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DOE OSTI · 1851043

Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics

Hong, T. M.·Carlson, B. T.·Eubanks, B. R.·Racz, S. T.·Roche, S. T.·Stelzer, J.·Stumpp, D. C.
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Hong, T. M., Carlson, B. T., Eubanks, B. R., Racz, S. T., Roche, S. T., Stelzer, J., Stumpp, D. C.. 2021-08-01. Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics. https://doi.org/10.1088/1748-0221%2F16%2F08%2Fp08016

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Abstract The demand for high-performance electromechanical resonators is ever-growing across diverse applications, ranging from sensing and time-keeping to advanced communication devices. Among the electromechanical materials being explored, thin-film lithium niobate stands out due to its strong piezoelectric properties and low acoustic loss. However, in nearly all existing lithium niobate electromechanical devices, the configuration is such that the electrodes are in direct contact with the mechanical resonator. This configuration introduces an undesirable mass-loading effect, producing spurious modes and additional damping. Here, we present an electromechanical platform that mitigates this challenge by leveraging a flip-chip bonding technique to separate the electrodes from the mechanical resonator. By offloading the electrodes from the resonator, our approach yields a substantial increase in the quality factor of these resonators, paving the way for enhanced performance and reliability for their device applications.

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