Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning
This work addresses the challenge of designing high-performance, manufacturable resonators for technologies like sensors and quantum networks, representing a new paradigm rather than an incremental improvement.
The researchers tackled the design of nanomechanical resonators by using machine learning to discover a novel 'torsional soft-clamping' mechanism, resulting in a spiderweb-inspired resonator that achieves quality factors above 1 billion at room temperature without requiring complex fabrication.
From ultra-sensitive 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 bio-inspired 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 which does not require sub-micron lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. Here we 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.