Researchers Utilize Machine Learning to Optimize Nanophotonic Structures

Machine Learning is a thriving part of Artificial Intelligence (AI) that has only been becoming bigger and better where its technological aspect is concerned. It is already a widely used application to help machines and computers learn from experience and their errors, so to speak, only to automatically enhance their productivity.

To this effect, a team of researchers from Helmholtz-Zentrum Berlin (HZB), Germany has deduced that a group of nanostructures can significantly be made better and enhanced in terms of their productivity using machine learning and computer simulations.

HZB is a popular research institute in Berlin known for studying the dynamics and structure of materials in addition to investigating the solar cell technology.

This research, the results of which are published in Communications Physics (2018) goes on to show how photonic applications have a real chance of being improved using the application of machine learning.

Nature of the nanostructures used for the study

This research boasts of photonic nanostructures that were examined in the paper. They are made up of a silicon layer that is base for quantum dots or hole-like structures that are made up of lead sulfide.

As opposed to what would happen in an unordered surface, when these quantum dots are subjected to the laser, they emit an extraordinary amount of light. Therefore, the interaction between the nanostructures and the laser light can now be ascertained.

Results of machine learning and its usage in the research paper

The Zuse Institute in Berlin has designed software that can actually record the events after the structure of the material modifies. The 3D electrical field distribution was then also calculated for every parameter of the change in the nanostructure using this same software.

A lot of data was collected by Dr. Carlo Barth from the team who then used other programs enabled by machine learning to analyze it all. In his words, “The computer has searched through the approximately 45,000 data records and grouped them into about ten different patterns.”

Ultimately, the results of this research were successful as the team determined the underlying patterns and dynamics between the different areas of the nanoholes. It is now clear, thanks to this study that photonic structures with machine learning and its applications can go a long way to be used in many areas.

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Their applications are wide and endless. They can obviously be utilized in solar cells but more than that, they are a boon for biomolecules and optical sensors that serve as cancer markers.  

As clearly stated in the research paper, “Machine learning is a rapidly developing discipline which uses statistical approaches to learn from data without explicitly rule-based programming. Driven by today’s massive increase in data amounts, the related techniques are extended and improving at a rapid pace.”

This is precisely how machine learning can contribute to the recognition of patterns, genetics and anomaly detection. What’s left to be seen is how this knowledge can be exploited in diverse applications.