Research on two-dimensional designer optical structures, or metasurfaces, has mainly focused on controlling the wavefronts of light propagating in free space. Here, we show that gradient metasurface structures consisting of phased arrays of plasmonic or dielectric nanoantennas can be used to control guided waves via strong optical scattering at subwavelength intervals. Based on this design principle, we experimentally demonstrate waveguide mode converters, polarization rotators and waveguide devices supporting asymmetric optical power transmission. We also demonstrate all-dielectric on-chip polarization rotators based on phased arrays of Mie resonators with negligible insertion losses. Our gradient metasurfaces can enable small-footprint, broadband and low-loss photonic integrated devices.

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventionally metasurface device design relies on trial-anderror methods to obtain target electromagnetic (EM) responses, which demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for metaatoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.Here we propose an implicit way to construct and train the networks to predict the amplitude and phase responses of meta-structures. For a typical meta-structure, like the one shown in Fig. 1A,

In this letter, we experimentally evaluate the effect of miniaturization and surface roughness on transmission losses within a Si/SiO2 waveguide system, and explain the results using a theoretical model. Micrometer/nanometer-sized waveguides are imperative for its potential use in dense integrated optics and optical interconnection for silicon integrated circuits. A theoretical model was employed to predict the relationship between the transmission losses of the dielectric silicon waveguide and its width. This model accurately predicts that loss increases as waveguide width decreases. Furthermore, we show that a major source of loss comes from sidewall roughness. We have constructed a complete contour map showing the interdependence of sidewall roughness and transmission loss, to assist users in their design of an optimal waveguide fabrication process that minimizes loss. Additionally, users can find an effective path to reduce the scattering loss from sidewall roughness. Using this map, we confirm that nanometer-size silicon waveguides with 0.1 dB/cm transmission loss are possible with the currently available technology.

Abstract:The emergence of silicon photonics over the past two decades has established silicon as a preferred substrate platform for photonic integration. While most silicon-based photonic components have so far been realized in the near-infrared (near-IR) telecommunication bands, the mid-infrared (mid-IR, 2-20-μm wavelength) band presents a significant growth opportunity for integrated photonics. In this review, we offer our perspective on the burgeoning field of mid-IR integrated photonics on silicon. A comprehensive survey on the state-of-the-art of key photonic devices such as waveguides, light sources, modulators, and detectors is presented. Furthermore, on-chip spectroscopic chemical sensing is quantitatively analyzed as an example of mid-IR photonic system integration based on these basic building blocks, and the constituent component choices are discussed and contrasted in the context of system performance and integration technologies.