In this paper a novel electrostatic MEMS combined shock sensor and normally-closed switch is presented. The switch uses combined attractive and repulsive forcing to toggle a cantilever beam to and from the pulled-in position. The attractive force is generated through a parallel plate electrode configuration and induces pull-in. The repulsive force is generated through electrostatic levitation from a third electrode and serves to pull the beam out of its pulled-in position. A triboelectric transducer converts impact energy to electrical energy to provide voltage for the third electrode, which temporarily opens the switch if enough impact energy is supplied. Triboelectricity addresses the high voltage requirement for electrostatic levitation. The multi-electrode sensor also addresses the low current output from the generator because it acts as an open circuit between the parallel plate and levitation electrodes. A theoretical model of the switch is derived to analyze stability and the dynamic response of the cantilever. Threshold voltages to pull-in and release the beam through repulsive forcing is calculated. Output voltage plots from a prototype generator under a single impact are applied to the sensor-switch model to demonstrate the working principle of the sensor-switch is feasible.
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Enhancing open-loop control of MEMS using linear electrostatic levitation actuators
MEMS electrostatic actuators are used in optical applications because of their small size and quick response. However, nonlinearities of electrostatic force, long settling-time and small range of motion significantly hampers their performance. Adding electrostatic levitation to MEMS parallel-plate mechanism, we achieved a wide linear operation region away from the center electrode. Because of linearity, command-shaping becomes an easy and effective method to decrease the settling-time and overshoot. Compared to the conventional parallel-plate electrodes, we have shown a considerable increase in the travel range of levitating electrodes using double-step command signals.
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- Award ID(s):
- 1919608
- PAR ID:
- 10327404
- Date Published:
- Journal Name:
- 2021 IEEE Sensors
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We introduce a capacitive MEMS filter that uses electrostatic levitation for actuation and sensing. The advantage of this electrode configuration is that it does not suffer from the pull-in instability and therefore tremendously high voltages can be applied to this system. A large sensing voltage will produce a large output signal, which boosts the signal to noise ratio. The filter outputs about a 110mV peak-to-peak signal when operated at 175V, and can be boosted to 175mV by increasing the voltage to 250V. Because pull-in is eliminated, voltages much higher than 250V can be applied. An outline of the filter design and operating principle is discussed. A model of the filter is derived and analyzed to show the mechanical response and approximate peak-to-peak signal output. This study shows the feasibility of a capacitive sensor that is based on electrostatic levitation, and outlines the advantages it has over traditional parallel-plate electrode configurations. This design is promising for signal signal processing applications where large strokes are important.more » « less
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Abstract The size and power limitations in small electronic systems such as wearable devices limit their potential. Significant energy is lost utilizing current computational schemes in processes such as analog-to-digital conversion and wireless communication for cloud computing. Edge computing, where information is processed near the data sources, was shown to significantly enhance the performance of computational systems and reduce their power consumption. In this work, we push computation directly into the sensory node by presenting the use of an array of electrostatic Microelectromechanical systems (MEMS) sensors to perform colocalized sensing-and-computing. The MEMS network is operated around the pull-in regime to access the instability jump and the hysteresis available in this regime. Within this regime, the MEMS network is capable of emulating the response of the continuous-time recurrent neural network (CTRNN) computational scheme. The network is shown to be successful at classifying a quasi-static input acceleration waveform into square or triangle signals in the absence of digital processors. Our results show that the MEMS may be a viable solution for edge computing implementation without the need for digital electronics or micro-processors. Moreover, our results can be used as a basis for the development of new types of specialized MEMS sensors (ex: gesture recognition sensors).
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null (Ed.)The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.more » « less
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Parametric resonances in a repulsive-force MEMS resonator are investigated. The repulsive force is generated through electrostatic fringe fields that arise from a specific electrode configuration. Because of the nature of the electrostatic force, parametric resonance occurs in this system and is predicted using Mathieu’s Equation. Governing equations of motion are solved using numerical shooting techniques and show both parametric and subharmonic resonance at twice the natural frequency. The primary instability tongue for parametric resonance is also mapped. This is of particular interest for MEMS sensors that require high signal-to-noise ratios due to the large oscillation amplitudes associated with parametric resonance.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SENSORS47087.2021.9639780
