Visible to the public Biblio

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Hu, Jizhou, Qu, Hemi, Guo, Wenlan, Chang, Ye, Pang, Wei, Duan, Xuexin.  2019.  Film Bulk Acoustic Wave Resonator for Trace Chemical Warfare Agents Simulants Detection in Micro Chromatography. 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems Eurosensors XXXIII (TRANSDUCERS EUROSENSORS XXXIII). :45–48.
This paper reported the polymer coated film bulk acoustic resonators (FBAR) as a sensitive detector in micro chromatography for the detection of trace chemical warfare agents (CWA) simulants. The FBAR sensor was enclosed in a microfluidic channel and then coupled with microfabricated separation column. The subsequent chromatographic analysis successfully demonstrated the detection of parts per billion (ppb) concentrations of chemical warfare agents (CWAs) simulants in a five components gas mixture. This work represented an important step toward the realization of FBAR based handheld micro chromatography for CWA detection in the field.
Zhao, Xuanyi, Cassella, Cristian.  2019.  On the Coupling Coefficient of ScyAl1-yN-based Piezoelectric Acoustic Resonators. 2019 Joint Conference of the IEEE International Frequency Control Symposium and European Frequency and Time Forum (EFTF/IFC). :1–4.
This work investigates the electromechanical coupling coefficient (kt2) attained by two available piezoelectric acoustic resonator technologies relying on Aluminum Scandium Nitride (ScyAl1-yN) films to operate. In particular, by using a theoretical approach, we extracted the maximum kt2-value attainable, for different scandium-doping concentrations (from 0% to 40%), by Film-Bulk-Acoustic-Resonators (FBARs) and Cross-Sectional-Lamé-Mode Resonators (CLMRs). For the first time, we show how the use of higher scandium doping concentrations can render the kt2 of CLMRs higher (35%) than the one attained by FBARs (28%). Such a unique feature renders CLMRs as ideal candidates to form lithographically defined resonators and filters for next-generation wideband radiofrequency (RF) front-ends.
Ansari, Azadeh.  2019.  Single Crystalline Scandium Aluminum Nitride: An Emerging Material for 5G Acoustic Filters. 2019 IEEE MTT-S International Wireless Symposium (IWS). :1–3.
Emerging next generation wireless communication devices call for high-performance filters that operate at 3-10 GHz frequency range and offer low loss, small form factor, wide bandwidth and steep skirts. Bulk and surface acoustic wave devices have been long used in the RF front-end for filtering applications, however their operation frequencies are mostly below 2.6 GHz band. To scale up the frequency of the filters, the thickness of the piezoelectric material needs to be reduced to sub-micron ranges. One of the challenges of such scaling is maintaining high electromechanical coupling as the film thickness decreases, which in turn, determines the filter bandwidth.Aluminum Nitride (AlN) - popular in today's film bulk acoustic resonators (FBARs) and mostly deposited using sputtering techniques-shows degraded crystal quality and poor electromechanical coupling when the thickness of AlN film is smaller than 1 μm.In this work, we propose using high-quality single-crystalline AlN and Scandium (Sc)-doped AlN epi-layers grown on Si substrates, wherein high crystal quality is maintained for ultra-thin films of only 400 nm thickness. Experimental results verify improved kt2 for 3-10 GHz resonators, with quality factors of the order of 250 and kt2 values of up to 5%based on bulk acoustic wave resonators. The experimental results suggest that single-crystal Sc-AlN is a great material candidate for 5G resonators and filters.
Lin, F., Beadon, M., Dixit, H. D., Vunnam, G., Desai, A., Sankar, S..  2018.  Hardware Remediation at Scale. 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :14–17.
Large scale services have automated hardware remediation to maintain the infrastructure availability at a healthy level. In this paper, we share the current remediation flow at Facebook, and how it is being monitored. We discuss a class of hardware issues that are transient and typically have higher rates during heavy load. We describe how our remediation system was enhanced to be efficient in detecting this class of issues. As hardware and systems change in response to the advancement in technology and scale, we have also utilized machine learning frameworks for hardware remediation to handle the introduction of new hardware failure modes. We present an ML methodology that uses a set of predictive thresholds to monitor remediation efficiency over time. We also deploy a recommendation system based on natural language processing, which is used to recommend repair actions for efficient diagnosis and repair. We also describe current areas of research that will enable us to improve hardware availability further.