Dynamics of a Close-Loop Controlled MEMS Resonator

The dynamics of a close-loop electrostatic MEMS resonator, proposed as a platform for ultra sensitive mass sensors, is investigated. The parameter space of the resonator actuation voltage is investigated to determine the optimal operating regions. Bifurcation diagrams of the resonator response are obtained at five different actuation voltage levels. The resonator exhibits bi-stability with two coexisting stable equilibrium points located inside a lower and an upper potential wells. Steady-state chaotic attractors develop inside each of the potential wells and around both wells. The optimal region in the parameter space for mass sensing purposes is determined. In that region, steady-state chaotic attractors develop and spend most of the time in the safe lower well while occasionally visiting the upper well. The robustness of the chaotic attractors in that region is demonstrated by studying their basins of attraction. Further, regions of large dynamic amplification are also identified in the parameter space. In these regions, the resonator can be used as an efficient long-stroke actuator

Fundamentals and applications of spatial dissipative solitons in photonic devices : [Chapter 6]

We review the properties of optical spatial dissipative solitons (SDS). These are stable, self‐localized optical excitations sitting on a uniform, or quasi‐uniform, background in a dissipative environment like a nonlinear optical cavity. Indeed, in optics they are often termed “cavity solitons.” We discuss their dynamics and interactions in both ideal and imperfect systems, making comparison with experiments. SDS in lasers offer important advantages for applications. We review candidate schemes and the tremendous recent progress in semiconductor‐based cavity soliton lasers. We examine SDS in periodic structures, and we show how SDS can be quantitatively related to the locking of fronts. We conclude with an assessment of potential applications of SDS in photonics, arguing that best use of their particular features is made by exploiting their mobility, for example in all‐optical delay lines

Dynamics of a close-loop controlled MEMS resonator

The dynamics of a close-loop electrostatic MEMS resonator, proposed as a platform for ultra sensitive mass sensors, is investigated. The parameter space of the resonator actuation voltage is investigated to determine the optimal operating regions. Bifurcation diagrams of the resonator response are obtained at five different actuation voltage levels. The resonator exhibits bi-stability with two coexisting stable equilibrium points located inside a lower and an upper potential wells. Steady-state chaotic attractors develop inside each of the potential wells and around both wells. The optimal region in the parameter space for mass sensing purposes is determined. In that region, steady-state chaotic attractors develop and spend most of the time in the safe lower well while occasionally visiting the upper well. The robustness of the chaotic attractors in that region is demonstrated by studying their basins of attraction. Further, regions of large dynamic amplification are also identified in the parameter space. In these regions, the resonator can be used as an efficient long-stroke actuator

7th International Conference on Nonlinear Vibrations, Localization and Energy Transfer: Extended Abstracts

International audienceThe purpose of our conference is more than ever to promote exchange and discussions between scientists from all around the world about the latest research developments in the area of nonlinear vibrations, with a particular emphasis on the concept of nonlinear normal modes and targeted energytransfer

