Metasurface-enhanced light detection and ranging technology.

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Among 3D computer vision techniques, LiDAR is currently considered at the industrial level for robotic vision. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low resolution, notably limited by the performance of mechanical or solid-state deflection systems. Metasurfaces are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that leverages from ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV (150°) and high framerate (kHz) which can provide simultaneous peripheral and central imaging zones. The use of our disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve perception and decision-making process of ADAS and robotic systems.

Blue to yellow emission from (Ga,In)/GaN quantum wells grown on pixelated silicon substrate.

It is shown that substrate pixelisation before epitaxial growth can significantly impact the emission color of semiconductor heterostructures. The wavelength emission from InxGa1-xN/GaN quantum wells can be shifted from blue to yellow simply by reducing the mesa size from 90 × 90 µm2 to 10 × 10 µm2 of the patterned silicon used as the substrate. This color shift is mainly attributed to an increase of the quantum well thickness when the mesa size decreases. The color is also affected, in a lesser extent, by the trench width between the mesas. Cathodoluminescence hyperspectral imaging is used to map the wavelength emission of the InxGa1-xN/GaN quantum wells. Whatever the mesa size is, the wavelength emission is red-shifted at the mesa edges due to a larger quantum well thickness and In composition.

Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering.

Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ) posts, on the self-organization of polystyrene beads (PS) dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.