Deep learning for the design of nano-photonic structures

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 had a profound impact on the field of optics by enabling the manipulation of light-matter interaction with subwave-length structures [1, 2, 3]. 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 [4, 5, 6]. 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 [7, 8]. Here, we harness the power of Deep Learning and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach 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 Plasmons mediated cancer thermotherapy.