skip to main content

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, February 13 until 2:00 AM ET on Friday, February 14 due to maintenance. We apologize for the inconvenience.


Title: Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network
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.  more » « less
Award ID(s):
1935641 1935598
PAR ID:
10279967
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Micromachines
Volume:
12
Issue:
3
ISSN:
2072-666X
Page Range / eLocation ID:
268
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Abstract

    The size and power limitations in small electronic systems such as wearable devices limit their potential. Significant energy is lost utilizing current computational schemes in processes such as analog-to-digital conversion and wireless communication for cloud computing. Edge computing, where information is processed near the data sources, was shown to significantly enhance the performance of computational systems and reduce their power consumption. In this work, we push computation directly into the sensory node by presenting the use of an array of electrostatic Microelectromechanical systems (MEMS) sensors to perform colocalized sensing-and-computing. The MEMS network is operated around the pull-in regime to access the instability jump and the hysteresis available in this regime. Within this regime, the MEMS network is capable of emulating the response of the continuous-time recurrent neural network (CTRNN) computational scheme. The network is shown to be successful at classifying a quasi-static input acceleration waveform into square or triangle signals in the absence of digital processors. Our results show that the MEMS may be a viable solution for edge computing implementation without the need for digital electronics or micro-processors. Moreover, our results can be used as a basis for the development of new types of specialized MEMS sensors (ex: gesture recognition sensors).

     
    more » « less
  2. A radio frequency (RF) reflectometry technique is presented to measure device capacitances using a probe station. This technique is used to characterize micro-electromechanical system (MEMS) variable capacitor devices that can be connected to create pull-up and pull-down networks used in digital gates for reversible computing. Adiabatic reversible computing is a promising approach to energy-efficient computing that can dramatically reduce heat dissipation by switching circuits at speeds below their RC time constants, introducing a trade-off between energy and speed. The variable capacitors in this study will be measured using single port RF reflectometry achieved with a custom-made RF probe. The RF probe consists of a micromanipulator with an on-board matching network and is calibrated by measuring a capacitive bank that shows a clearly visible frequency shift with the increase in capacitance. The RF probe worked well when measuring static capacitors with no parasitic resistance; however, the frequency shift is masked when measuring the MEMS variable capacitors due to their high in-series parasitic resistance (around 80 kΩ). Therefore, RF reflectometry has the potential to measure MEMS variable capacitors in the range of 0–30 fF when not masked by a high in-series parasitic resistance, creating a fast and versatile method for characterizing variable capacitors that can be used in energy-efficient computing.

     
    more » « less
  3. null (Ed.)
    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. 
    more » « less
  4. Fabrication and acoustic performance of a microelectromechanical systems (MEMS) microphone are presented. The microphone utilizes an unusual electrostatic sensing scheme that causes the sensing electrode to move away, or levitate from the biasing electrode as the bias voltage is applied. This approach differs from existing electrostatic sensors and completely avoids the usual collapse, or pull-in instability. In this study, our goal is to fabricate a MEMS microphone whose sensitivity could be improved simply by increasing the bias voltage, without suffering from pull-in instability. The microphone is tested in our anechoic chamber and a read-out circuit is used to obtain electrical signals in response to sound pressure at various bias voltages. Experimental results show that the sensitivity increases approximately linearly with bias voltage for bias voltages from 40 volts to 100 volts. The ability to design electrostatic sensors without concerns about pull-in failure can enable a wide-range of promising sensor designs. 
    more » « less
  5. Abstract

    The Casimir force, a quantum mechanical effect, has been observed in several microelectromechanical system (MEMS) platforms. Due to its extreme sensitivity to the separation of two objects, the Casimir force has been proposed as an excellent avenue for quantum metrology. Practical application, however, is challenging due to attractive forces leading to stiction and device failure, called Casimir pull-in. In this work, we design and simulate a Casimir-driven metrology platform, where a time-delay-based parametric amplification technique is developed to achieve a steady-state and avoid pull-in. We apply the design to the detection of weak, low-frequency, gradient magnetic fields similar to those emanating from ionic currents in the heart and brain. Simulation parameters are selected from recent experimental platforms developed for Casimir metrology and magnetic gradiometry, both on MEMS platforms. While a MEMS offers many advantages to such an application, the detected signal must typically be at the resonant frequency of the device, with diminished sensitivity in the low frequency regime of biomagnetic fields. Using a Casimir-driven parametric amplifier, we report a 10,000-fold improvement in the best-case resolution of MEMS single-point gradiometers, with a maximum sensitivity of 6 Hz/(pT/cm) at 1 Hz. Further development of the proposed design has the potential to revolutionize metrology and may specifically enable the unshielded monitoring of biomagnetic fields in ambient conditions.

     
    more » « less