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.

In this study, high-performance few-layered ReS₂ field-effect transistors (FETs), fabricated with hexagonal boron nitride (h-BN) as top/bottom dual gate dielectrics, are presented. The performance of h-BN dual gated ReS₂ FET having a trade-off of performance parameters is optimized using a compact model from analytical choice maps, which consists of three regions with different electrical characteristics. The bottom h-BN dielectric has almost no defects and provides a physical distance between the traps in the SiO₂ and the carriers in the ReS₂ channel. Using a compact analyzing model and structural advantages, an excellent and optimized performance is introduced consisting of h-BN dual-gated ReS₂ with a high mobility of 46.1 cm² V⁻¹ s⁻¹, a high current on/off ratio of ≈10⁶, a subthreshold swing of 2.7 V dec⁻¹, and a low effective interface trap density (N_t,eff) of 7.85 × 10¹⁰ cm⁻² eV⁻¹ at a small operating voltage (<3 V). These phenomena are demonstrated through not only a fundamental current–voltage analysis, but also technology computer aided design simulations, time-dependent current, and low-frequency noise analysis. In addition, a simple method is introduced to extract the interlayer resistance of ReS₂ channel through Y-function method as a function of constant top gate bias.