Abstract
From ultrasensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room-temperature environments. Currently, silicon nitride nanoresonators stand as a leading microchip platform in these advances by allowing for mechanical resonators whose motion is remarkably isolated from ambient thermal noise. However, to date, human intuition has remained the driving force behind design processes. Here, inspired by nature and guided by machine learning, a spiderweb nanomechanical resonator is developed that exhibits vibration modes, which are isolated from ambient thermal environments via a novel "torsional soft-clamping" mechanism discovered by the data-driven optimization algorithm. This bioinspired resonator is then fabricated, experimentally confirming a new paradigm in mechanics with quality factors above 1 billion in room-temperature environments. In contrast to other state-of-the-art resonators, this milestone is achieved with a compact design that does not require sub-micrometer lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. These results demonstrate the ability of machine learning to work in tandem with human intuition to augment creative possibilities and uncover new strategies in computing and nanotechnology.
Highlights
Inspired by nature and guided can indicate the dissipation of mechanby machine learning, a spiderweb nanomechanical resonator is developed ical noise into a resonator from ambient that exhibits vibration modes, which are isolated from ambient thermal environments via a novel “torsional soft-clamping” mechanism discovered by the data-driven optimization algorithm
For quantum technoloto other state-of-the-art resonators, this milestone is achieved with a compact design that does not require sub-micrometer lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. These results demonstrate the ability of machine learning gies, mechanical quality factor dictates the average number of coherent oscillations a nanomechanical resonator can undergo before one phonon of thermal noise enters the to work in tandem with human intuition to augment creative possibilities and resonator and causes decoherence of its uncover new strategies in computing and nanotechnology
The spiderweb nanomechanical resonator is fabricated on high-stress Si3N4 grown by low-pressure chemical vapor deposition (LPCVD) on a silicon wafer (Figure 5A)
Summary
Inspired by nature and guided can indicate the dissipation of mechanby machine learning, a spiderweb nanomechanical resonator is developed ical noise into a resonator from ambient that exhibits vibration modes, which are isolated from ambient thermal environments via a novel “torsional soft-clamping” mechanism discovered by the data-driven optimization algorithm. For quantum technoloto other state-of-the-art resonators, this milestone is achieved with a compact design that does not require sub-micrometer lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales These results demonstrate the ability of machine learning gies, mechanical quality factor dictates the average number of coherent oscillations a nanomechanical resonator (in the quantum regime) can undergo before one phonon of thermal noise enters the to work in tandem with human intuition to augment creative possibilities and resonator and causes decoherence of its uncover new strategies in computing and nanotechnology. In room-temperature environments, on-chip mechanical the most sought after characteristics for a mechanical resonator resonators with state-of-the-art quality factors have mostly is noise isolation from thermal environments, namely at room- consisted of high-aspect-ratio suspended nanostructures
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