Self-Calibration and Performance Control of MEMS with Applications for IoT

A systemic problem for microelectromechanical systems (MEMS) has been the large gap between their predicted and actual performances. Due to process variations, no two MEMS have been able to perform identically. In-factory calibration is often required, which can represent as much as three-fourths of the manufacturing costs. Such issues are challenges for microsensors that require higher accuracy and lower cost. Towards addressing these issues, this paper describes how microscale attributes may be used to enable MEMS to accurately calibrate themselves without external references, or enable actual devices to match their predicted performances. Previously, we validated how MEMS with comb drives can be used to autonomously self-measure their change in geometry in going from layout to manufactured, and we verified how MEMS can be made to increase or decrease their effective mass, damping, and or stiffness in real-time to match desired specifications. Here, we present how self-calibration and performance control may be used to accurately sense and extend the capabilities of a variety of sensing applications for the Internet of things (IoT). Discussions of IoT applications include: (1) measuring absolute temperature due to thermally-induced vibrations; (2) measuring the stiffness of atomic force microscope or biosensor cantilevers; (3) MEMS weighing scales; (4) MEMS gravimeters and altimeters; (5) inertial measurement units that can measure all four non-inertial forces; (6) self-calibrating implantable pressure sensors; (7) diagnostic chips for quality control; (8) closing the gap from experiment to simulation; (9) control of the value of resonance frequency to counter drift or to match modes; (10) control of the value of the quality factor; and (11) low-amplitude Duffing nonlinearity for wideband high-Q resonance.