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Machine Learning Helps Improving Photonic Applications


A computer simulation shows how the electromagnetic field is distributed in the silicon layer with hole pattern after excitation with a laser.

Researchers in Germany used computer models and machine learning to demonstrate how the design of photonic nanostructures can be selectively optimized.

Credit: Carlos Barth/HZB

Researchers at Helmholtz-Zentrum Berlin (NZB) in Germany used computer models and machine learning to demonstrate how the design of photonic nanostructures can be selectively optimized.

The researchers focused on nanostructures comprised of a silicon layer with a regular hole pattern coated with quantum dots of lead sulphide.

Laser excitation causes quantum dots close to local field amplifications to discharge more light than on an unordered surface, exposing the laser light's interaction with the nanostructure.

NZB's Carlo Barth recorded what occurs when individual parameters of the nanostructure change by calculating the three-dimensional electric field distribution for each parameter set via special software, after which machine learning algorithms analyzed the data.

Barth and NZB's Christiane Becker identified three fundamental patterns in which the fields are amplified in various specific areas of the nanoholes. This enables photonic crystal membranes based on excitation amplification to be optimized for virtually any application.

From Helmholtz-Zentrum Berlin
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