Print Email Facebook Twitter Spiderweb Nanomechanical Resonators via Bayesian Optimization Title Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning Author Shin, D. (TU Delft Team Georgy Filonenko) Cupertino, A. (TU Delft Dynamics of Micro and Nano Systems) de Jong, M.H.J. (TU Delft QN/Groeblacher Lab; TU Delft Dynamics of Micro and Nano Systems) Steeneken, P.G. (TU Delft Dynamics of Micro and Nano Systems; TU Delft QN/Steeneken Lab) Bessa, M.A. (TU Delft Team Georgy Filonenko) Norte, R.A. (TU Delft QN/Groeblacher Lab; TU Delft Dynamics of Micro and Nano Systems) Date 2021 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. Subject bioinspirationdata-driven optimizationhigh quality factorroom-temperature nanoresonatorstorsional soft clamping To reference this document use: http://resolver.tudelft.nl/uuid:964391de-c6cf-4c86-8726-7f0e4a03dc85 DOI https://doi.org/10.1002/adma.202106248 ISSN 0935-9648 Source Advanced Materials, 34 (3) Part of collection Institutional Repository Document type journal article Rights © 2021 D. Shin, A. Cupertino, M.H.J. de Jong, P.G. Steeneken, M.A. Bessa, R.A. Norte Files PDF Advanced_Materials_2021_S ... re_and.pdf 3.57 MB Close viewer /islandora/object/uuid:964391de-c6cf-4c86-8726-7f0e4a03dc85/datastream/OBJ/view