Spiderweb Nanomechanical Resonators via Bayesian Optimization
| Autor: | Dongil Shin, Peter G. Steeneken, Matthijs H. J. de Jong, Andrea Cupertino, Richard A. Norte, Miguel A. Bessa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
data-driven optimization Computer Science - Machine Learning Materials science FOS: Physical sciences Applied Physics (physics.app-ph) torsional soft clamping Machine learning computer.software_genre Data-driven Machine Learning (cs.LG) Machine Learning Resonator Normal mode Mesoscale and Nanoscale Physics (cond-mat.mes-hall) high quality factor Humans Nanotechnology General Materials Science Lithography Quantum network Condensed Matter - Mesoscale and Nanoscale Physics business.industry Mechanical Engineering Detector Bayesian optimization Bayes Theorem Physics - Applied Physics Micro-Electrical-Mechanical Systems Fundamental interaction room-temperature nanoresonators Mechanics of Materials bioinspiration Artificial intelligence business computer |
| Zdroj: | Advanced Materials, 34(3) |
| ISSN: | 0935-9648 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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