Search Results for author: Matteo Rinaldi

Found 7 papers, 0 papers with code

2-16 GHz Multifrequency X-Cut Lithium Niobate NEMS Resonators on a Single Chip

no code implementations9 May 2024 Ryan Tetro, Luca Colombo, Matteo Rinaldi

This work presents the design, fabrication, and testing of X-Cut Lithium Niobate (LN) acoustic nanoelectromechanical (NEMS) Laterally Vibrating Resonators (LVRs) and Degenerate LVRs (d-LVRs) operating in the S0 (YZ30) and SH0 (YZ-10) modes between 2 to 16 GHz range, monolithically fabricated on a single chip.

Scandium Aluminum Nitride Overmoded Bulk Acoustic Resonators for Future Wireless Communication

no code implementations23 Apr 2024 Walter Gubinelli, Pietro Simeoni, Ryan Tetro, Luca Colombo, Matteo Rinaldi

This work reports on the modeling, fabrication, and experimental characterization of a 13 GHz 30% Scandium-doped Aluminum Nitride (ScAlN) Overmoded Bulk Acoustic Resonator (OBAR) for high-frequency Radio Frequency (RF) applications, notably in 5G technology and beyond.

Millimeter Wave Thin-Film Bulk Acoustic Resonator in Sputtered Scandium Aluminum Nitride Using Platinum Electrodes

no code implementations22 Nov 2023 Sinwoo Cho, Omar Barrera, Pietro Simeoni, Ellie Y. Wang, Jack Kramer, Vakhtang Chulukhadze, Joshua Campbell, Matteo Rinaldi, Ruochen Lu

This work describes sputtered scandium aluminum nitride (ScAlN) thin-film bulk acoustic resonators (FBAR) at millimeter wave (mmWave) with high quality factor (Q) using platinum (Pt) electrodes.

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

no code implementations13 Oct 2023 Federico Tomasi, Joseph Cauteruccio, Surya Kanoria, Kamil Ciosek, Matteo Rinaldi, Zhenwen Dai

In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment.

Collaborative Filtering reinforcement-learning

Meson and glueball spectroscopy within the graviton soft wall model

no code implementations7 Jan 2021 Matteo Rinaldi, Vicente Vento

This success has led us to extend the analysis to the description of the spectra of other hadrons.

High Energy Physics - Phenomenology

Reflective Parametric Frequency Selective Limiters with sub-dB Loss and $μ$Watts Power Thresholds

no code implementations21 Dec 2020 Hussein M. E. Hussein, Mahmoud A. A. Ibrahim, Matteo Rinaldi, Marvin Onabajo, Cristian Cassella

This article describes the design methodology to achieve reflective diode-based parametric frequency selective limiters (pFSLs) with low power thresholds ($P_{th}$) and sub-dB insertion-loss values ($IL^{s. s}$) for driving power levels ($P_{in}$) lower than $P_{th}$.

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