An 8-Bit Digitally Operated Micromachined Accelerometer

Abbasalipour, Amin; Nikfarjam, Hamed; Pourkamali, Siavash

doi:10.1109/jmems.2020.3026258

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…While previous works have shown the potential of performing simple edge computing using MEMS sensors [43], this approach shows a new generation of intelligent sensors can be produced without using large networks of sensory elements [44]. Such sensors will entirely utilize transience, increasing their speeds.…”

Section: Discussionmentioning

confidence: 96%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

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Section: Discussionmentioning

confidence: 96%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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“…To address this challenge, we have adopted the electrostatic mechanical coupling mechanism. This approach has been already used by our team to realize a digital mechanical MEMS accelerometer [ 22 ].…”

Section: Theory and Methodologymentioning

confidence: 99%

Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

et al. 2021

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The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.

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“…The selected adjustable plates were pushed by diverse PDMS caps to produce a specific displacement of the sensitive mass, thus realizing the adjustment of the acceleration threshold. By using different upper PDMS caps, the acceleration thresholds changed between 40 g and 75 g. Abbasalipour et al [ 72 ] expanded on the original design of the fully digital MEMS accelerometer and designed a micromechanical accelerometer with 8-bit digital control. The tuning force of each electrode group was three times that of the adjacent electrode group.…”

Section: Intermittent Inertial Switchesmentioning

confidence: 99%

Research Progress of MEMS Inertial Switches

Liu

Niu

et al. 2022

Micromachines

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As a typical type of MEMS acceleration sensor, the inertial switch can alter its on-off state while the environmental accelerations satisfy threshold value. An exhaustive summary of the design concept, performance aspects, and fabrication methods of the micro electromechanical system (MEMS) inertial switch is provided. Different MEMS inertial switch studies were reviewed that emphasized acceleration directional and threshold sensitivity, contact characteristics, and their superiorities and disadvantages. Furthermore, the specific fabrication methods offer an applicability reference for the preparation process for the designed inertial switch, including non-silicon surface micromachining technology, standard silicon micromachining technology, and the special fabrication method for the liquid inertial switch. At the end, the main conclusions of the current challenges and prospects about MEMS inertial switches are drawn to assist with the development of research in the field of future engineering applications.

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An 8-Bit Digitally Operated Micromachined Accelerometer

Cited by 7 publications

References 8 publications

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

Research Progress of MEMS Inertial Switches

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