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Unsticking MEMS

Exotic materials could combat the Casimir effect, a kind of quantum-mechanical stickiness

2 min read

Researchers at Los Alamos National Laboratory, in New Mexico, think they may have the answer to a vexing problem called stiction, which causes ultrasmall components of microelectromechanical systems (MEMS) to stick together. This impediment to micromovement is caused by the Casimir effect (after the Dutch theoretical physicist Hendrik Casimir), an odd attractive force that influences only objects that are very close together. As MEMS components are shrunk to a scale of hundreds of nanometers or less, many engineers predict that the Casimir effect will become more of a problem.

”The Casimir force is the ultimate cause of friction in the nanoworld,” says Ulf Leonhardt, a theoretical physicist at the University of St. Andrews, in Scotland. ”Micro- or nanomachines could run smoother and with less or no friction at all if one can manipulate the Casimir force.”

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
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Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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