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Deep Learning: An Advanced Method to Design Metamaterials
Metamaterials are not naturally occurring materials; they are made from the combination of composite materials like metals and plastics. Their structure gives them smart properties by which they can manipulate electromagnetic wavelengths. Metamaterials have a diverse application which includes optical fiber, medical devices, aerospace application, sensor detection, and many other applications.
Nanophotonics is a part of material science which studies the behavior of light on the nanometer scale and also the interaction of nanometer-scale objects with light. Breakthroughs in the study of nanophotonics have paved the way for the invention of metamaterials. The manufacturing process of metamaterials is very complex and has a large error margin; thus it is extremely difficult to use metamaterials in the aforementioned applications.
A study on nanotechnology, published at Tel Aviv University has demonstrated a way to streamline the process of designing metamaterials using nanophotonics. The method of designing metamaterials involves a precise electromagnetic response to carve nanoscale elements. The new learning is based entirely on Deep Learning. Deep Learning is a computer network which is inspired by the layered and hierarchical architecture of the human brain. Deep Learning is an advanced form of machine learning which helps to find the right approach to design a metamaterial with artificial intelligence. In order to teach the network of the complex relationship between shapes of nanoelements and their electromagnetic responses, the researchers fed the Deep Learning network with thousands of artificial experiments.
The study of the result can be used in a wide variety of results like spectroscopy and targeted therapy. Targeted therapy is a fast and efficient method to design a nanoparticle capable of targeting malicious proteins. The research not only helps to develop a metamaterial but it also helps to determine the shape of that material. Determining the shape of a metamaterial is an expensive and time-consuming method. Deep Learning helps to determine the shape of the metamaterial in split seconds with a computer-based solution of simple transmission measurement.