No abstract

Nanomechanical resonator devices are widely used as ultrasensitive mass detectors for fundamental studies and practical applications. The resonance frequency of the resonators shifts when a mass is loaded, which is used to estimate the mass. However, the shift signal is often blurred by the thermal noise, which interferes with accurate mass detection. Here, we demonstrate the reduction of the noise interference in mass detection in suspended graphene-based nanomechanical resonators, by using applied machine learning. Featurization is divided into image and sequential datasets, and those datasets are trained and classified using 2D and 1D convolutional neural networks (CNNs). The 2D CNN learning-based classification shows a performance with f1-score over 99% when the resonance frequency shift is more than 2.5% of the amplitude of the thermal noise range.

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In this study, the radiation effects of electron beam (e‐beam) on field‐effect transistors (FETs) using transition‐metal dichalcogenides (TMD) as a channel are carefully investigated. Electron‐hole pairs (EHPs) in SiO2 generated by e‐beam irradiation induce additional traps, which change the surface potential of the TMD channel, resulting in strong negative shifts of transfer characteristics. These negative shifts, which remind one of n‐doping effects, are highly affected not only by the condition of e‐beam irradiation, but also by the gate bias condition during irradiating. As a result of the e‐beam irradiation effect, band bending and contact resistance are affected, and the degree of formation of oxide traps and interface traps varies depending on the gate bias conditions. In the case of VG > 0 V application during e‐beam irradiation, the negative shifts in the transfer characteristics are fully recovered after ambient exposure. However, the interface traps increase significantly, resulting in variations of low‐frequency (LF) noise and time‐dependent current fluctuations.