are developed and compared with conventional ones. The calibration methodology is tested using arbitrarily captured acceleration profiles of the human daily routine and shows that the developed algorithm can significantly reject any misconfiguration of the acceleration sensor.

A new low-cost process for mid resolution FIR-Arrays for thermal imaging in the LWIR range (8- 14 μm) has been developed. Integrated array devices with 1k and 5k pixel array size have been designed and manufactured using this process which is fully compatible with semiconductor production. The devices are suitable to support detection of humans e.g. in security and automotive safety applications.