MEMS Sensing and Neural Computing research lab
MEMS Sensing and Neural Computing research lab
- Micro-Electro-Mechanical Systems (MEMS) have been gaining a lot of interest in the recent years due to their cheap manufacturing costs, mass-producibility, low weight and power consumption, and their very high accuracy.
- Our lab is set to study Micro-oscillator-type MEMS devices and use their unique features to create new, more reliable sensors.
- Our lab is located in PKI 117B. It is equipped with a state-of-the-art holographic MEMS analyzer to study the static and dynamic response of our MEMS devices. The MEMS analyzer is also equipped with a stroboscope to offer a broad range of oscillation frequencies and input voltage
- The MEMS analyzer, shown in the figure aside, measures the oscillation of a micro-beam, either in a horizontal or a vertical plane. To achieve higher oscillation magnitude, the microbeam is enclosed in a vacuum chamber.
A MEMS Nonlinear Dynamic Approach For Neural Computing
With the enormous data generated every day from countless applications and sensor networks, the need for intelligent devices to process and make use of this data continues to grow and is projected to only increase in the future. These applications range from heat ventilation and air conditioning (HVAC) diagnostic systems to smart cars and robotics. The demand is even greater when the applications require computations in real-time. These data-intensive applications are now handled by parallel computing and distributed computing approaches, among others, where many silicon-based devices are interconnected and made to work as an integrated unit to achieve high performance and scalability. However, the high power consumption during switching periods and complicated thermal management requirements of existing CMOS-based logic gates, the fundamental building block of modern digital computing, have been a source of skepticism and future concern for many in the industry. The introduction of neuromorphic computing, an approach that mimics the way neurons function in the human brain, has created a lot of excitement. This work presents a novel concept for a micro-electro-mechanical-system (MEMS) neural computing unit based on the Dynamic Field Theory (DFT); a qualitative neuron approach to model cognition and human behavior. The concept utilizes the nonlinear dynamics of MEMS resonators, specifically, bi-stability and hysteresis, to simulate detection and memory of a single DFT neuron. We introduce bi-stability into a straight microbeam by actuating the microbeam with an AC voltage at a frequency near its electric circuit resonance, which also significantly amplifies the applied voltage and introduces electromechanical coupling. Moreover, we demonstrate memory and detection processes using a MEMS arch beam by utilizing snap-through instability.
Low voltage activation for electrostatic MEMS device using double resonance
State-of-the-art electrostatically driven micro and nanoresonators have major limitations in their range of deflection due to the separation between electrodes, instability regions, and high operating voltages. In this project, we develop a novel concept to couple the mechanical resonance of an electrostatically driven MEMS resonator with its electrical circuit resonance. This concept simultaneously activates the mechanical and electrical resonances of the MEMS circuit to significantly amplify the voltage across the MEMS electrodes and increase the MEMS sensitivity to the electrostatic force input. In this work, we realize MEMS amplitude amplification > 30 times that can be achieved for any electrostatically driven MEMS and NEMS device, regardless of the mismatch between electrical and mechanical resonance frequencies.
Figure: Response of a MEMS resonator under double resonance excitation using two AC sources (a) 3D frequency response showing the MEMS response amplitude as a function of mechanical and electrical resonances (b, c) 2D mechanical frequency response as a function of electrical resonance. The response is compared to a classical response under mechanical resonance alone using only one source. (d) Time history of the MEMS response under double resonance excitation.
MEMS Humidity sensor
In this project, we propose the use of an electrostatic MEMS (Microelectromechanical system) oscillator exposed to air to measure relative humidity. As water content in air changes, this should change the MEMS air viscosity and its air’s dielectric constant. Because those changes are so small, we propose amplifying the sensor’s response by dynamically driving the sensor at its subharmonic regime (actuation at twice the natural frequency). At this regime, the output of the sensor makes a big jump at a particular input frequency. This frequency is related to the air’s squeeze film damping (which is a function of the air humidity).
Our novel idea eliminates the need for the polymer layers typically used in current MEMS humidity sensors. Those layers are known to degrade with time, which limits the sensor’s lifespan and its accuracy.MEMS Oscillators are found to have a life of the magnitude 10^7 Hours. Moreover, because of their microscale sizes and the use of capacitive sensing, Electrostatic MEMS devices require very little energy to run (in the mW range) and can be powered with a single battery for a very long time. It can also be powered using energy harvesting, which is one of our future projects.
Mohammad H Hasan - PhD candidate (Mechanical and materials engineering)
Mehari Tesfay - PhD candidate (Architectural engineering - Electrical)