Electrostatic MEMS Bifurcation Sensors

We report experimental evidence of a new instability in electrostatic sensors, dubbed quasi-static pull-in, in two types of micro-sensors operating in ambient air. We find that the underlying mechanism and features of this instability are distinct from those characterizing hitherto known static and dynamic pull-in instabilities. Specifically, the mechanism instigating quasi-static pull-in is a global Shilnikov homoclinic bifurcation where a slow-varying waveform drives the sensor periodically through a saddle-node bifurcation. Based on these findings, we propose a new taxonomy of pull-in instabilities in electrostatic sensors. Experimental evidence of nonlinear chaotic behaviors were observed in an electrostatic MEMS sensor. Period doubling bifurcation (P-2), period three (P-3), and period six (P-6) were observed. A new class of intermittency subsequent to homoclinic bifurcation in addition to the traditional intermittencies of type-I and type-II were demonstrated. Quasiperiodicity and homoclinic tangles leading to chaos were also reported. All of these nonlinear phenomena instigate either banded chaos or full chaos and both are observed in this work. Based on our knowledge, this is the first observation such chaotic behaviors in electrostatic MEMS sensors. All of the experimental observations have been measured optically via a laser Doppler-vibrometer (LDV) in ambient pressure. Also, a new class of intermittencies was found in the oscillations of an electrostatic sensor. These intermittencies involve a dynamic system spending irregular time intervals in the vicinity of the ghost of an orbit before undergoing bursts that are arrested by landing on a larger attractor. Re-injection into the vicinity of the ghost orbit is noise induced. As a control parameter is increased, switching intermittency of type-I leads to a stable periodic orbit, whereas switching intermittency of type-II leads to a chaotic attractor. These significant findings in nonlinear dynamic were used to develop novel MEMS sensors. An electrostatic MEMS gas sensor is demonstrated. It employs a dynamic-bifurcation detection technique. In contrast to traditional gas or chemical sensors that measure (quantify) the concentration of an analyte in analog mode, this class of sensors does not seek to quantify the concentration. Rather, it detects the analyte's concentration in binary mode, reporting ON-state (1) for concentrations above a preset threshold and OFF-state (0) for concentrations below the threshold. The sensing mechanism exploits the qualitative difference between the sensor state before and after the dynamic pull-in bifurcation. Experimental demonstration was carried out using a laser-Doppler vibrometer to measure the sensor response before and after detection. The sensor was able to detect ethanol vapor concentrations as 100\,ppb in dry nitrogen. A closed-form expression for the sensitivity of dynamic bifurcation sensors was derived. It captured the dependence of sensitivity on the sensor dimensions, material properties, and electrostatic field. An analog dynamic bifurcation mass sensor is developed to demonstrate a sensing mechanism that exploits a quantitative change in the sensor state before and after depositing added mass. A polymeric material was deposited on the top surface of the sensor plate to represent added mass. A variation in the frequency and current amplitude were utilized to demarcate the added mass optically and electrically. A chemical sensor was also developed to detect mercury in deionized-water in a fashion of analog detection. A polymeric sensing material that has high selectivity to mercury was utilized to captured mercury molecules in water. The sensor was submerged completely in water with a pre-defined flow-rate. The sensor was excited electrostatically. A variation in the frequency response due to added mass was measured electrically using a lock-in amplifier. A frequency-shift was observed while releasing the mercury to the water

MACHINE LEARNING AUGMENTATION MICRO-SENSORS FOR SMART DEVICE APPLICATIONS

Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability of MEMS devices for sensing. Furthermore, this dissertation addresses the high-voltage requirements in electrostatically actuated MEMS devices using a passive amplification scheme. The CTRNN architecture is emulated using a network of bistable MEMS devices. This bistable behavior is shown in the pull-in, the snapthrough, and the feedback regimes, when excited around the electrical resonance frequency. In these regimes, MEMS devices exhibit key behaviors found in biological neuronal populations. When coupled, networks of MEMS are shown to be successful at classification and control tasks. Moreover, MEMS accelerometers are shown to be successful at acceleration waveform classification without the need for external processors. MEMS devices are additionally shown to perform computing by utilizing the RC architecture. Here, a delay-based RC scheme is studied, which uses one MEMS device to simulate the behavior of a large neural network through input modulation. We introduce a modulation scheme that enables colocalized sensing-and-computing by modulating the bias signal. The MEMS RC is tested to successfully perform pure computation and colocalized sensing-and-computing for both classification and regression tasks, even in noisy environments. Finally, we address the high-voltage requirements of electrostatically actuated MEMS devices by proposing a passive amplification scheme utilizing the mechanical and electrical resonances of MEMS devices simultaneously. Using this scheme, an order-of-magnitude of amplification is reported. Moreover, when only electrical resonance is used, we show that the MEMS device exhibits a computationally useful bistable response. Adviser: Dr. Fadi Alsalee