Filipe Manuel Serra Alves

High-Resolution MEMS Inclinometer Based on Pull-In Voltage

High-resolution pull-in-based microelectromechanical system (MEMS) inclinometers are presented in this paper. Pull-in is characterized by the sudden loss of stability in electrostatically actuated parallel-plate structures, and since pull-in voltage is stable and easy to measure, it enables an effective transduction mechanism that does not require complex and stable capacitive readout electronics. The MEMS devices used to test the novel architecture have differential actuation electrodes resulting in two pull-in voltages that change differentially with applied acceleration. Dedicated MEMS microstructures with extra proof mass show high sensitivity; 269 mV/° with a nonlinearity <;0.5% FS (Full Scale of ±23°). The measured noise is limited by the actuation mechanism, setting the sensors resolution at 75

Bi-directional extended range parallel plate electrostatic actuator based on feedback linearization

\mu

Real-Time Operation and Characterization of a High-Performance Time-Based Accelerometer

°; high above state-of-the-art MEMS devices. [2014-0156].

Porting SLOTH system to FreeRTOS running on ARM Cortex-M3

In this paper, we present a bi-directional extended range parallel-plate electrostatic actuator using feedback linearization control. The actuator can have stable displacements up to 90% of the full-gap (limited by mechanical stoppers) on both directions, i.e., the device can move ±2μm within a ±2.25μm gap. The system has successfully tracked references until 1kHz (limited by the dynamics of the device) and it presents a capacitor tuning range of 17, using an actuation voltage from 0 to 10V. The results presented here are a clear advance in respect to the current state-of-the-art in terms of tracking capabilities, total stable displacement and tuning range.

FPGA controlled MEMS inclinometer

An accelerometer based on the electrostatic pull-in time of a microstructure is presented in this paper. The device uses a parallel-plate overdamped microstructure, and real-time control operation is performed using a field programmable gate array and high precision digital-to-analog converters. Both open-loop and closed-loop measurements are presented. The low noise is a key feature of this approach, which is limited only by the mechanical-thermal noise of the microstructure used, 2 μg/√Hz as shown in the open-loop results (3 μg/√Hz in closed-loop operation). The time readout method has extremely high-resolution capabilities. The pull-in time sensitivity can be adjusted up to 1.6 μs/μg, and the electrostatic feedback voltage sensitivity is 61.3 V2/g. The closed-loop control allows operation of the accelerometer in a much larger range than in open-loop (±500 mg have been achieved) and the linearity is greatly improved (<;1%FS). The current closed-loop control algorithm allows operation up to 2 Hz. A bias stability of 50 μg has been measured over 45 h in open loop and 250 μg in closed loop.

Digital platform for wafer-level MEMS testing and characterization using electrical response

Traditionally, operating system (OSes) suffers from a bifid priority space dictated by the co-existence of threads managed by kernel scheduler and asynchronous interrupt handlers scheduled by hardware. On real-time systems, where reliability and determinism plays a critical role, this approach presents a noteworthy lack, as any interrupt handler can interrupt an execution thread, regardless of its priority. This paper presents the implementation of an unified priority space approach (SLOTH), handling each thread as an interrupt. A light-weight version of FreeRTOS was internally redesigned, to replace the software scheduler by a hardware one, which exploits a Commercial Off-The-Shelf (COTS) hardware interrupt controller, provided by ARM Cortex-M3. The results showed that our implementation solves the priority inversion problem, and simultaneously improves the system performance, reduces the memory footprint and simplifies maintainability.

Autonomous MEMS Inclinometer

A FPGA (Field Programmable Gate Array) controlled inclinometer based on MEMS structures is presented in this paper. Pull-in voltage measurements are used in this work as the transduction mechanism. The pull-in phenomenon is characterized by the sudden loss of stability in electrostatically actuated parallel-plate actuators and since pull-in voltage is stable and easy to measure, it enables an interesting transduction mechanism. By successively bringing the microstructure to pullin while measuring the pull-in voltage allows the detection of external accelerations. A FPGA is responsible to control the entire system increasing its performance and reliability. The sensor resolution is defined by the resolution of the measured pull-in, i.e., resolution of the actuation voltage. Experimental results show a sensitivity of 50 mV/o with the resolution of the actuation voltage set below 1 μV using a 24-bit Digital-to-Analog Converter (DAC).

Vision based automatic traffic condition interpretation

The uniqueness of microelectromechanical system (MEMS) devices, with their multiphysics characteristics, presents some limitations to the borrowed test methods from traditional integrated circuits (IC) manufacturing. Although some improvements have been performed, this specific area still lags behind when compared to the design and manufacturing competencies developed over the last decades by the IC industry. A complete digital solution for fast testing and characterization of inertial sensors with built-in actuation mechanisms is presented in this paper, with a fast, full-wafer test as a leading ambition. The full electrical approach and flexibility of modern hardware design technologies allow a fast adaptation for other physical domains with minimum effort. The digital system encloses a processor and the tailored signal acquisition, processing, control, and actuation hardware control modules, capable of the structure position and response analysis when subjected to controlled actuation signals in real time. The hardware performance, together with the simplicity of the sequential programming on a processor, results in a flexible and powerful tool to evaluate the newest and fastest control algorithms. The system enables measurement of resonant frequency (Fr), quality factor (Q), and pull-in voltage (Vpi) within 1.5 s with repeatability better than 5 ppt (parts per thousand). A full-wafer with 420 devices under test (DUTs) has been evaluated detecting the faulty devices and providing important design specification feedback to the designers.

High-Resolution Seismocardiogram Acquisition and Analysis System

Pull-in voltage measurements are used in this work as the transduction mechanism to build a novel microelectromechanical system (MEMS) inclinometer. By successively bringing the microstructure to pull-in while measuring the pull-in voltage allows the detection of external accelerations. Moreover, the availability of asymmetric pull-in voltages that depend on the same mechanical structure and properties enables the implementation of an auto-calibrated thermal compensated inclinometer. The thermal compensation method is described and it relies on the measurement of pull-in voltages only. Both simulations and experiments are used to validate this novel approach and first results show a sensitivity of 50mV/o and a resolution of 0.006o.

High-performance pull-in time accelerometer

Traffic flow, analysis and control is gaining high relevance, as the number of circulating vehicles continuously increases. This article proposes a computer vision based platform, which automatically detects vehicles in order to infer the traffic conditions. The developed real time detection algorithm is based on a dual background subtraction technique, incorporating the one known has Codebook and an edges one. These two layers interact mutually allowing the compensation of individuality weaknesses. The traffic flow parameters are extracted comparing the detected vehicles with a known model of the road lanes, which can be automatically generated based on the vehicles trajectory analysis over time. The achieved results demonstrate that the developed algorithm is able to correctly understand the traffic flow state, even in the presence of adverse situations that are typical of an outdoor application.