Deep learning in light-matter interactions

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light-matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics

High Q factor modes in semiconductor nanoantennas for active nanophotonics and lasing

On-chip light sources are critical for the realization of fully integrated photonic circuitry. So far, semiconductor miniaturized lasers have been mainly limited to sizes on the order of a few microns. Further reduction of sizes is challenging fundamentally due to the associated radiative losses. While using plasmonic metals helps to reduce radiative losses and sizes, they also introduce Ohmic losses hindering real improvements. Here, we circumvent these fundamental issues using quasi-bound states in the continuum or supercavity modes and realize one of the smallest purely semiconductor nanolasers thus far. Coupling between a nanoparticle and a waveguide is studied as an extension of the original work. Multiple-particle 1D configurations, operating in lower azimuthal order modes, are optimized numerically and studied experimentally. The obtained results open a way for the realization of smaller low-loss dielectric nanolasers that might find applications in future photonic circuitry, among others.Doctor of Philosoph