First step towards new generation nanomechanical resonators

In recent years, major advances in nanotechnology have inspired some of the most innovative breakthroughs in various fields, including the development of nanomechanical resonators, such as high-precision sensing and quantum grating applications.

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Today, a team of researchers from TU Delft has designed one of the most advanced and precise nanomechanical resonators, drawing inspiration from both the natural and virtual world. Inspired by cobwebs and guided by machine learning, the team set out to create a web-like device that would exhibit vibration modes isolated from ambient thermal environments.

One of the most desirable characteristics for a mechanical resonator is the sound insulation of thermal environments, that is, under ambient temperature conditions where thermomechanical noise can dominate.

Dr Richard Norte, TU Delft

Inspired by nature

Spider webs are made up of unique and intricate geometries, making them one of nature’s best known and most intriguing structures. Although they are found almost anywhere and everywhere, researchers from various fields, such as materials science, physics and biology, are always discovering new features regarding the mechanics of cobwebs.

One of the things that are known about cobwebs is that they are exceptional and robust isolated vibration sensors. Spiders are able to detect their entangled prey through web design. They detect the vibrations emanating from within the web, not the environmental disturbances that surround it.

To facilitate their work and develop the right kind of nanomechanical resonator, the team used machine learning to guide process optimization using the Bayesian optimization algorithm.

In the context of the design of the spider web nanomechanical resonator, Bayesian optimization should not only explore the design space to find new vibrational modes that induce soft clamping with a compact design, but also use them to achieve high quality factors in the low frequency regime for a given resonator size.

Dr Richard Norte, TU Delft

Bayesian evolution

While cobwebs are the product of millions of years of extremely sophisticated evolution, the Bayesian Optimization Model has sped up the process and allowed researchers to quickly perform their calculations to streamline the design process of their ultra-sensitive device.

The resulting design model was a simplified web-like structure made up of radial and side beams with junctions between them. Computer simulations also revealed that the nanomechanical resonator could operate at room temperature under ambient conditions with high vibrational energy.

The results also showed that the simple design performed well with extremely low levels of environmental disturbance entering the resonator. This led the team to manufacture an ultra-thin device that demonstrated exceptional performance capabilities.

The team were also amazed that there was minimal energy loss outside the device, as the vibrations remained mostly confined to the nanomechanical spider web resonator.

The functionality of the TU Delft team’s nanomechanical resonator could have important implications for the field of quantum computing, especially since quantum devices must generally be stored at sub-zero temperatures (as close as possible absolute zero) because they are very sensitive to ambient temperature. conditions at room temperature.

However, the expense and techniques required to store and operate quantum devices mean their costs can prove inconvenient. Therefore, developing a strategy that could usher in the next generation of nanomechanical resonators could help shape the future of quantum computing.

The researchers also state that the design strategy could be applied to a wide range of geometries and design problems involving simulations or low throughput experiments. They predict that future developments in machine learning and optimization, as well as new manufacturing techniques, could usher in an unprecedented advance in nanotechnology over the next decade.

References and further reading

Shin, D., and Cupertino, A., et al., (2021) Nanomechanical Spider Web Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning. Advanced materials, [online] p.2106248. Available at:

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