Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning

• Published in: Advanced materials (Weinheim) 2022-01, Vol.34 (3), p.e2106248-n/a
• Main Authors: Shin, Dongil, Cupertino, Andrea, de Jong, Matthijs H. J., Steeneken, Peter G., Bessa, Miguel A., Norte, Richard A.
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes Theorem; bioinspiration; data‐driven optimization; high quality factor; Human motion; Humans; Machine Learning; Materials science; Micro-Electrical-Mechanical Systems; Nanotechnology - methods; Optimization; Resonators; room‐temperature nanoresonators; Silicon nitride; Thermal environments; Thermal noise; torsional soft clamping; Vibration mode
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202106248

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. Human creativity, analytical models, and trial‐and‐error experimentation have led to groundbreaking achievements in nanotechnology. In this work, machine learning is shown to offer a new path to discovery, complementing the traditional process. A spiderweb‐inspired sensor is designed and fabricated by this strategy achieving world‐class performance. The algorithm finds a novel vibration mechanism, enabling devices isolated from room‐temperature environments.