MEMS sensor

The static and dynamic pull-in phenomenon of a functionally graded Al/Al 2 O 3 microplate, considering the damping coefficient and fringing field effects, has been analyzed because of its crucial effect in micro-electromechanical systems application, especially in microswitches. The nonlinear equation of motion of functionally graded microplate has been derived using Hamilton's principle, and solved analytically. Furthermore, a finite element code has been developed to solve the problem. Comparing the theoretical and numerical results for specific boundary conditions demonstrates that the numerical solution predicts the pull-in phenomenon with the least errors; and it can be used for various material power laws, damping coefficients, and initial gaps between the microplate and the substrate. The numerical results for various boundary conditions demonstrate that by increasing the damping coefficient, the dynamic pull-in voltage is also increased, and pull-in time will slow down. Moreover, the effect of power law and applied voltage on the pull-in instability is investigated.

Advancements in neurotechnologies for electrophysiology, neurochemical sensing, neuromodulation, and optogenetics are revolutionizing scientific understanding of the brain while enabling treatments and preventative measures for a variety of neurological disorders. The grand challenge in neural interface engineering is to seamlessly integrate the interface between neurobiology and engineered technology to record from and modulate neu-rons over chronic timescales. However, the biological inflammatory response to implants, neural degeneration, and long-term material stability diminishes the quality of the interface overtime. Recent advances in functional materials are aimed at engineering solutions for chronic neural interfaces, yet, the development and deployment of neural interfaces designed from novel materials have introduced new challenges that have been largely unad-dressed. Many engineering efforts that solely focus on optimizing individual probe design parameters, such as softness or flexibility, downplay critical multi dimensional interactions between different physical properties of the device that contribute to overall performance and biocompatibility. Moreover, the use of these new materials present substantial new difficulties that must be addressed before regulatory approval for use in human patients is achievable. In this review, the interdependence of different electrode components is highlighted to demonstrate the current material-based challenges facing the field of neural interface engineering.

Seismology deals with the study of the activity of physical forces responsible for the origin of earthquakes and the seismic waves generated within the Earth. All structures located from the center of the Earth to its surface are the subject of study in this discipline. Seismology therefore pursues the understanding of the Earth’s internal structure and the physical processes that cause earthquakes, resorting to advanced instruments for observation and measurements. This paper presents an overview of important milestones in the seismological field, followed by revolutions in the instrumentation and observation of seismological events.

Modern Remotely Piloted Aircraft Systems (RPAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced functionalities and trusted autonomy in a wide range of mission-essential and safety-critical tasks. In particular, Navigation and Guidance Systems (NGS) for small RPAS require a typical combination of lightweight, compact and inexpensive sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. In this paper, the synergies attainable by the combination of Global Navigation Satellite System (GNSS), Micro-Electromechanical System based Inertial Measurement Unit (MEMS-IMU) and Vision-Based Navigation (VBN) sensors are explored. In case of VBN, an appearance-based navigation technique is adopted and feature extraction/optical flow methods are employed to estimate the navigation parameters during precision approach and landing phases. A key novelty of the proposed approach is the employment of Aircraft Dynamics Models (ADM) augmentation to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. To obtain the best estimates of Position, Velocity and Attitude (PVA), different sensor combinations are analysed and dynamic Boolean Decision Logics (BDL) are implemented for data selection before the centralised data fusion is accomplished. Various alternatives for data fusion are investigated including a traditional Extended Kalman Filter (EKF) and a more advanced Unscented Kalman Filter (UKF). A novel hybrid controller employing fuzzy logic and Proportional-Integral-Derivative (PID) techniques is implemented to provide effective stabilization and control of pitch and roll angles. After introducing the key mathematical models describing the three NGS architectures: EKF based VBN-IMU-GNSS (VIG) and VBN-IMU-GNSS-ADM (VIGA) and UKF based Enhanced VIGA (EVIGA), the system performances are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope. A dedicated ADM processor (i.e., a local pre-filter) is adopted in the EVIGA architecture to account for the RPAS maneuvering envelope in different flight phases (assisted by a maneuver identification algorithm), in order to extend the ADM validity time across all segments of the RPAS trajectory. Simulation results show that the VIG, VIGA and EVIGA systems are compliant with ICAO requirements for precision approach down to CAT-II. In all other flight phases, the VIGA system shows improvement in PVA data output with respect to the VIG system. The EVIGA system shows the best performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved in this configuration.

