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We demonstrate nanoelectromechanical contact-mode switches and logic gates with high performance, enabled by cantilever-structured SiC nanoelectromechanical systems (NEMS). In full-cycle recording measurements (complete time-domain trace of every single switching cycle recorded), we show that in ambient air, SiC NEMS switches with nanocontacts have operated >1×10 7 cycles of 'hot-switching' without failure (devices still alive). When only recording valid 'on'/'off' states (without the complete trace, to avoid overflowing data recording and to speed up acquisition), >2×10 10 cycles have been measured. These clearly exhibit the unique properties and advantages of SiC NEMS, amongst all contact-mode, genuinely nanoscale switches. We also show robust switching events at high temperature T≈500°C.

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We report an experimental study on AC measurements of contact-mode switches based on silicon carbide (SiC) nanoelectromechanical systems (NEMS). We describe the development of circuits and measurement techniques for recording long cycles of AC switching characteristics of SiC NEMS featured by ultrasmall device movable volumes (at ~1μm 3 level) and contact areas (only ~0.01-0.1μm 2 ), and challenging contact resistances (can be from ~10kΩ to ~100MΩ). We perform time-domain AC characterization of SiC NEMS switches with operating speeds up to 1kHz and high on/off current ratios of ~10 6 . For multiple devices, we have recorded the complete time evolution of AC switching data traces of >10 6 cycles at 1kHz, without failure in ambient air. Beyond these long cycles the devices are still alive, which demands even higher-speed, accelerated AC measurements for long-lifetime recording.

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Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue.Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

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