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Abstract
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.
Highlights
Attributes of microelectromechanical systems (MEMS) include a wide variety of transduction capabilities in packages that are small in size, weight, power, and cost
The benefits of self-calibration are expected to: (1) reduce the cost of MEMS devices since the costly expense of in-factory calibration can be reduced or eliminated, which should increase manufacturing throughput; (2) greatly extend the usefulness of sensors since devices will be able to re-calibrate after long-term dormancy or after harsh environmental change; (3) improve the quality of data being analyzed in terms of increased accuracy and reduced uncertainty; and (4) close the large gap in going from experiment back to simulation, to build experimentally-accurate predictive computer models of Internet of Things (IoT) sensors and of their environments
electro micro metrology (EMM) can be used to recalibrate inertial measurement unit (IMU) as temperatures change, and because the equation of motion is known, the forces due to movement within a non-inertial reference frame can be accurately measured. These forces include: (1) the Coriolis force due to the proof mass M moving with velocity r = x in a frame that is rotating with frequency vector ω: FCoriolis = −2M ω × r; (2) the Euler force due to M located at a displacement vector r from the point of rotation of a nonconstant frequency vector ω that is changing in magnitude and or direction: FEuler = − M ω × r ; (3) the centrifugal force on MEMM
Summary
Attributes of microelectromechanical systems (MEMS) include a wide variety of transduction capabilities in packages that are small in size, weight, power, and cost. Multimode sensors at the wafer- or chip-level, which bottlenecks throughput, increases cost, and impedes design improvements Such difficulties require designers to consider the back-end of manufacturing issues, such as packaging and testing, at the beginning of the design process. Most measurement methods are functions of one or more quantities that do not have a standard, are not well-measured, or are found in a look-up table Such issues yield large uncertainties (>10%) and unknown accuracy.
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