Inverse design of metasurfaces using deep learning neural networks

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Deep learning artificial neural networks have seen a remarkable development in recent years and hold promise for a variety of applications in the field of nanophotonics. In this project the student will explore the use of deep learning techniques in nanophotonic systems at the frontiers of the field. Techniques of interest include the use of Physics informed networks to improve the quality of solutions, as well as recently proposed neural-adjoint optimization schemes. The student will explore how these techniques can be applied to current topics in optical metasurfaces and the study of light-matter coupling in hybrid quantum-electromagnetic systems. Theoretical and computational work will be complemented with proof of principle experiments involving the design of nanosystems that can be fabricated in our cleanroom facilities and their subsequent optical studies.