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1. Gate reflectometry of single-electron box arrays using calibrated low temperature matching networks

   https://doi.org/10.1038/s41598-022-06727-1  

   Filmer, Matthew J.; Huebner, Matthew; Zirkle, Thomas A.; Jehl, Xavier; Sanquer, Marc; Chisum, Jonathan D.; Orlov, Alexei O.; Snider, Gregory L. (December 2022, Scientific Reports)

   Abstract Sensitive dispersive readouts of single-electron devices (“gate reflectometry”) rely on one-port radio-frequency (RF) reflectometry to read out the state of the sensor. A standard practice in reflectometry measurements is to design an impedance transformer to match the impedance of the load to the characteristic impedance of the transmission line and thus obtain the best sensitivity and signal-to-noise ratio. This is particularly important for measuring large impedances, typical for dispersive readouts of single-electron devices because even a small mismatch will cause a strong signal degradation. When performing RF measurements, a calibration and error correction of the measurement apparatus must be performed in order to remove errors caused by unavoidable non-idealities of the measurement system. Lack of calibration makes optimizing a matching network difficult and ambiguous, and it also prevents a direct quantitative comparison between measurements taken of different devices or on different systems. We propose and demonstrate a simple straightforward method to design and optimize a pi matching network for readouts of devices with large impedance, $$Z \ge 1\hbox {M}\Omega$$ Z ≥ 1 M Ω . It is based on a single low temperature calibrated measurement of an unadjusted network composed of a single L-section followed by a simple calculation to determine a value of the “balancing” capacitor needed to achieve matching conditions for a pi network. We demonstrate that the proposed calibration/error correction technique can be directly applied at low temperature using inexpensive calibration standards. Using proper modeling of the matching networks adjusted for low temperature operation the measurement system can be easily optimized to achieve the best conditions for energy transfer and targeted bandwidth, and can be used for quantitative measurements of the device impedance. In this work we use gate reflectometry to readout the signal generated by arrays of parallel-connected Al-AlOx single-electron boxes. Such arrays can be used as a fast nanoscale voltage sensor for scanning probe applications. We perform measurements of sensitivity and bandwidth for various settings of the matching network connected to arrays and obtain strong agreement with the simulations. 

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2. MTJ-Based Dithering for Stochastic Analog-to-Digital Conversion

   https://doi.org/10.1109/ISCAS51556.2021.9401632  

   Mitrovic, Ana; Friedman, Eby G. (May 2021, Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS))

   null (Ed.)

   The stochastic behavior of magnetic tunnel junctions (MTJ) finds use in many applications - from analog-to- digital conversion to neuromorphic computing. In this paper, a dithering method exploiting the stochastic behavior of an MTJ, based on the voltage controlled magnetic anisotropy effect, is proposed. This method is used to measure low frequency analog signals over an interval. The circuit is composed of two MTJ devices that convert an analog signal into a series of high resistance and low resistance stochastic states, creating a dithering effect. The behavior of the input signal is extracted from the switching frequency. The binary nature of the output signal reduces the complexity, enabling a small, fast, and energy efficient circuit for extracting different forms of information from an analog signal. 

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3. 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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4. CMOS integrated ZnO thin film bulk acoustic resonator with Si3N4 susceptor layer for improved IR sensitivity

   https://doi.org/10.1109/DRC.2017.7999463  

   Colon-Berrios, Aida R.; Edrees, Hassan; McGinn, Christine; Cavallari, Marco Roberto; Kinget, Peter; Kymissis, Ioannis (June 2017, Device Research Conference)

   Microelectromechanical sensors (MEMS) offer a new way of measuring temperature and infrared (IR) radiation, through measurements of total optical energy, overcoming the obstacles associated with narrow bandgap semiconductor detectors[1]. Specifically, Thin Film Bulk Acoustic Resonators (FBAR) offers a versatile MEMS technology based on resonant devices fabricated using piezoelectric materials [2]. With the addition of an absorbing susceptor and the use of zinc oxide (ZnO) as the piezoelectric it is possible to obtain a higher sensitivity device than previously demonstrated systems, taking advantage of ZnO's higher temperature coefficient of frequency (TCF) [3,4,5]. This work studies the improved sensing ability of an FBAR structure and ease of testing and superior parasitic performance provided in a package using an engineered high TCF FBAR in a monolithically integrated CMOS process. 

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5. 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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