The symposium is organized by Z. Cordt, C. Conti, H. Cao and S. Mujumdar, and will be held in Boston from 25 to 30 November.
Optical parametric oscillators are widely used as pulsed and continuous-wave tunable sources for innumerable applications, such as quantum technologies, imaging, and biophysics. A key drawback is material dispersion, which imposes a phase-matching condition that generally entails a complex design and setup, thus hindering tunability and miniaturization. Here we show that the burden of phase-matching is surprisingly absent in parametric micro-resonators utilizing mono-layer transition-metal dichalcogenides as quadratic nonlinear materials. By the exact solution of nonlinear Maxwell equations and first-principle calculations of the semiconductor nonlinear response, we devise a
novel kind of phase-matching-free miniaturized parametric oscillator operating at conventional pump intensities. We find that different two-dimensional semiconductors yield degenerate and non-degenerate emission at various spectral regions due to doubly resonant mode excitation, which can be tuned by varying the incidence angle of the external pump laser. In addition, we show that high-frequency electrical modulation can be achieved by doping via electrical gating, which can be used to efficiently shift the threshold for parametric oscillation. Our results pave the way for the realization of novel ultra-fast tunable micron-sized sources of entangled photons—a key device underpinning any quantum protocol. Highly miniaturized optical parametric oscillators may also be employed in lab-on-chip technologies for biophysics, detection of environmental pollution and security.
A. Ciattoni, A. Marini, C. Rizza and C. Conti, Light: Science & Applications 7 (2018) 5
” Symposium EP07: Tailored Disorder─Novel Materials for Advanced Optics and Photonics” at the MRS Fall Meeting 2018 in Boston, USA.
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We develop a novel theoretical framework describing polariton-enhanced spin-orbit interaction of light on the surface of two-dimensional media. Starting from the integral formulation of electromagnetic scattering, we exploit the reduced dimensionality of the system to introduce a quantum-like formalism particularly suitable to fully take advantage of rotational invariance. Our description is closely related to that of a fictitious spin one quantum particle living in the atomically thin medium, whose orbital, spin and total angular momenta play a key role in the scattering process. Conservation of total angular momentum upon scattering enables to physically unveil the interaction between radiation and the two-dimensional material along with the detailed exchange processes among orbital and spin components. In addition, we specialize our model to doped extended graphene, finding such spin-orbit interaction to be dramatically enhanced by the excitation of surface plasmon polaritons propagating radially along the graphene sheet. We provide several examples of the enormous possibilities offered by plasmon-enhanced spin-orbit interaction of light including vortex generation, mixing, and engineering of tunable deep subwavelength arrays of optical traps in the near field. Our results hold great potential for the development of nano-scaled quantum active elements and logic gates for the manipulation of hyper-entangled photon states as well as for the design of artificial media imprinted by engineered photonic lattices tweezing cold atoms into the desired patterns.
A. Ciattoni, C. Rizza, H. W. H. Lee, C. Conti, A. Marini in ArXiv:1804.10533
Topological concepts open many new horizons for photonic devices, from integrated optics to lasers. The complexity of large scale topological devices asks for an effective solution of the inverse problem: how best to engineer the topology for a specific application? We introduce a novel machine learning approach to the topological inverse problem. We train a neural network system with the band structure of the Aubry-Andre-Harper model and then adopt the network for solving the inverse problem. Our application is able to identify the parameters of a complex topological insulator in order to obtain protected edge states at target frequencies. One challenging aspect is handling the multivalued branches of the direct problem and discarding unphysical solutions. We overcome this problem by adopting a self-consistent method to only select physically relevant solutions. We demonstrate our technique in a realistic topological laser design and by resorting to the widely available open-source TensorFlow library. Our results are general and scalable to thousands of topological components. This new inverse design technique based on machine learning potentially extends the applications of topological photonics, for example, to frequency combs, quantum sources, neuromorphic computing and metrology.