Where deep learning meets metamaterials: Researchers devise new approach to streamlining design of nanoscale building blocks with endless applications

Now a new interdisciplinary Tel Aviv University study published in Light: Science and Applications demonstrates a way of streamlining the process of designing and characterizing basic nanophotonic, metamaterial elements. The study was led by Dr. Haim Suchowski of TAU's School of Physics and Astronomy and Prof. Lior Wolf of TAU's Blavatnik School of Computer Science and conducted by research scientist Dr. Michael Mrejen and TAU graduate students Itzik Malkiel, Achiya Nagler and Uri Arieli. "The process of designing metamaterials consists of carving nanoscale elements with a precise electromagnetic response," Dr. Mrejen says. "But because of the complexity of the physics involved, the design, fabrication and characterization processes of these elements require a huge amount of trial and error, dramatically limiting their applications." "Our new approach depends almost entirely on Deep Learning, a computer network inspired by the layered and hierarchical architecture of the human brain," Prof. Wolf explains.