Title: Deep Learning for Design and Retrieval of Nano-photonic Structures,
Abstract: Our visual perception of our surroundings is ultimately limited by the
diffraction limit, which stipulates that optical information smaller than
roughly half the illumination wavelength is not retrievable. Over the past
decades, many breakthroughs have led to unprecedented imaging capabilities
beyond the diffraction-limit, with applications in biology and nanotechnology.
In this context, nano-photonics has revolutionized the field of optics in
recent years by enabling the manipulation of light-matter interaction with
subwavelength structures. However, despite the many advances in this field, its
impact and penetration in our daily life has been hindered by a convoluted and
iterative process, cycling through modeling, nanofabrication and
nano-characterization. The fundamental reason is the fact that not only the
prediction of the optical response is very time consuming and requires solving
Maxwell's equations with dedicated numerical packages. But, more significantly,
the inverse problem, i.e. designing a nanostructure with an on-demand optical
response, is currently a prohibitive task even with the most advanced numerical
tools due to the high non-linearity of the problem. Here, we harness the power
of Deep Learning, a new path in modern machine learning, and show its ability
to predict the geometry of nanostructures based solely on their far-field
response. This approach also addresses in a direct way the currently
inaccessible inverse problem breaking the ground for on-demand design of
optical response with applications such as sensing, imaging and also for
plasmon's mediated cancer thermotherapy.