AIP Publishing LLC
AIP Publishing LLC
  • pubs.aip.org
  • AIP
  • AIP China
  • University Science Books
  • Resources
    • Researchers
    • Librarians
    • Publishing Partners
    • Topical Portfolios
    • Commercial Partners
  • Publications

    Find the Right Journal

    Explore the AIP Publishing collection by title, topic, impact, citations, and more.
    Browse Journals

    Latest Content

    Read about the newest discoveries and developments in the physical sciences.
    See What's New

    Publications

    • Journals
    • Books
    • Physics Today
    • AIP Conference Proceedings
    • Scilight
    • Find the Right Journal
    • Latest Content
  • About
    • About Us
    • News and Announcements
    • Careers
    • Events
    • Leadership
    • Contact
  • pubs.aip.org
  • AIP
  • AIP China
  • University Science Books

New Reservoir Computer Marks First-Ever Microelectromechanical Neural Network Application

  • October 16, 2018
  • Journal of Applied Physics
  • News
Share:

Researchers used oscillations from a microscopic beam of silicon to enable the nonlinear dynamics that allow neural networks to complete tasks ranging from processing image patterns to recognizing words.

From the Journal: Journal of Applied Physics

WASHINGTON, D.C., October 16, 2018 — As artificial intelligence has become increasingly sophisticated, it has inspired renewed efforts to develop computers whose physical architecture mimics the human brain. One approach, called reservoir computing, allows hardware devices to achieve the higher-dimension calculations required by emerging artificial intelligence. One new device highlights the potential of extremely small mechanical systems to achieve these calculations.

A group of researchers at the Université de Sherbrooke in Québec, Canada, reports the construction of the first reservoir computing device built with a microelectromechanical system (MEMS). Published in the Journal of Applied Physics, from AIP Publishing, the neural network exploits the nonlinear dynamics of a microscale silicon beam to perform its calculations. The group’s work looks to create devices that can act simultaneously as a sensor and a computer using a fraction of the energy a normal computer would use.

The article appears in a special topic section of the journal devoted to “New Physics and Materials for Neuromorphic Computation,” which highlights new developments in physical and materials science research that hold promise for developing the very large-scale, integrated “neuromorphic” systems of tomorrow that will carry computation beyond the limitations of current semiconductors today.

“These kinds of calculations are normally only done in software, and computers can be inefficient,” said Guillaume Dion, an author on the paper. “Many of the sensors today are built with MEMS, so devices like ours would be ideal technology to blur the boundary between sensors and computers.”

The device relies on the nonlinear dynamics of how the silicon beam, at widths 20 times thinner than a human hair, oscillates in space. The results from this oscillation are used to construct a virtual neural network that projects the input signal into the higher dimensional space required for neural network computing.

A single silicon beam (red), along with its drive (yellow) and readout (green and blue) electrodes, implements a MEMS capable of nontrivial computations. Credit: Guillaume Dion

In demonstrations, the system was able to switch between different common benchmark tasks for neural networks with relative ease, Dion said, including classifying spoken sounds and processing binary patterns with accuracies of 78.2 percent and 99.9 percent respectively.

“This tiny beam of silicon can do very different tasks,” said Julien Sylvestre, another author on the paper. “It’s surprisingly easy to adjust it to make it perform well at recognizing words.”

Sylvestre said he and his colleagues are looking to explore increasingly complicated computations using the silicon beam device, with the hopes of developing small and energy-efficient sensors and robot controllers.

###

For More Information:
Rhys Leahy
media@aip.org
301-209-3090
@AIPPhysicsNews

Article Title

Reservoir computing with a single delay-coupled non-linear mechanical oscillator

Authors

Guillaume Dion, Salim Mejaouri and Julien Sylvestre

Author Affiliations

Université de Sherbrooke


Journal of Applied Physics

Journal of Applied Physics is an influential international journal publishing significant new experimental and theoretical results of applied physics research.

http://jap.aip.org/

Share:
  • New Model Helps Define Optimal Temperature and Pressure to Forge Nanoscale Diamonds in an Explosion
  • Scientists Unravel the Mysteries of Polymer Strands in Fuel Cells

Keep Up With AIP Publishing

Sign up for the AIP newsletter to receive the latest news and information from AIP Publishing.
Sign Up

AIP PUBLISHING

1305 Walt Whitman Road,
Suite 110
Melville, NY 11747
(516) 576-2200

Resources

  • Researchers
  • Librarians
  • Publishing Partners
  • Commercial Partners

About

  • About Us
  • Careers 
  • Leadership

Support

  • Contact Us
  • Terms Of Use
  • Privacy Policy

© 2025 AIP Publishing LLC
  • 𝕏