MEMS Technology for Adaptive Sensors

This research presents the design, development, and experimental validation of a bio-inspired MEMS-based acoustic sensor with dynamically tunable resonance frequencies. The objective is to develop a sensor capable of efficiently analyzing and processing information directly at the sensor level. Inspired by human auditory system, the sensor mimics the ear’s ability to selectively respond to different frequencies with high sensitivity. Dynamic tuning allows a wider frequency spectrum within a single sensor beam, reducing the number of sensors required.

Two tuning approaches are demonstrated:
(i) Geometric nonlinearity
(ii) Ni-Ti shape memory alloys (SMAs)

In the first approach, clamped-clamped micromechanical beam achieves up to 25% resonance-frequency tunability with low bias voltages (<1 V). DC actuation modifies the beam’s pre-deflection, shifting its resonance frequency. A nonlinear dynamic model based on the Duffing equation describes system behavior, including hardening and softening effects. Experiments validate the sensor’s ability to compensate for fabrication variations and perform frequency-component analysis of sound signals. In the second approach, Ni-Ti SMA thin films deposited on the sensor’s backside enable reversible frequency shifts via temperature-driven phase transformations, achieving up to 25% tunability. The shape memory effect imparts memory-like behavior with transformation hysteresis, enhancing functionality. This marks a step toward intelligent MEMS sensors integrating sensing, actuation, and SMA for autonomous adaptation without external systems. Combining both approaches enables frequency-selective sensing over a broader range. Applications include bio-acoustic monitoring, gas-flow detection, and frequency-selective signal processing, providing a foundation for advanced sensor networks with real-time dynamic adaptation and memory-based frequency tuning.

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