In biology, mechanosensors, equipped with differing hair-like structures for signal pick-up, are sensitive to a variety of physical quantities like acceleration, flow, rotational rate, balancing and IR-light. As an example, crickets use filiform hairs for sensing of low-frequency flows to obtain information about the environment and avoid e.g. predator attacks crickets. Their filiform hairs are able to sense airflows with velocity amplitudes down to 30 um/s and operate around the energy levels of thermal noise. Taking these hair-sensors as a source of inspiration, hair-sensor inspired flow-sensors for measurement of (tiny) ac-airflows using capacitive readout have been designed and fabricated using technology generally denoted as MEMS (microelectromechanical systems). To determine the performance of these flow sensitive hair-sensors, three different setups for oscillatory airflow are used for thorough characterization. Each of these flow sources has specific properties regarding frequency range, pressure dependence and bandwidth. By combining information from the used flow setups important insights in the sensor operation are gained and discussed. To improve the performance of these hair flow sensors, the nature of energy-buffering two-port transducers is exploited for implementation of electrostatic spring softening (ESS). On the application of a dc-bias voltage on the capacitors of our flow sensors, both an increase in responsivity for frequencies within the sensor's bandwidth and a lower flow velocity threshold are obtained. Changing the dc-bias voltage to an ac-bias voltage, non-resonant parametric amplification and filtering has been demonstrated in our hair flow sensors. By selecting appropriate values for the ac-bias voltage, selective gain and filtering is achieved. On application of an appropriate sinusoidal voltage on the capacitor plates, upconversion of the flow information is achieved when the flow frequency is much lower than the voltage frequency resulting in electromechanical amplitude modulation (EMAM). It is demonstrated that EMAM can improve the measurement performance at low frequencies, in case of limitations within the measurement setup. This method can be applied equally well to transients as to harmonic signals. Under certain conditions, noise can be used to increase signal-to-noise ratio by exploiting the concept of stochastic resonance (SR). This concept is demonstrated using a voltage-controlled MEMS-slider, the signal-to-noise ratio can be increased by adding white noise. SR is implemented by controlling the strength of positiondependent capacitive wells by a dc-bias voltage, operating the device in push-pull mode by electrostatic actuation and adding a judiciously amount of white noise to the actuation comb drives. It is demonstrated that the use of SR allows for detection of sub-threshold forces and that the noise bandwidth has a clear impact on the required optimal noise strength. Further, three different types of bio-inspired inertial sensors have been developed. First, a biomimetic accelerometer has been realized using surface micromachining and SU-8 lithography, inspired by the clavate hair system of the cricket. Second, inspired by the fly's haltere, a biomimetic gimbal-suspended hair-based gyroscope has been designed, fabricated and partially characterized. Third, an angular accelerometer based on the semicircular channels of the vestibular system has been developed. The accelerometer consists of a water-filled tube, wherein the fluid flow velocity is measured thermally as a representative for the external angular acceleration. For all three sensors, the necessary models are presented and guidelines are derived for optimization. Also, their performance is compared to their biological counterpart and its biomimetic potential is discussed. In quantifying the performance of our MEMS hair flow sensors and comparing it to their source of inspiration, five independent metrics and a figure of merit are described, modelled and evaluated for both cricket and MEMS hair sensors. In general, cricket flow sensors perform not only better than the MEMS hair sensors, but are also close to operation at their physical limits. The results emphasize the intriguing research on bio-inspired sensors in order to learn from nature.
|Award date||28 Feb 2014|
|Place of Publication||Enschede|
|Publication status||Published - 28 Feb 2014|
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