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

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

© 2021 The Authors. Advanced Materials published by Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 3 vom: 01. Jan., Seite e2106248
1. Verfasser: Shin, Dongil (VerfasserIn)
Weitere Verfasser: Cupertino, Andrea, de Jong, Matthijs H J, Steeneken, Peter G, Bessa, Miguel A, Norte, Richard A
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article bioinspiration data-driven optimization high quality factor room-temperature nanoresonators torsional soft clamping
Beschreibung
Zusammenfassung:© 2021 The Authors. Advanced Materials published by Wiley-VCH GmbH.
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
Beschreibung:Date Completed 31.03.2022
Date Revised 31.05.2022
published: Print-Electronic
Citation Status MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202106248