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

   https://doi.org/10.3390/mi12030268  

   H Hasan, Mohammad; Abbasalipour, Amin; Nikfarjam, Hamed; Pourkamali, Siavash; Emad-Ud-Din, Muhammad; Jafari, Roozbeh; Alsaleem, Fadi (March 2021, Micromachines)

   null (Ed.)

   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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2. Trick or Heat?: Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks

   https://doi.org/10.1145/3319535.3354195  

   Tu, Yazhou; Rampazzi, Sara; Hao, Bin; Rodriguez, Angel; Fu, Kevin; Hei, Xiali (November 2019, Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security)

   Temperature sensing and control systems are widely used in the closed-loop control of critical processes such as maintaining the thermal stability of patients, or in alarm systems for detecting temperature-related hazards. However, the security of these systems has yet to be completely explored, leaving potential attack surfaces that can be exploited to take control over critical systems. In this paper we investigate the reliability of temperature-based control systems from a security and safety perspective. We show how unexpected consequences and safety risks can be induced by physical-level attacks on analog temperature sensing components. For instance, we demonstrate that an adversary could remotely manipulate the temperature sensor measurements of an infant incubator to cause potential safety issues, without tampering with the victim system or triggering automatic temperature alarms. This attack exploits the unintended rectification effect that can be induced in operational and instrumentation amplifiers to control the sensor output, tricking the internal control loop of the victim system to heat up or cool down. Furthermore, we show how the exploit of this hardware-level vulnerability could affect different classes of analog sensors that share similar signal conditioning processes. Our experimental results indicate that conventional defenses commonly deployed in these systems are not sufficient to mitigate the threat, so we propose a prototype design of a low-cost anomaly detector for critical applications to ensure the integrity of temperature sensor signals. 

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3. Machine Learning Augmentation in Micro-Sensor Assemblies

   https://doi.org/10.1115/DETC2020-22665  

   Hasan, Mohammad H.; Alsaleem, Fadi; Abbasalipour, Amin; Pourkamali Anaraki, Siavash; Emad-Un-Din, Muhammad; Jafari, Roozbeh (August 2020, 14th International Conference on Micro- and Nanosystems (MNS))

   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). 

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4. MEMS Acoustic Emission Sensors

   https://doi.org/10.3390/app10248966  

   Ozevin, Didem (December 2020, Applied Sciences)

   null (Ed.)

   This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook. 

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5. Fabrication and Experimental Characterization of a MEMS Microphone Using Electrostatic Levitation

   https://doi.org/10.1109/SENSORS43011.2019.8956879  

   Ozdogan, Mehmet; Towfighian, Shahrzad; Miles, Ronald N. (October 2019, 2019 IEEE SENSORS)

   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. 

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