This documents describes a modular hardware platform for inertial measuring unit and its integration into Matlab Simulink. Its purpose is to give students a way to work with real device in an environment which they are familiar with. They can train various methods of signal processing and filtering and they can get familiar with used MEMS gyroscopes and accelerometers.

Considerable research efforts have been devoted to the development of MEMS sensor packaging based on different material types (ceramic, plastic and metal). In this work, we describe the design, fabrication and test of a metal packaging with high reliability and low cost. The novelty of our research is on the packaging assembly by mechanical interference replacing the expensive plasma welding process that is used commonly in the development of traditional metal packaging.

A modern alternative to the positive-tone PMMA resist is the ZEP 520A (Nippon Zeon) brand co-polymer resist, which offers a higher sensitivity and etch durability for electron beam lithography. However, the molecular mechanisms are not entirely understood, and the relative performance of two resists for various process conditions of nanofabrication is not readily predictable. The authors report a thorough experimental comparison of the performance of PMMA 950k and ZEP 520A resists in MIBK:IPA, ZED, and IPA:water developers. Interestingly, ZEP resist performance was found to depend significantly on the developer. ZED developer increases the sensitivity, whereas IPA:water optimizes line edge roughness and conceivably the resolution at the expense of sensitivity. The authors also describe two alternative numerical models, one assuming an enhancement of the main chain scission in ZEP as a result of electronic excitations in side groups, and another without such enhancement. In the second case, the differences in ZEP and PMMA resists performance are attributed to their different interaction with the developers. Using both approaches, the authors parameterize the respective models of ZEP development by fitting numerical results to the experimental resist morphologies, and analyze the outcomes.

The objective of this paper is to demonstrate the integration of a MOF thin film on electrostatically actuated microstructures to realize a switch triggered by gas and a sensing algorithm based on amplitude tracking. The devices are based on the nonlinear response of micromachined clamped-clamped beams. The microbeams are coated with a metal-organic framework (MOF), namely HKUST-1, to achieve high sensitivity. The softening and hardening nonlinear behaviors of the microbeams are exploited to demonstrate the ideas. For gas sensing, an amplitude-based tracking algorithm is developed to quantify the captured quantity of gas. Then, a MEMS switch triggered by gas using the nonlinear response of the microbeam is demonstrated. Noise analysis is conducted, which shows that the switch has high stability against thermal noise. The proposed switch is promising for delivering binary sensing information, and also can be used directly to activate useful functionalities, such as alarming.

We present the design, fabrication and experimental results for a novel air-microfluidic lab-on-a-chip sensor, which measures the mass concentration of airborne particulate matter (PM), such as tobacco smoke or diesel exhaust. The sensor detects PM having aerodynamic diameter (AD) of less than 2.5μm, (PM2.5) directly by measuring the deposited PM on a mass-sensing resonator, at flowrates two orders of magnitude lower than the closest comparable devices. We report on an improved sensor design, new mask-less dispenser-printed SU-8-based bonding process, and recorded sensitivity of our newly-developed design.

An Aircraft Dynamics Model (ADM) augmentation scheme for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. The proposed ADM virtual sensor is employed in the RPAS navigation system to enhance continuity and accuracy of positioning data in case of Global Navigation Satellite System (GNSS) data degradations/losses, and to improve attitude estimation by vision based sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors. The ADM virtual sensor is essentially a knowledge-based module that predicts RPAS flight dynamics (aircraft trajectory and attitude motion) by employing a rigid body 6-Degree of Freedom (6-DoF) model. Two possible schemes are studied for integration of the ADM module in the aircraft navigation system employing an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). Additionally, the synergy between the navigation systems and an Avionics-Based Integrity Augmentation (ABIA) module is examined and a sensor-switching framework is proposed to maintain the Required Navigation Performance (RNP) in the event of single and multiple sensor degradations. The ADM performance is assessed through simulation of an RPAS in representative fight operations. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results confirm that the ADM virtual sensor provides improved performance in terms of data accuracy/continuity, and an extension of solution validity time, especially when pre-filtered and employed in conjunction with a UKF.