Machine learning helps improving photonic applications

phys.org | 10/1/2018 | Staff
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Photonic nanostructures can be used for many applications besides solar cells—for example, optical sensors for cancer markers or other biomolecules. A team at HZB using computer simulations and machine learning has now shown that the design of such nanostructures can be selectively optimised. The results are published in Communications Physics.

Nanostructures can increase the sensitivity of optical sensors enormously—provided that the geometry meets certain conditions and matches the wavelength of the incident light. This is because the electromagnetic field of light can be greatly amplified or reduced by the local nanostructure. The HZB Young Investigator Group "Nano-SIPPE" headed by Prof. Christiane Becker is working to develop these kinds of nanostructures. Computer simulations are an important tool for this. Dr. Carlo Barth from the Nano-SIPPE team has now identified the most important patterns of field distribution in a nanostructure using machine learning, and explained the experimental findings.

Nanostructures - Paper - Consist - Silicon - Layer

The photonic nanostructures examined in the paper consist of a silicon layer with a regular hole pattern coated with quantum dots made of lead sulphide. Excited with a laser, the quantum dots close to local field amplifications emit much more light than on an unordered surface. This empirically demonstrates how the...
(Excerpt) Read more at: phys.org
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