Machine Learning to Expand the Application of Photonic Nanostructures

Photonic Nanostructures

‘Nano-SIPPE’, the HZB (Helmholtz-Zentrum Berlin) Young Investigator Group, led by Professor Christiane Becker is working on the development of photonic nanostructures that can act as optical sensors for biomolecules and cancer markers. When photonic nanostructures meet particular conditions and match the wavelength of incident light, they can largely increase the sensitivity of optical sensors. Researchers are using machine learning and computer simulations to optimize the design of such nanostructures.

In a recently published study in Communications Physics, Dr. Carlo Barth, a member from Nano-SIPPE team, explained the experimental findings used to identify the vital patterns of field distribution in a photonic nanostructure by using machine learning.

The nanostructure was studied on a paper comprising of a silicon layer and typical hole pattern made of lead sulphide which is referred to as quantum dots. When the paper was excited with a laser, emission of light was more through quantum dots close to local field amplifications than through uneven surface. This experiment has enabled to demonstrate the interaction of laser light and the photonic nanostructure.

With the help of a software developed in Zuse Institute Berlin, Dr. Barth calculated the electric field distribution of each parameter in three-dimension to record what happens when individual parameters of the nanostructure change. The data of each parameter was further analyzed by computer programs developed with machine learning. Barth added that the computer program ran through about 45,000 data and nearly ten different patterns were grouped together. Among these, three basic patterns were identified in which the electric fields are amplified at specific areas of the photonic nanostructures.

Based on excitation amplification, photonic crystal membranes can be virtually optimized for any application. This is mainly due accumulation of certain biomolecules along the hole edges while other are accumulated towards the plateaus, depending on the application. Maximum electric field amplification can be generated at the attachment sites of the particular molecules with correct geometry and right excitation by light. As a result, the sensitivity of optical sensors would increase to individual molecular level, which can be used to optimize the sensors for cancer markers and other biomolecules.


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Ganesh Rajput

Ganesh Rajput

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