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Parametric amplification of ultrasmall signals from electromechanical transducers directly in the mechanical domain, prior to electrical readout, is an intriguing challenge and is important for both scientific measurements and technologies utilizing micro/nanoelectromechanical systems (MEMS/NEMS). Here, we report on parametric amplification of aluminum nitride (AlN) multimode NEMS resonators (with broad intrinsic dynamic ranges up to 90 dB) for enabling detection of their thermomechanical resonances in both optical and electrical readout schemes simultaneously. The experiments demonstrate that, upon parametric pumping, the electrically transduced thermomechanical motions experience significant amplification, surpassing the extrinsic electronic noise level, while still below the parametric pumping threshold. We achieve noise matching that enables room temperature force sensitivity of 0.46 fN/Hz1/2. We observe high parametric gain up to 650, accompanied by a strong boost (over 3.5×) in the effective quality factor (Qeff, from 9000 to 32 000). These findings underscore the utilities of parametric amplification in noise matching and improving force sensitivity for NEMS transducers and their emerging applications. 

We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii-Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin-orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks.